We introduce the BIR-Adapter, a parameter-efficient diffusion adapter for blind image restoration. Diffusion-based restoration methods have demonstrated promising performance in addressing this fundamental problem in computer vision, typically relying on auxiliary feature extractors or extensive fine-tuning of pre-trained models. Building on the observation that large-scale pretrained diffusion models can retain informative representations under image degradations, BIR-Adapter introduces a parameter-efficient, plug-and-play attention mechanism that substantially reduces the number of trained parameters. To further improve reliability, we adapt a sampling guidance mechanism that mitigates hallucinations during restoration. Experiments on synthetic and real-world degradations demonstrate that BIR-Adapter achieves competitive, and in several settings superior, performance compared to state-of-the-art methods while requiring up to 36 × fewer trained parameters. Moreover, the adapter-based design enables integration into existing models. We validate this generality by extending a super-resolution–only diffusion model to handle additional unknown degradations, highlighting the adaptability of our approach for broader image restoration tasks.

Efficient radio resource management (RRM) in 5G networks is increasingly challenged by the diverse quality of service (QoS) requirements of emerging applications and the growing uplink (UL) traffic from resource-constrained devices. Existing scheduling approaches often lack user and service-specific context, limiting their ability to guarantee timely and energy-efficient data transmission, particularly critical for the internet of things (IoT) and mission-critical services. In this work, we introduceQUEST, a QoS-aware UL scheduling framework that exploits the 5G QoS model alongside network and device context to efficiently allocate radio resources. Designed and evaluated in an indoor factory environment,QUESTsupports users with various heterogeneous 5QI services under dynamic multi-user conditions. Evaluation results, validated through both real-world measurements and 3GPP-compliant simulations, show thatQUESTconsistently outperforms traditional channel- and QoS-aware schedulers. It improves QoS compliance, reduces packet drops and serving time, and enhances energy efficiency. For users with stringent QoS demands, measurements show a 13% increase in successfully transmitted packets and a 6.2% reduction in delay for 50% of transmissions, compared to the best-performing baseline. Benchmarking against an optimal scheduler shows thatQUESTachieves the closest performance among baselines, while maintaining low complexity, making it a practical and scalable solution for 5G and beyond UL RRM.

The deployment of 5G networks, which are characterized by high-frequency bands, dense cell deployments, and users with diverse application demands, presents significant challenges for efficient mobility management. Frequent handovers lead to transmission interruptions and reduced network capacity, affecting both the user experience and overall network performance. The current standard handover algorithm selects target base stations solely based on signal strength, without considering the availability of resources, which can result in overloaded cells. To address these challenges, in this paper, we formulate a multi-objective optimization problem that reliably captures the network support for dual connectivity. The objective is to maximize network throughput (of interest to the operator), and to minimize service interruptions (of interest to users). Given the computational complexity of the optimization problem, we develop a Deep Reinforcement Learning (DRL)-based algorithm to perform user-to-BS assignment and wireless resource allocation. We evaluate our approach through realistic simulations in two scenarios: an indoor factory floor that uses Frequency Range (FR) 2 and a two-tier outdoor network that operates in both FR1 and FR2. The proposed algorithm demonstrates significant improvements over state-of-the-art methods, achieving better sum throughput and zero handover rates while operating within 10% of the optimum. Additionally, our algorithm ensures a 100% user rate satisfaction. Instead of performing handovers, it utilizes dual connectivity initiations and keeps their rate 20−30% lower than the handover rate.

The demands on networks are increasing at a fast pace. In particular, real-time applications have very strict network requirements. However, setting up a network that hosts real-time applications is a cost-intensive endeavor, especially for experimental systems such as testbeds. Systems that provide guaranteed real-time networking capabilities usually work with expensive, high-rate software-defined switches. In contrast, real-time networking systems based on low-cost hardware face the limitation of lower link speeds. This paper fills this gap and presents Low-Cost Deterministic Networking (LCDN), a system designed to work with inexpensive, common off-the-shelf switches and devices. LCDN works at Gigabit speed and enables powerful testbeds to host real-time applications with strict delay guarantees. LCDN’s performance is similar to industrial- and production-grade solutions. This paper also provides an evaluation of the determinism of a low-cost switch and a Raspberry Pi used as an end-device to demonstrate the applicability of LCDN for inexpensive, low-power systems.

Within the research for the sixth generation of mobile networks, so-called in-X subnetworks (SNs) were proposed to support services with extreme performance requirements in geographically confined areas. To facilitate efficient spectrum usage and reliable connectivity within in-X SNs, adequate dynamic frequency planning methods for managing interference must be developed. This work provides background on in-X SNs and reviews related work on frequency planning and interference management. The problem of minimizing SN reconfigurations is identified as a key objective, while it is not addressed in the literature to date. Thus, the trade-off between minimizing subband usage and reducing SN reconfigurations is analyzed using both an already existing basic and a newly proposed advanced interference model. Preliminary results highlight how interference modeling and knowledge of future interference scenarios affect optimization outcomes, and future research directions toward dynamic frequency planning for in-X SNs in real 6G systems are discussed.

Goal-oriented (GO) semantic communication facilitates scaling modern networks with growing real-time traffic generated within networked Cyber Physical Systems (CPSs). Network resource management in GO communication prioritizes data effectiveness for the application goal. This implies reducing network resources allocated to low-priority information. Existing GO approaches often lack generalization, because they tailor particular network schemes to particular applications. In the current work, we propose a practical GO scheme operating in the transport layer (TL) middleware, i.e., not requiring specific hardware or network structure. Using Reinforcement Learning (RL), the proposed GO RL TL captures the potential contribution of the currently sampled observed state to the real-time CPS process evolution at the remote monitor. Together with the network congestion level, the state’s effect on the application goal determines whether the distributed sensors deploying GO RL TL agents accept corresponding packets into the network or discard them. The offline environment for training uses the real data traces of traffic patterns and application dynamics. The model generalizes to arbitrary network and application setups present in traces by learning the corresponding inter-dependencies from data. The extensive hardware tests witness the adaptability of the proposed GO RL TL, as well as its superiority in application performance compared to competitors. GO RL TL improves remote estimation mean-squared error by $20 \%$ to $100 \%$ in static network conditions, and by $\sim 30 \%$ in the dynamic setup.

5G makes it possible to reach to very low latency and very high reliability for different use cases. However, it is not clear how mobility procedures would impact the latency and the reliability of these use cases. Therefore, in this paper, we investigate how a packet flows through 5G RAN Protocol Stack and precisely how handovers affect that flow. Later, we present simulation studies conducted under three different scenarios, Future Railway Mobile Communications System Scenario (a high speed train use case that requires very high reliability), a highway scenario for C-V2X communications and an FR2 Urban Scenario for XR use cases. Our findings indicate that optimizing for handover interruption time can improve reliability up to 0.0094% in the FRMCS scenario, up to 0.045% in the Highway scenario, and up to 0.0071% in the FR2 scenario, underlining the importance of mobility procedures for use cases that require very high reliability, up to 99.9999%.

Evolving communication technologies and standards enable novel IoT use cases, such as real-time edge or fog assistance for distributed sensing and control in smart environments. Synchronizing the system state at centralized management entities with distributed components is a network resource-hungry task. Semantic communication is envisioned to cope with rapid network traffic increase and associated performance degradation by optimizing resource utilization for the application goal. Identification (ID) codes are a novel semantic technology that reduces network traffic for remote synchronization by representing a potentially multidimensional system state with a short tag. This work exploits ID codes in the intelligent transportation context. The vehicles in a truck platoon use ID codes to synchronize their distributed control decisions with the centralized edge server. We present one of the first frameworks demonstrating the advantages of using ID codes in practical scenarios. We show that the truck platoon management utilizing ID codes is$\sim 40 {\%}$more efficient than a fully distributed control scenario and never lags behind a fully centralized control. Moreover, ID codes reduce network traffic by$\sim 4$times compared to the latter.

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

Proposals for molecular communication networks as part of a future internet of bio-nano-things have become more intricate and the question of practical implementation is gaining more importance. One option is to apply detailed chemical modeling to capture more realistic effects of computing processes in biological systems. In this paper, we present ChemSICal, a detailed model for implementing the successive interference cancellation (SIC) algorithm for molecular multiple access in diffusion-based molecular communication networks as a chemical reaction network (CRN). We describe the structure of the model as a number of smaller reaction blocks, their speed controlled by reaction rate constants (RRCs). Deterministic and stochastic methods are utilized to first iteratively improve the choice of RRCs and subsequently investigate the performance of the model in terms of an error probability. We analyze the model's sensitivity to parameter changes and find that the analytically optimal values for the non-chemical model do not necessarily translate to the chemical domain. This necessitates careful optimization, especially of the RRCs, which are crucial for the successful operation of the ChemSICal system.

The rapid development of 5G brought advanced capabilities to support low-latency and bandwidth-hungry applications, including edge computing capabilities. However, service orchestration on the network edge is characterized by the scarcity of available resources in the edge infrastructure, emphasizing the need for optimal resource allocation. In this work, we explore a multi-objective optimization approach to resource allocation in 5G edge networks that focuses on the deployment of edge applications and User Plane Functions (UPFs) to maximize operator’s profit with minimum energy consumption. Due to the computationally expensive nature of the optimization formulation, we develop a solution that relies on the use of genetic algorithms, specifically a non-dominated sorting genetic algorithm (NSGA-II)-based multi-objective approach. We compare the performance obtained with our approach against the optimal solution, where the latter can be obtained only for small instances, and another benchmark heuristic. We do this for three different topologies under varying demand levels. Results show that our approach yields solid performance, outperforming the benchmark by at least 25%.

Massive Machine-Type Communications are one of the service types supported in 5G. They are characterized by massiveness and sporadic traffic, as well as to be energy efficient. While the scattered nature of the traffic occurrence does not pose a serious burden on efficient network planning, the requirement to serve a massive number of devices and to be energy-efficient certainly does. Moreover, besides the successful transmission/reception, these data need to be processed too. With limited resources on both the Radio Access Network part (responsible for communication) and edge cloud part (responsible for processing), as well as with the competition among a large number of users with this traffic in the cellular network, we need to address the problem of admission control, so that the network can successfully serve all the users that were admitted. To that end, in this paper we model the behavior of the system using a queueing network and perform the analysis that leads to admission policies for mMTC traffic with computation requirements. We do this both for homogeneous and heterogeneous users. Using data from a 5G trace, we validate our analytical results and provide further insights. Results show that the number of admitted users almost completely depends on the traffic pattern and that the entity with the lower capacity determines the number of admitted users.

This paper introduces CVSC, a real-time-enabled and temporally consistent semantic video communication approach. We minimize the denoising steps of the diffusion model by using the most recent frame and motion information, enabling real-time-capable semantic video synthesis. To ensure temporal consistency, we employ windowed temporal cross-attention. While our objective evaluation highlights the limitations of existing metrics for generative models in semantic video communication, subjective evaluations demonstrate the superiority of our approach in terms of human preference at extremely low bit rates. (< 0.006 bpp).

This work addresses the fidelity problem in low-bitrate real-time semantic video communication, which is crucial for enhancing the user experience. We present I2V-SC, extending baseline diffusion-based Image2Video (I2V) synthesis with ControlNet and distillation tailored to semantic video coding, enabling real-time performance with high fidelity. Evaluations using perceptual, pixel-level, motion, and semantic metrics demonstrate that I2V-SC outperforms baseline I2V and a baseline semantic video communication approach, namely CVSC, in ultra-low-bitrate ($<0.006$bpp) and real-time-enabled settings. Subjective evaluations confirm that I2V-SC further improves the user QoE in terms of overall preferability.

This work presents a two-stage digital twin methodology for developing and validating DFL algorithms on resource-constrained microcontrollers. The first stage, our simulation-based twin, enables rapid prototyping and algorithm exploration without hardware constraints, while the second stage, based on leveraging several hardware emulation instances in a containerized environment, provides hardware-aware validation under realistic conditions including network delays, resource limitations, and communication protocols. This approach bridges the critical gap between research and deployment, enabling performance analysis at a pace impractical with physical hardware alone. We demonstrate how this digital twin pipeline is essential for robust Machine Learning Operations (MLOps) in IoT environments, allowing for scalable, cost-effective testing of decentralized tiny ML. Our results across simulation, emulation, and a cluster of real ESP32-S3 microcontrollers show that our twins faithfully reproduce physical device behavior, making it a valuable framework for advancing tiny, decentralized AI.

In the realm of cluster operation, continuously validating and optimizing the configuration requires access to accurate cluster behavioral models. Network Digital Twins (NDTs) have emerged as a paradigm to provide such accurate, live representations of network systems. To capture the live state, NDTs need to anticipate the cluster behavior in a faster than real-time manner. With increasingly complex clusters, classical NDTs relying on detailed handcrafted simulators become too slow to fulfill this task. Leveraging measurements from the actual system demonstrates the potential to create more highlevel, lightweight NDTs that are still fairly accurate. Nonetheless, the degree of abstraction required to create fast and accurate data-driven NDTs is not well understood. To address this, our work investigates the impact of different abstraction levels on modeling accuracy. We develop and compare three Network Digital Twins of a Kubernetes Cluster - a Twin based on a Handcrafted Simulator, a Decentralized Data-driven Twin, abstracting individual system components, and a Centralized Data-driven Twin, abstracting the system as a whole. Our results show that Data-driven Twins improve the performance prediction by 18-53% over the handcrafted one, with the Centralized Twin surpassing the Decentralized Twin in accuracy by 35% and speed by two orders of magnitude.

The proliferation of 5G technology necessitates advanced network management strategies to ensure optimal performance and reliability. Digital Twin (DT)s have emerged as a promising paradigm for modeling and simulating complex systems like the 5G Radio Access Network (RAN). In this paper, we present TwinRAN, a DT of the 5G RAN built leveraging the Azure DT platform. TwinRAN is built on top of the Open RAN (O-RAN) architecture and is agnostic to the vendor of the underlying equipment. We demonstrate three applications using TwinRAN and evaluate the required resources and their performance for a network with 800 users and eight gNBs. We first evaluate the performance and limitations of the Azure DT platform, measuring the latency under different conditions. The results from this evaluation allow us to optimize TwinRAN for the DT platform it uses. Then, we present the system's architectural design, emphasizing its components and interactions. We propose that two types of twin graphs be simultaneously maintained on the cloud. The first is for intercell operations, keeping a broad overview of all the cells in the network. The second twin graph where each cell is spawned in a separate Azure DT instance for more granular operation and monitoring of intracell tasks. We evaluate the performance and operating costs of TwinRAN for each of the three applications. The TwinRAN DT in the cloud can keep track of its physical twin within a few hundred milliseconds, extending its utility to many 5G network management tasks - some of which are shown in this paper. The novel framework for building and maintaining a DT of the 5G RAN presented in this paper offers network operators enhanced capabilities, empowering efficient deployments and management.

Within the development of 6G, so-called subnetworks were proposed to serve special use cases like intra-vehicle sensor-actuator communication, robot control in industrial environments, or health monitoring. These use cases are characterized by extreme communication demands between the devices served by a single subnetwork. Moreover, the subnetworks will be densely deployed, with mobile and autonomous vehicles carrying the subnetwork Access Points (APs). These properties necessitate novel approaches for frequency planning in order to enable reliable communication within all subnetworks and efficient resource usage. In this context, the problem of dynamic frequency planning for mobile 6G in-X subnetworks is investigated in this paper. To this end, a multi-objective optimization problem with the objectives of minimizing frequency subband usage and subnetwork reconfigurations leveraging knowledge about future interference scenarios is formulated. Afterward, the problem is shown to be NP-hard, and two heuristic algorithms are developed. Using realistic vehicular movement data from simulations, results show that the heuristics outperform a State-of-the-Art (SotA) benchmark. Moreover, the value of knowledge about future interference scenarios is shown. Reconfigurations can be reduced by 18.91% when prioritizing subband usage and even by 33.02% when prioritizing reconfigurations if interference scenarios are known for three time steps instead of one.

Medical applications, such as telemedicine or smart operation rooms, place stringent requirements on the underlying network architecture. 6G as the next-generation communication standard currently in research promises to satisfy the needs of such applications by utilizing advances in technology and networking concepts. One crucial concept for medical applications is the capability of using computing resources within the network. By placing the applications on such processing nodes in different locations within the Radio Access Network (RAN), the performance metrics of a medical application, such as latency, throughput, and availability can be optimized. However, problems arise when the available processing capabilities are not sufficient for all requested medical applications. In this paper, we formulate an Integer Linear Program (ILP) to address the problem of processing medical applications within the network when the processing capabilities are not sufficient. We consider the priority and different service levels of application functions and aim to place as many applications as possible with the best possible service quality. Additionally, we take into account that some applications must run in the network even if their priority is low. Furthermore, we propose a heuristic in order to obtain a good solution quickly. The evaluation of our solution and comparison to existing approaches shows an increase of accepted demands in the network by up to 35%.

With the advent of 5-th generation (5G) and the rising mobile traffic demands, automation in mobility management has become more critical. 3rd Generation Partnership Project (3GPP) has introduced mobility robustness optimization (MRO) function to automate the handover (HO) procedure by optimizing the handover control parameters (HCPs) such as handover margin (HOM) and time-to-trigger (TTT). However, traditional MRO methods require online parameter adjustments, and the performance is subject to initialization. This paper proposes a supervised learning-based parameter configuration mechanism (SL-MRO) for configuring initial cell-pair-specific HOM values using historical network data and geographical information of base stations. Besides, a conventional heuristics-based MRO algorithm (H-MRO) is introduced and implemented in a system-level simulator where the performance evaluation is done. The results demonstrate that SL-MRO achieves similar performance as H-MRO with its best initialization, without the need for online parameter adjustments.

Mobile users frequently face significant interruptions in transmission and reception during handovers from one Base Station (BS) to another, resulting in latencies that are incompatible with the stringent requirements of Ultra-Reliable Low Latency Communications (URLLC). To address this, 3GPP introduced a novel handover procedure, called Layer 1/Layer 2 Triggered Mobility (LTM), in Release 18. LTM uses lower level signaling to respond quicker to mobility events, bypassing the reconfiguration of higher layers while keeping modifications to the lower layers at a minimal level. This drastically reduces service interruptions during handovers, making them practically negligible. However, since LTM uses more frequent L1 measurements, it has a higher handover and ping-pong handover rates, as well as signaling overhead. In this work, we propose to incorporate future channel predictions in LTM to perform cell preparations and handover decisions with the goal of reducing signaling overhead and resource reservation. We focus on a controlled indoor scenario, where future user channel predictions are possible with a high accuracy. Our proactive algorithm reduces the cell preparation rate by 76 % and the handover rate by 72 %, without compromising the network sum throughput. Moreover, the resource reservation time at the target BS is reduced to nearly 0 ms.

Applications like multi user and distributed MIMO can benefit from a sparse transmission scheme due to a reduced inter-cell and inter-cluster interference. In this paper, the so-called start stop bit method, which presents a new type of transmission with sparse resource usage is studied. Several enhancements are analyzed such as i) transmitting multiple stop bit sequences, ii) adding sparse stop bit areas representing artificial fractional time frequency shifts, iii) adding a given MCS over the stop bit areas, and iv) compressing the permutation matrix needed for multiple stop bit sequences. Bit error rate curves of the proposed transmission scheme are analyzed and suitable parameter sets are provided that lead to the highest throughput per set of resource elements for a given sparsity level in comparison with a conventional OFDMA system.

Mobile Adhoc Networks (MANETs) provide a flexible and infrastructure-free communication framework, making them suitable for smart indoor environments. They enable direct, dynamic, and efficient communication between devices. Nevertheless, achieving optimal routing in such environments remains a critical challenge. Routing plays a critical role in MANETs, as it ensures efficient packet delivery between mobile and dynamically connected nodes. In this paper, we propose and evaluate a novel routing protocol for MANETs, specifically designed for Smart Home networks. Our approach is tailored to address the unique challenges of smart indoor environments, such as energy efficiency, latency, and adaptability to topology changes. Through extensive simulations, we demonstrate that our protocol significantly outperforms state-of-the-art routing protocols, decreasing power consumption by$\text{5 0. 4 6 \%}$, control overhead by 60.85 %, while maintaining end-to-end delay, making it a promising solution for Smart Home networks.

With the development of 5G, network slicing was proposed to enable service provisioning for diverse sets of Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC) users which are characterized by different Quality of Service (QoS) demands. Within network slicing, Radio Access Network (RAN) slicing plays a central role for efficient resource management. In addition, user admission control poses a major challenge. In this context, the problem of joint slice dimensioning and user admission control is investigated in this paper. To this end, an optimization problem based on symbol-level resource allocation with the objective of maximizing an operator's revenue while fulfilling the traffic requirements of all users is formulated. Afterward, the optimization problem is reduced to a knapsack problem and integrated into a Long-Term Revenue Maximization (LTRM) algorithm. Using data from real-world 5G measurements, the efficiency of the LTRM algorithm is verified, and the impact of various resource granularities in the time domain (symbol vs. slot) and frequency domain (varying Resource Block Group (RBG) sizes) is investigated. The revenue gain of the proposed joint algorithm over a sequential slice dimensioning and user admission control scheme is 24%, while symbol-level resource allocation offers at least 13% gain over a slot-based allocation for specific slices.

Navigation and trajectorial estimation of maritime vessels are contingent upon the context of positional accuracy. Even the smallest deviations in the estimation of a given vessel may result in detrimental consequences in terms of economic and ecologic quotients. To ensure an agile and precise environment for maritime vessel positional estimation, preexisting marine radar technologies can be utilized in a way that ensures a higher level of precision compared to GNSS-based identification and positioning. In this paper, we present a positional optimization for radar-based vessel navigation systems that utilize the installment of vessel detection sensors. The main objective of this research is to employ as fewer sensors as possible while preserving the attainable error threshold for positioning that is defined by International Maritime Organization (IMO). Our approach leads most of the time to a positioning error of up to 5m along shorelines and rivers and up to 50m along open coastal regions.

Providing a high-quality real-time video streaming experience to mobile users is one of the biggest challenges in cellular networks. This is due to the need of these services for high rates with low variability, i.e., stable throughput, which is not easily accomplished given the competition among (an ever-increasing number of) users for limited network resources and the high variability of their channel conditions. A way to improve the user experience is by exploiting users’ buffers and the ability to provide a constant data rate to everyone, as one of the initially envisioned features of 5G networks. However, it was already shown that the latter is not very efficient, neither in terms of the achievable data rates nor in terms of the amount of resources left unused. In this paper, we provide a theoretical-analysis framework for resource allocation in 5G networks that leads to an improved user experience when watching live video while providing a constant video resolution at almost all times. We do this by solving four problems, in which the objectives are to provide the highest achievable video resolution to all single-class and multi-class users, and to maximize the number of users that experience a given video resolution. The analysis is validated by simulations that are run on publicly-available traces. We also compare the performance of our approach against other techniques for different Quality of Experience metrics. Results show that performance can be improved by at least 15% with our approach compared to state of the art.

There is no system yet that provides predictable data plane and control plane operations in programmable networks. However, both predictable data plane and control plane operations are needed, e.g., in industrial networks. Particularly there, the operation of the network needs to be planned and, hence, relies on network operations that are deterministic and executed in a timely manner. To fill this gap, this paper proposes our system namedNAGA, which provides data plane deterministic guarantees along with consistent and timely network updates in programmable networks. In order to not rely on specialized hardware,NAGAuses widely-available hardware capabilities such as priority queuing and label-based forwarding. Whereas the real implementation ofNAGAin a P4-based testbed demonstrates that applications receive guaranteed performance in terms of latency and data rate, simulation studies show the ability ofNAGAto be even deployed in large scale scenarios beyond industrial networks, such as wide area and data center networks.

The rapid increase in connected devices has significantly intensified the computational and communication demands on modern telecommunication networks. To address these challenges, integrating advanced Machine Learning (ML) techniques like Federated Learning (FL) with emerging paradigms such as Multi-access Edge Computing (MEC) and Software-Defined Wide Area Networks (SD-WANs) is crucial. This paper introduces online resource management strategies specifically designed for FL model aggregation, utilizing intermediate aggregation at edge nodes. Our analysis highlights the benefits of incorporating edge aggregators to reduce network link congestion and maximize the potential of edge computing nodes. However, the risk of network congestion persists. To mitigate this, we propose a novel aggregation approach that deploys an aggregator overlay network. We present an Integer Linear Programming (ILP) model and a heuristic algorithm to optimize the routing within this overlay network. Our solution demonstrates improved adaptability to network resource utilization, significantly reducing FL training round failure rates by up to 15% while also alleviating cloud link congestion.

Mobility management in 5G is challenging due to the usage of high frequencies and dense cell deployments. As a result, users experience frequent handovers that cause an interruption in transmission/reception and diminish network capacity. In the common handover algorithm, the target Base Station (BS) is selected based solely on the signal strength, while the available resources are not considered, leading to overloaded cells, especially for macro cells with large coverage. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to provide α-fairness in data rates among users and to reduce handovers. To accomplish that, we jointly perform user assignment and resource allocation while accounting for the interruption due to handovers. This is an integer nonlinear program and, by relaxing it, an upper bound is obtained. Further, because of the time complexity of the original problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm, which finds near-optimal user-to-BS assignments and the amount of resources that should be allocated to a user. Our approach outperforms considerably state of the art in terms of fairness and handover rate while being within at most 12% of the optimum in most cases.

Most network planning problems in the literature consider metrics, such as cost, availability, and other technology-aware attributes. However, network operators now face new challenges in designing their networks to minimize their dependencies on manufacturers. A low dependency is associated with higher network robustness in case one or more manufacturers fail due to erroneous component design, geopolitical banning of manufacturers, or other reasons discussed in this work. Our work discusses network sovereignty, i.e., the ability to operate a network while minimizing the dependencies on a particular manufacturer to minimize the impact of simultaneous manufacturer failure(s). Network sovereignty is considered by solving the manufacturer assignment problem in the network such that robustness is maximized. The following three main contributions of this work are, first, the discussion of network sovereignty as a special attribute of dependability, second, the introduction of a novel metric—the path set diversity (PSD) score to measure a network's sovereignty based on the manufacturers used in the network, and, third, the introduction of Naga, an integer linear program formulation to maximize network sovereignty using the PSD score. We compare the Naga’s performance with centrality metrics-based heuristics and an availability-based optimization. Our work aims to be the foundation to guide network operators in increasing their network sovereignty.

Industry 4.0 has brought to attention the need for a connected, flexible, and autonomous production environment. The New Radio (NR)-sidelink, which was introduced by the third-generation partnership project (3GPP) in Release 16, can be particularly helpful for factories that need to facilitate cooperative and close-range communication. Automated Guided Vehicles (AGVs) are essential for material handling and carriage within these environments, and using NR-sidelink communication can further enhance their performance. An efficient resource allocation mechanism is required to ensure reliable communication and avoid interference between AGVs and other wireless systems in the factory using NR-sidelink. This work presents a simulation analysis of the 3GPP standardized resource allocation algorithm for NR-sidelink in an industrial scenario with a use case of cooperative-carrying AGVs. We suggest further improvements that are tailored to the quality of service (QoS) requirements of an indoor factory communication scenario with cooperative AGVs. The use of NR-sidelink communication has the potential to help meet the QoS requirements for different Industry 4.0 use cases. This work can be a foundation for further improvements in NR-sidelink in 3GPP Release 18 and beyond.

The rising prevalence of real-time applications that require deterministic communication over mobile networks necessitates the joint operation of both mobile and fixed network components. This joint operation requires designing components that interact between the two technologies to provide users with latency and packet loss guarantees. In this work, we demonstrate a fully integrated 5G-DetNet that can guarantee the end-to-end demands of different flows. Moreover, we show how such a network can be implemented using low-cost hardware and open-source software, making it accessible to many 5G testbeds. The features demonstrated in this work are a network manager that does the routing and scheduling, an application function in the 5G core that interfaces with the network manager, and a network-side translator for user-plane management and de-jittering of the real-time streams.

With the rapid increase in the amount of exchanged traffic over cellular networks, stemming partly from computation-intensive tasks, and the highly mobile nature of the users, mobility management exhibits considerable challenges in next-generation cellular networks. A way to alleviate these problems is by using Software-defined Radio Access Networks (SD-RAN), where a centralized controller with a complete overview of the network topology (distribution of users across base stations and their channel conditions) can make decisions on the user assignment and resource allocation. To that end, in this paper, we formulate an optimization problem with the objective of maximizing the network utility, where computation-intensive tasks are sent from the users to edge clouds, taking into account the communication constraints (uplink and downlink bandwidth) as well as the finite storage and processing capabilities of edge clouds. Moreover, we provide a user rate guarantee to satisfy an additional application for all users. The problem is NP-hard, therefore, we propose to use Deep Reinforcement Learning (DRL) to solve it. Extensive realistic simulations show that our approach is close to the optimal solution, where the latter is obtained using a solver, while outperforming a benchmark by up to 65%.

Time-Sensitive Networking can provide a wide array of QoS guarantees that can be leveraged in a variety of use cases, ranging from industrial applications to autonomous driving. While the control plane can be configured following either a distributed or a centralized paradigm, it is difficult to make general assertions about the affinity of different topologies and use cases towards these configuration methods. In this work, we propose a hybrid simulation of the Resource Allocation Protocol to evaluate the reservation performance across a wide range of network configurations. Results show distinct behaviors between the configuration paradigms, making a case for decentralized control when scalability is critical. In addition, we make our implementation of the developed hybrid model publicly available.

A significant portion of modern network traffic analysis still relies on human expertise only. To overcome human limitations in light of increases in volume, dynamicity, and overall traffic complexity, modern networks need to autonomously gain an understanding of traffic patterns and present them in an interpretable way. This work presents T-MAW, an approach for Traffic Monitoring and Analysis using Weighted Stochastic Block Models (WSBMs). T-MAW applies WSBMs to network data to create traffic characterizations in human-interpretable form. In addition to the insights gained from the fitted models, T-MAW evaluates unseen traffic against these models to perform anomaly detection. Both, network node behavior characterization and anomaly detection complement human expertise in modern network traffic analysis. As an example, we show how T-MAW can be used to create a behavior-based structured view of network nodes in a real campus network. In the anomaly detection context, we present results for an IP scan attack against the network, as well as from a layer-2 device fault that caused network disruption.

Semantic Communication has recently emerged as a novel communication strategy that prioritizes transmitting meaning over conventional bit-based transmission. By significantly reducing resource requirements in communication tasks such as video conferencing, natural language, and audio transmission, Semantic Communication promises better utilization of challenging, near radio link failure (NRLF) channels. This paper introduces a novel semantic communication framework designed to further enhance the resilience of the transmission of semantics over NRLF channels. Unlike classical, Shannon-based communication that prioritizes the perfect reception of bits, our approach focuses on ensuring the successful synthesis of the message semantics. Our framework leverages the significance of discrete semantics, and a cross-layer strategy to ensure message integrity and comprehension, even under significant loss. Key to our framework is the novel, code block based Proactive Redundancy Transmission (PRT) mechanism prioritizing critical semantics, coupled with a novel error concealment step enabling meaningful reconstruction of non-critical semantics. We establish the resulting importance-driven resilience optimization problem, and introduce and validate preliminary heuristics as an initial attempt to optimize it. We formalize, implement, and evaluate our framework, demonstrating significant improvements in the resilience of semantic communication in NRLF environments. Our evaluations, leveraging a First Order Motion Model (FOMM) for video conferencing synthesis, underscore the resilience of our semantic communication framework against traditional H.265 compression under challenging Channel Block Error Rates (CBLERs). Unlike H.265, which fails to decode under significant CBLERs, our method exhibits remarkable resilience, maintaining perceptual quality even with CBLERs surpassing 75%.

Unmanned Aerial Vehicles (UAVs) have created new opportunities for various applications in civil airspace. One critical aspect of a successful operation is to establish reliable communication between a UAV and the remote pilot in a Ground Control Station in Remotely Piloted Aircraft Systems. The effectiveness of this communication, known as C2 communication, depends on the underlying network technology (LTE/5G, Satellite, etc.) and the specific application telemetry protocol. A single C2 link using cellular or satellite communication technology fails to satisfy the ICAO C2 performance requirements [1]. Therefore, a multi-link framework for C2 communication is widely adopted. However, the homogeneous cellular-based multi-link [2] experimental solutions have limitations concerning the geographical location of Beyond Visual Line-of-Sight (BVLOS) operations. Additionally, simulation analysis of the heterogeneous multi-link [3] solution, using cellular and GEO-satellite communication, is inadequate to conclude the link performance requirements (bandwidth and latency) for the telemetry protocol. Therefore, we propose a heterogeneous 5G and LEO-satellite Iridium multi-link network architecture for C2 communication. Our quantitative analysis of the link technology present in this architecture demonstrates (i) the required logical Link Quality Indicator for C2 communication, and (ii) the possible solutions for C2 telemetry protocol to utilize the link capabilities in this architecture. We demonstrate that a cellular link can provide a throughput of 3.5, 8.5 Mbps and 30 ms latency for 5G, whereas an Iridium satellite link can provide 22, 88 kbps (uplink, downlink) throughput and 750 ms latency sufficient for reliable C2 communication.

This work presents a novel flow-based molecular communication (MC) testbed using spectral sensing and ink intensity estimation to enable real-time multi-molecule (MUMO) transmission. MUMO communication opens up crucial opportunities for increased throughput as well as implementing more complex coding, modulation, and resource allocation strategies for MC testbeds. An estimator using non-invasive spectral sensing at the receiver is proposed based on a simple absorption model. We conduct in-depth channel impulse response (CIR) measurements and a preliminary communication performance evaluation. Additionally, a simple analytical model is used to check the consistency of the CIRs. The results indicate that by utilizing MUMO transmission, on-off-keying, and a simple difference detector, the testbed can achieve up to 3 bits per second for near-error-free communication, which is on par with comparable testbeds that utilize more sophisticated coding or detection methods. Our platform lays the ground for implementing MUMO communication and evaluating various physical layer and networking techniques based on multiple molecule types in future MC testbeds in real time.

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

This paper presents an evaluation of non-orthogonal multiple access (NOMA) as a novel approach for diffusion-based molecular communication (DBMC) networks. The scheme draws from the example of power-domain NOMA in classical communication and relies on differences in the number of received molecules. It utilizes successive interference cancellation to separate simultaneously transmitted messages from multiple transmitters (TXs) at the receiver (RX) using a single molecule type. We analytically derive the bit error probability of a communication system using DBMC-NOMA with$K$TXs and a central RX and validate the model with Monte Carlo simulations. Our results show that the emitted number of molecules from each TX is a crucial parameter to optimize the performance of DBMC-NOMA. Additionally, we compare the performance of DBMC-NOMA against time-division multiple access (TDMA) and molecule-division multiple access (MDMA) with respect to the mutual information at the RX. The investigation shows that TDMA and MDMA act as the lower and upper performance bounds for DBMC-NOMA, respectively. For a sufficiently large molecule budget and SNR, DBMC-NOMA outperforms TDMA and matches MDMA using only one molecule type even as the number of TXs grows. These results show the potential of NOMA as an option for DBMC and the need for further analysis of power control schemes to optimize the number of emitted molecules in DBMC networks.

This paper presents a real-time semantic video communication method for general scenes, combining lossy semantic map coding with motion compensation to achieve reduced bit rates while maintaining perceptual and semantic quality. Our findings show that semantic image synthesis effectively adapts to minute errors resulting from motion estimation, eliminating the need to transmit the residuals. We recommend the Group of Pictures approach as a more efficient alternative. Comparative assessments against HEVC and VVC confirm the method’s effectiveness. This research paves the way for efficient real-time semantic video communication, addressing the demands of data-intensive visual applications.

Time-sensitive applications are increasingly prevalent in various network domains, such as industrial, medical, and vehicular communications, imposing substantial demands on network infrastructure. Consequently, ensuring low latency has become a crucial requirement for future networks, particularly through the implementation of deterministic latency network controllers. However, it is essential to recognize that the network controller represents just one facet of network performance management. The configuration of the network’s topology also significantly influences its overall performance. This study, therefore, investigates the impact of different topologies on network performance, specifically focusing on deterministic latency guarantees. Our analysis shows the correlation between graph metrics characterizing the topology and its performance. This correlation facilitates a straightforward ranking of topology performance during critical phases like network planning or expansion. We introduce a readily obtainable graph metric that enables relative performance ranking without the need for exhaustive simulations or emulations. The metric exhibits a Spearman Ranking correlation coefficient exceeding 0.93.

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

The hand blockage effect of the human hand around the user equipment (UE) is too considerable to be ignored in frequency range 2 (FR2). This adds another layer of complexity to the link budget design in FR2 for 5G networks, which already suffer from high path and diffraction loss. More recently, multi-panel UEs (MPUEs) have been proposed as a way to address this problem, whereby multiple distinct antenna panels are integrated into the UE body as a way to leverage gains from antenna directivity. MPUEs also enhance the Rx-beamforming gain because it is now subject to each individual antenna panel. In this paper, the mobility performance of hand blockage induced by three practical hand grips is analyzed in a system-level simulation, where in each grip both the UE orientation and the hand positioning around the UE is different. It is seen that each hand grip has a significant impact on mobility performance of the network, where in the worst case mobility failures increase by 43% compared to the non-hand blockage case. Moreover, a detailed analysis of the tradeoff between the mobility key performance indicators and the panel and Rx beam switching frequency is also studied. Results have shown that both the panel and Rx beam switches can be reduced considerably without compromising on the mobility performance. This is beneficial because it helps in reducing UE power consumption.

GSM-R (GSM for Railways) is a 2$G$-based standardized ground-to-train communications system that enabled interoperability across different countries. However, as a 2G-based system, it is nearing its lifetime and therefore, it will be replaced with 5G-based Future Railway Mobile Communications System (FRMCS). FRMCS is expected to bring in new use cases that demand low latency and high reliability. However, from a mobility perspective, it is not clear how the low latency and high reliability will be achieved. This paper investigates the effect of handover procedure on latency and reliability and analyzes which use cases of FRMCS can be satisfied using baseline handover. We also sweep through different handover parameter configurations and analyze their effect on mobility performance. Then, we analyze the effect of mobility performance on packet latency and reliability. Our results show that, with baseline handover, Standard Data Communications Scenario is met and optimizing for baseline handover performance can reduce latency by up to 18.5%, indicating that optimizing for mobility performance is crucial in FRMCS.

The currently researched 6G communication standard promises to enable new applications with the native integration of new technologies and concepts. Especially the medical area will greatly benefit from the developments of new communication networks. As the communication infrastructure will be able to satisfy stringent and varying requirements, emerging medical applications such as telemedicine and telesurgery will be enabled. However, the concrete networking architecture remains an open question for research. In this paper, we propose a dedicated med-ical 6G Radio Access Network (RAN) architecture and discuss possible usage scenarios based on two medical use cases. First, a network architecture is presented which leverages in-network computing to execute medical applications. The main idea of this concept is the dynamic interaction between medical applications and the network. In particular, the placement of Modular Application Functions (MAFs) and their execution depends on the state of the network. Secondly, we map two medical use cases, namely a semiautonomous telerobotic examination suite and a context-sensitive medical environment, to the proposed network architecture and explain the interaction between our network architecture and these applications in detail. Our approach demonstrates the potential of the combined development of medical applications and their underlying communication architecture.

As 5G is currently being rolled out, security considerations for this critical infrastructure are getting more into focus. Hereby, the security investigation of the 5G core as the central element plays a pivotal role. The structure of the core is based on a Service-Based Architecture (SBA) consisting of Network Functions (NFs). These NFs communicate via REST/HTTP2 interfaces, that can be secured using Transport Layer Security (TLS) for encryption. However, this enhanced security is not enforced by standardization, but up to the system operator to decide. Therefore, in this work we derive recommendations on when to use TLS. For that, we investigate the overhead of TLS in a simulation, based on the open-source frameworks Open5GS and UERANSIM. To measure a user-relevant overhead, we look into 5G's UE registration and Packet Data Unit (PDU) session establishment procedures. By testing 14 of the most relevant cipher suites, our results show, that TLS adds no more than 1\,% of time overhead in a running system. Further, we show cipher suites using ECDSA keys to be faster than the ones using RSA keys. Surprisingly, TLS 1.3 shows a larger performance overhead than its predecessor TLS 1.2. We demonstrate CPU and memory overhead of TLS to be insignificant in the context of the 5G core.

While LTE networks are known to provide relatively high data rates, reaching values as high as tens of Mbps, these rates exhibit considerable variability over time. The rate variability hurts especially the performance of applications and services that require stable data rates, such as real-time video streaming, online gaming, virtual reality, augmented reality, etc. 5G emerged as a solution to this as well as to many other problems. However, it has been shown that strict constant data rates come at the cost of underutilized network resources, resulting in inefficient operation of cellular networks. Therefore, a tradeoff between the data rate stability, important to cellular users, and the efficient utilization of resources, important to network operators, needs to be taken into account. To that end, in this paper, we consider the problem of allocating all the network resources to cellular users in such a way that it provides as high a data rate as possible to all users while limiting the rate variation within tight bounds. We do this for different scenarios in terms of the user activity, user type, and the nature of the policy. Firstly, we consider the case of static allocation policy, irrespective of channel conditions, for users that are always active. Then, for these same users, we look at the case when resources are allocated dynamically over time. Secondly, we consider static and dynamic policies for users that are only intermittently active. Thirdly, we consider the case with users having different Service Level Agreements (SLAs) with the cellular operator. Furthermore, we run extensive simulations with input parameters from real traces. Results show that allocating the resources dynamically improves performance in terms of data rates over static allocation mechanisms by an additional 10%, and that allowing a slightly higher outage in not complying with the guaranteed data rate further increases the user's throughput by at least 20%.

While one of the main features of 5G networks is provisioning very high rates with low (or no) variability to cellular users, it has been shown that this turns out to be very ineffective for operators because it leads to an abundance of unused network resources. Yet, reallocating the unused resources to the same users, after providing them with the same constant rate, increases back the variability in data rates. A more efficient way would be to provide different low-variability data rates to the users depending on their channel conditions while trying to bring the wasted resources to the lowest possible extent. To that end, in this paper, two approaches are considered; one with reserved resources for every user and the other where the amount of resources is decided on the fly, depending on their current channel conditions. Then, for each approach, we look at different allocation policies and derive the corresponding maximum achievable constant rate for every user jointly with the level of resource utilization, showing which policy is more beneficial. Further, the performance is evaluated on a real 5G trace using both extensive simulations and real measurements conducted on OpenAirInterface. Results show that no-resource reservation policies increase the utilization of resources and data rates at the expense of increased rate variability across all the users. Moreover, all the policies proposed in this paper outperform state-of-the-art approaches by at least 2×, bringing the waste of resources down to 15%.

The diversity of deployed applications in data centers leads to a complex traffic mix in the network. Reconfigurable Data Center Networks (RDCNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations, transport layer protocols, and congestion control (CC) algorithms. This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates? This paper focuses on TCP and presents a measurement study of TCP performance in RDCNs. In particular, it evaluates diverse traffic mixes combining TCP variants, UDP, and QUIC transport protocols. The quantitative analysis of the measurements shows that migrated TCP flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g., increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.

Website Fingerprinting (WFP) attacks infer the websites or webpages a user is visiting from encrypted traffic. To date, it remains uncertain if WFP can attack many users from a central location in an online scenario. We close this gap with PROFI, a WFP attack that detects websites based on the initial TLS connection from the client to the server using at most the connection’s first 30 packets. PROFI achieves a precision and recall of 86.51% and 85.35% in a closed-world, and 68.90% and 78.71% in an open-world scenario, which is competitive to state-of-the-art (SoA) WFP attacks, while taking a fraction of the time of SoA attacks to classify a webpage. Further, we implement PROFI as a micro service-based prototype and evaluate the attack in an online scenario with real traffic traces. We show that PROFI can monitor up to 100 websites at 10 G, corresponding to up to 424 webpages per second. We also show that PROFI has the potential to interfere with a victim’s webpage access.

In our digitized society, emerging applications require highly-performing and flexible networks that can adapt to satisfy varying connectivity needs. P4 as a domain-specific programming language for data plane pipelines introduces the required flexibility through easy-to-use programmability. However, the performance of P4-capable devices is still an open question that has not yet been completely addressed. Understanding whether a P4-enabled device can meet the performance requirements for a specific network function pipeline is key for planning as well as for the selection of the proper deployment scenarios in a network. To bridge this gap, we propose a simple analytical model that can predict the performance of network functions written in P4 for a given device. The programmable data plane of P4 devices is modeled as a forward queueing system with a variable service rate that depends on the complexity of the configured data path program. On top of the data plane model, the controller’s interaction is modeled as a feedback queueing system. In terms of the analysis, we first assume exponentially distributed service times in the data plane and control plane. In a second step, we extend the analysis to generally distributed service times using approximations. In order to cover a wide rang of possible behavior of the control plane, three types of distributions with different coefficients of variation are inspected: Erlang, exponential, and hyperexponential. We evaluate the accuracy of our model for different scenarios and show that the discrepancy between actual results and our analytical predictions does not exceed 8.7%. We also validate the model with a commercial P4 hardware switch.

This paper presents MobiFi, a framework addressing the challenges in managing LiFi-WiFi heterogeneous networks focusing on mobility-aware resource allocation. Our contributions include introducing a centralized framework incorporating reactive and proactive strategies for resource management in mobile LiFi-only and LiFi-WiFi networks. This framework reacts to current network conditions and proactively anticipates the future, considering user positions, line-of-sight blockages, and channel quality. Recognizing the importance of long-term network performance, particularly for use cases such as video streaming, we tackle the challenge of optimal proactive resource allocation by formulating an optimization problem that integrates access point assignment and wireless resource allocation using the alpha-fairness objective over time. Our proactive strategy significantly outperforms the reactive resource allocation, ensuring 7.7% higher average rate and 63.3% higher minimum user rate for a 10-user LiFi-WiFi network. We employ sophisticated techniques, including a Branch and Bound-based Mixed-Integer solver and a low-complexity, Evolutionary Game Theory-based algorithm to achieve this. Lastly, we introduce a novel approach to simulate errors in predictive user position modeling to assess the robustness of our proactive allocation strategy against real-world uncertainties. The contributions of MobiFi advance the field of resource management in mobile LiFi-WiFi networks, enabling efficiency and reliability.

Control over networks is envisioned to be one of the driving applications of future mobile networks. Networked control systems contain sensors and controllers exchanging time-sensitive information to fulfill a particular control goal. In this work, we consider$N$heterogeneous feedback control loops closed over a wireless star network. A centralized scheduler located at the central node, i.e., base station (BS), determines the transmission schedule of sensor-to-BS and BS-to-controller communication links. We assume that each link can accommodate a single transmission at a time and is prone to data losses with time-varying probability. Moreover, each controller estimates the system state remotely based on available information. In such a setting, we formulate an optimization problem to minimize the network-induced estimation error at the controller. In particular, we determine the optimal transmission schedule on each link that leads to the minimum normalized mean squared error (nMSE) in a given finite horizon (FH). We compare the performance of our proposed FH scheduler to various schedulers from the existing literature. Our simulation results show that by solving the finite horizon problem optimally, we are able to reduce the nMSE by$10\%$when compared to the best performing scheduling policy among the selected policies from the state-of-the-art. Moreover, the linear-quadratic Gaussian (LQG) cost is reduced by more than$13\%$indicating a control performance improvement in the network.

5G networks have emerged as the only viable solution to render a satisfying level of performance to different types of services, each of them with very stringent traffic requirements. One of those services are Ultra-Reliable Low-Latency Communications (URLLC). A use case where these services are especially sensitive are vehicular networks. Therefore, in order to satisfy their traffic requirements, adequate resource allocation schemes should be devised. However, the time-varying nature of the channel conditions in wireless networks renders this process challenging. In this paper, we consider the problem of jointly allocating Radio Access Network (RAN) resources and computing resources (to process the data from vehicles) such that all the traffic requirements of individual users are met and the utility is maximized for different types of fairness. We formulate an optimization problem for the general case of$\alpha$-fairness, explore its characteristics, and consider in more detail the opposite sides of fairness; the case of no fairness provided$(\alpha=0)$and the max-min fair allocation$(\alpha\rightarrow\infty)$. For each of these problems, we propose polynomial-time assignment heuristics. Using data from real traces, we show that the performance achieved with our approaches is not more than 1% away from the optimum.

The introduction of Software-Defined Radio Access Networks in 5G, whose main feature is the possibility of decoupling the control plane from the data plane, and associating the former with a controller away from base stations, represents a paradigm shift in the way the network resources are allocated. This property provides an increased flexibility in cellular network operation, yielding significant improvements compared to the pre-5G resource allocation era. However, the full extent to which this amelioration ranges is not yet clear for different metrics of interest and objectives. One such objective is to allocate resources so that proportional fairness is achieved. Therefore, in this paper, we consider analytically the problem of proportionally fair allocation in SD-RAN environments, by deriving the policy which accomplishes that. We do this for two scenarios. In the first, the goal is to provide proportional fairness across all the users in the network, whereas in the second, the objective is to provide proportionally fair allocation in terms of the throughput of all BSs. We evaluate the performance with input parameters from a real trace. Results show that the introduction of SD-RAN increases the value of the objective by up to an order of magnitude compared to the scenario with no SD-RAN.

Software-Defined Radio Access Networks (SD-RANs), introduced in 5G, represent a paradigm shift in the process of cellular network resource allocation. The decoupling of the control from the data plane, and associating the former with a controller away from Base Stations (BSs), has enabled an increased flexibility in allocating network resources which would lead to performance improvements. However, so far, it is not yet clear to what extent this amelioration ranges in terms of the maximum throughput that can be achieved. Therefore, in this paper, we consider analytically the problem of maximizing the overall network throughput in an SD-RAN environment, by deriving the policy which accomplishes that along with the total throughput, of interest to cellular operators. We assess the performance with real user traces. Results show that the introduction of SD-RAN improves performance by at least 20%.

Mobility management in 5G, especially at higher frequencies, is challenging because the signal quality fluctuates significantly due to blockages of Line of Sight (LoS), shadowing and user mobility. As a result, users experience frequent handovers, which reduce the network capacity. In order to perform smooth network operation, the decisions when to handover and to which Base Station (BS) a user is to be assigned should be considered jointly. Another important goal is to strive for fairness in data rates among the users. To this end, in this paper, we formulate an optimization problem whose solution provides proportional fairness and reduces the handover rate significantly. To solve the problem, we propose a Deep Reinforcement Learning (DRL) algorithm, specifically a Deep Q Network (DQN), which turns out to find a near-optimal user-to-BS assignment. We compare our approach with other state-of-the-art baselines and show that it outperforms them considerably in terms of fairness, handover, ping-pong and radio link failure rates while being within 96% of the optimal solution. Our DQN algorithm also reduces the handover rate by 86% and avoids ping-pong handovers.

Researchers developing new architectures and algorithms for data center networks (DCNs) face the challenge of producing meaningful evaluations of their contributions. Traditional evaluation methods like traffic traces and parametric models can fail to reveal weak spots in DCNs. The concept of adversarial inputs shapes traffic data, making it challenging for a DCN to serve it. Adversarial traffic can provide insight into performance issues of a DCN that might go unnoticed with traces and models. This paper presents AdFAT, a genetic algorithm-based system for automated adversarial input generation for DCNs. While previous work focuses on reordering flow volumes or individual packets, our system uses flow arrival times as the adversarial traffic dimension. By creating adversarial flow arrivals for a demand-oblivious RotorNet topology, we show that AdFAT not only finds traffic that causes 22.64% higher mean flow completion times than traffic with uniform random arrival times but is also sensitive to the inherent periodicities and connection patterns of RotorNet. The results indicate AdFAT can find and exploit temporal and structural properties of dynamic and demand-oblivious topologies in an automated way.

As public and private cloud-native deployments of the 5G Core (5GC) networks are rolling-out on a wide scale, attention is shifting towards efficient state management. While stateful deployments were the default method in the previous generations of mobile networks, they lack the necessary flexibility that cloud-native orchestration demands. Yet, traditional approaches taken to enable stateless deployments require the operators to sacrifice on performance due to the frequent state transactions. To overcome this issue, in this paper we propose a Piggyback-based and a Proactive-Push approach which allow for procedure-aware stateless 5GC systems. Our evaluations highlight the advantages of the Piggyback approach for two synchronous control procedures, reducing their completion time by ~44% and ~70% compared to the baseline. For asynchronous procedures, the Proactive-Push approach outperforms the baseline with ~13% and ~22%. More importantly, these mechanisms do not pose additional overhead on CPU and bandwidth utilization.

The rising number of connected Internet of Things (IoT) devices in 5G networks and the standardization of the 3GPP reduced capability (RedCap) devices, turn the IoT energy efficiency into a topic of paramount importance for 5G. The design goals and use cases of RedCap devices highlight the need for long device battery life due to the infeasibility of replacing batteries. With the focus emerging on sustainable networks, battery lifetime prediction becomes essential. Therefore, in this paper, we propose and evaluate a Markov Chain based energy consumption model suitable for IoT devices in 5G networks, especially RedCap devices. We design a realistic model consistent with the procedures described in 3GPP standardization, mainly focused on the uplink transmission procedures. The proposed model is validated through extensive analysis with varying interarrival times (IAT) of the uplink traffic. For short IAT, the analytical results show a decrease of 33% in energy consumption and 89% in transmission latency. This demonstrates that our model can be applied to evaluate battery life for a broad range of IoT devices.

Networked control systems (NCSs) are an example of task-oriented communication systems, where the purpose of communication is real-time control of processes over a network. In the context of NCSs, with the processes sending their state measurements to the remote controllers, the deterioration of control performance due to the network congestion can be partly mitigated by shaping the traffic injected into the network at the transport layer (TL). In this work, we conduct an extensive performance evaluation of selected TL protocols and show that existing approaches from communication and control theories fail to deliver sufficient control performance in realistic network scenarios. Moreover, we propose a new semantic-aware TL policy, which uses the process state information to filter the most relevant updates and the network state information to prevent delays due to network congestion. The proposed mechanism is shown to outperform all the considered TL protocols with respect to control performance.

The possibility of decoupling the operation of control plane from data plane in RANs, which became possible with the introduction of Software-Defined Networks in 5G, brought a paradigm shift in cellular network operation. The key element that enables this is a centralized controller, located away from base stations. This yields increased flexibility in the functioning of cellular networks, resulting in considerable enhancements compared to classical pre-5G resource allocation approaches. However, so far the range these improvements span is known only in terms of throughput. The advantages in terms of other metrics and objectives, like delay fairness, are not yet known. Therefore, in this paper, we derive analytically the resource allocation policies that lead to different delay fairness definitions among the entities in an SD-RAN-enabled network and show the advantages compared to the classical pre-5G approaches. We do this for different scenarios. First, we consider the minimum potential delay fairness in the network. Then, we consider the min-max delay fairness among base stations, and also the min-max delay fairness among users. We evaluate performance extensively with input data from a dataset. The results indicate that the introduction of SD-RAN improves the objective value up to 6× compared to policies without SD-RAN.

Sixth-generation (6G) networks propose integrating multiple networks and domains while improving network performance. Hence, today’s networks are becoming increasingly larger and more complex. Traditional methods to manage networks are facing significant challenges as the topology sizes, traffic patterns, and network domains are changing.This paper presents the state-of-the-art in literature for network management and proposes a research plan for an autonomous network management framework fueled by the Digital Twin (DT) paradigm. Unlike the existing methods such as Queuing Theory (QT) or network simulation studies, the proposed framework relies on state-of-the-art Graph Neural Networks (GNNs) for network performance analysis. We argue that seamless integration of networks while improving performance guarantees can be achieved via autonomous management of networks and present a research plan in this paper.

Cluster orchestrators such as Kubernetes (K8s) provide many knobs that cloud administrators can tune to conFigure their system. However, different configurations lead to different levels of performance, which additionally depend on the application. Hence, finding exactly the best configuration for a given system can be a difficult task. A particularly innovative approach to evaluate configurations and optimize desired performance metrics is the use of Digital Twins (DT). To achieve good results in short time, the models of the cloud network functions underlying the DT must be minimally complex but highly accurate. Developing such models requires detailed knowledge about the system components and their interactions. We believe that a data-driven paradigm can capture the actual behavior of a network function (NF) deployed in the cluster, while decoupling it from internal feedback loops. In this paper, we analyze the HTTP load balancing function as an example of an NF and explore the data-driven paradigm to learn its behavior in a K8s cluster deployment. We develop, implement, and evaluate two approaches to learn the behavior of a state-of-the-art load balancer and show that Machine Learning has the potential to enhance the way we model NF behaviors.

One of the three types of services supported by 5G networks are Ultra-Reliable Low-Latency Communications, which are characterized by the stringent requirement to deliver packets within a very short time with a high reliability. Besides being successfully transmitted/received, these data need to be processed as well. To satisfy these strict requirements, one needs to determine both the required data rate and the processing rate, given the channel conditions and traffic intensity of the service. Moreover, with constraints on both the Radio Access Network and edge computing resources as well as with the competition between an ever-increasing number of users in cellular networks, a very important question which arises is that of admission control. This guarantees users will not suffer from deteriorating performance. In this paper, using analytical modeling, we derive admission control policies for both homogeneous and heterogeneous types of users, taking into account the delay incurred by the RAN part of the network and that caused by the finite computing capability at the edge. We validate theoretical outcomes and provide additional insights on a 5G dataset. Results show that the number of admitted users depends on the worst channel conditions, the deadline by which the data must be processed and the available resources. There is an almost linear increase in the number of admitted users with the decrease in latency.

Frequency range 2 (FR2) has become an integral part of 5G networks to fulfill the ever-increasing demand for data hungry-applications. However, radio signals in FR2 experience high path and diffraction loss, which also pronounces the problem of inter and intra-cell interference. As a result, both the serving and target links are affected, leading to radio link failures (RLFs) and handover failures (HOFs), respectively. To address this issue, multi-panel user equipment (MPUE) is proposed for 5G-Advanced whereby multiple spatially distinct antenna panels are integrated into the UE to leverage gains from antenna directivity. It also opens the possibility of using UE-side Rx-beamforming for each panel. In this paper, three different Rx-beamforming approaches are proposed to improve the serving link, the target link, and the handover process for an MPUE equipped with three directional panels. Thereafter, the mobility performance is analyzed in a system-level simulation for a multi-beam FR2 network. Results have shown that the proposed schemes can help reduce RLFs by 53% and HOFs by 90%.

Goal-oriented communication is a promising approach to tailor the network resource management algorithms to the needs of particular applications, thus enhancing the efficiency of resource utilization and boosting the application performance. In the context of distributed cyber-physical systems and networked control systems, the design of a control-aware transport layer (TL) represents a realistic approach for goaloriented communications since it can be integrated into generic control setups without making assumptions on particular hardware or network technologies and deliver enhanced end-to-end performance. This demo showcases the application performance of different TL schemes used for communication between the sensors and the controllers monitoring and actuating inverted pendulums, i.e., multi-dimensional plants. The nodes of the control loops are realized with Zolertia Re-Mote devices, and multiple control loops communicate over the shared wireless network using IEEE 802.15.4 standard. We use the demonstration testbed to compare the performance of conventional, state-of-the-art, and novel goal-oriented TL schemes by observing the emulated dynamics of inverted pendulums.

In Wireless Networked Control Systems (WNCSs), the feedback control loops are closed over a wireless communication network. The proliferation of WNCSs requires efficient network resource management mechanisms since the control performance is significantly affected by the impairments caused by network limitations. In conventional communication networks, the amount of transmitted data is one of the key performance indicators. In contrast, in WNCSs, the efficiency of the network is measured by its ability to facilitate control applications, and the data transmission rate should be limited to avoid network congestion. In this work, we consider an experimental setup where multiple control loops share a wireless communication network. Our testbed comprises up to five control loops that include Zolertia Re-Mote devices implementing IEEE 802.15.4 standard. We propose a novel relevance- and network-aware transport layer (TL) scheme for WNCSs. The proposed scheme admits the most important measurements for the control process into the network while considering current network conditions. Moreover, we propose a mechanism for the scheme parameters adaptation in dynamic scenarios with unknown network statistics. Unlike the conventional TL mechanisms failing to provide adequate control performance due to either congestion in the network or inefficient utilization of available resources, our method prevents network congestion while keeping the control performance high. We argue that relevance- and network-awareness are critical components of network protocol design to avoid control performance degradation in practice.

Mobility management in 5G is challenging, and at higher frequencies, a larger number of cells is needed to provide similar coverage to that in 4G. Consequently, Base Stations (BSs) are placed much more densely and users experience frequent handovers, reducing network capacity. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to strive for fairness in data rates among users and to reduce handovers. To accomplish that, we consider jointly the decisions when to handover and to which BS a user is to be assigned. This is an integer nonlinear program, and by relaxing it, we obtain an upper bound. Further, due to its NP-hardness, we propose a centralized and a multi-agent Deep Q Network (DQN)-based algorithm, which both find near-optimal user-to-BS assignments. We evaluate our Reinforcement Learning-based solutions for networks of different sizes and users with different velocities. We compare our approaches with baselines and show that they outperform them considerably in terms of fairness and radio link failures while being within 95% of the optimum. Our DQN algorithms also reduce the handover rate by up to 93% and avoid ping-pong handovers almost completely.

In this paper, we study binary tree-algorithms that exploit a combination of multi-packet reception (MPR) and successive interference cancellation (SIC), which so far has not been considered in the literature. Specifically, we assume that the receiver is capable of successfully decoding any collision of up to and including$K$concurrent packet transmissions and can perform SIC along the tree. We show a number of novel results for this type of tree algorithms. We first derive the basic performance parameters, which are the expected length of the collision resolution interval and the throughput normalized with$K$, conditioned on the number of contending users. We then analyze their asymptotic behaviour, identifying an oscillatory component that amplifies as$K$increases. In the next step, we derive the maximum stable throughput (MST) for the gated and windowed access assuming Poisson arrivals. We show that for windowed access, the bound on MST normalized with$K$increases with$K$. Finally, we discuss practical issues related to implementation of such scheme, as well as compare it to slotted ALOHA-based schemes that exploit both$K$-MPR and SIC.

In the above article, the authors propose$d$-ary SICTA and derive the expected conditional length of the collision resolution interval, optimal splitting probability and the maximum stable throughput (MST) for$d \geq 2$under stationary ergodic packet arrivals. In this correction, we show that the premise of the analysis for$d > 2$and consequentially the results presented for$d > 2$do not hold.

The introduction of the Service-Based Architecture (SBA) for the 5G Core Networks has drastically changed the way these networks are designed and operated. Aiming for higher flexibility and agility, the adoption of SBA is the first step towards cloud-native deployments of 5G Core. However, the high degree of functional decomposition in SBA has implications in terms of increased inter-NF signaling traffic during the execution of control plane procedures, as well as an increased complexity in orchestrating a system with tight inter-NF dependencies. In this work, we introduce PP5GS as a stateless 5G Core architecture that implements a procedure-based functional decomposition of the 5G Core NFs. We develop Per-Procedure NFs for four different control plane procedures and perform extensive evaluations in a private cloud environment orchestrated with Kubernetes. The results show that PP5GS requires up to 34% and 55% less computing resources compared to the baseline stateful and stateless systems, respectively, while generating at least 40% less signaling traffic. Moreover, complex control plane procedures can complete up to 50% faster. Lastly, the results show that PP5GS is a more feasible architecture in leveraging edge-offloading of 5G Core NFs.

The recent trend to deploy programmable packet processors in cloud environaxsxsments enhances the packet processing capability without losing the flexibility to adapt the functions at runtime. In particular, distributed edge clouds can have a heterogeneous programmable processing substrate made up of different classes of devices: CPUs, NPUs, FPGAs, etc. However, managing the allocation of workloads in such a heterogeneous programmable processing substrate, in particular deciding where to instantiate a certain function, is a non-trivial task with many decisive functional and QoS-related factors. In this paper, we propose a mathematical model for optimizing the embedding of Service Function Chains implemented in P4, while considering the functional and QoS requirements associated with embedding requests, and the various types of processing devices that have different properties in terms of processing delay and supported features. To satisfy delay requirements, the problem formulation utilizes performance models to predict the forwarding latency associated with different candidate embedding options. Furthermore, a greedy solution is proposed to solve the problem in an efficient manner. Finally, a detailed numerical evaluation is conducted to evaluate the formulated model when different workload and infrastructure characteristics are varied and to evaluate the effectiveness of the proposed greedy solution.

Traffic Engineering (TE) mechanisms in data center networks make distributed forwarding decisions based on the global network state. Thus, new TE mechanisms require the design and implementation of effective information exchange and efficient decentralized algorithms to compute forwarding decisions, which is challenging and time-intensive. To automate and simplify this process, we proposeMistill.Mistilldistills the forwarding behavior of TE policies from exemplary forwarding decisions into a Neural Network.Mistilllearns (i) how to encode local state into update messages, (ii) which network devices must exchange updates, and (iii) how to map the exchanged updates into forwarding decisions. We demonstrate the abilities ofMistillby learning three TE policies, verifying their performance in simulations on synthetic and real-world traffic patterns, and by showing that the learned policies generalize to unseen traffic patterns. We implementMistillas a proof-of-concept and show thatMistillreacts on average within 1.3ms to changes in the network.

Software-Defined Radio Access Networks (SD-RANs) foster the concepts of programmability and flexibility, which are vital for next generation cellular networks. However, SD-RANs render network management and orchestration very challenging. Indeed, related works indicate that when thousands of connected devices are spread across the underlying network, SD-RAN approaches with a single controller become deficient and exhibit undesired behavior. Despite this, state-of-the-art research papers lack concrete solutions and evaluations with respect to throughput predictability, where the latter is jeopardized by irregularities in the SD-RAN control plane, specifically in realistic testbeds. In order to overcome the aforementioned issues, in this work, we presentDelphi: a novel platform that provides both analytical and experimental methods to achieve our goal, which is computing the maximum achievable throughput in SD-RAN environments. Analyzing the results provided byDelphi, we can capture the impact of the SD-RAN control plane on throughput. Moreover, we can design important guidelines as to which policy to choose given objectives such as throughput maximization or robustness. Providing a platform for SD-RAN evaluations based on open-source components,Delphienables new avenues for research in the mobile network community. Focusing on FlexRAN SD-RAN controller for our initial results, overall, our findings show that when the number of Base Stations (BSs) and User Equipment (UEs) in the network increases beyond 5000, due to non-timely received control packets for the maximum Channel Quality Indicator (maxCQI) policy the overall throughput decreases by more than 20%.

Multiple solutions for the coexistence of different radio access technologies operating in the same frequency band have been proposed for 5G and WiFi. Most solutions based on spatial division just consider a small amount of radio access points, one link direction, and/or a single radio access technology. As a consequence, the performance of these solutions on realistic wireless network deployments may be poor and difficult to estimate. This paper investigates the serving of multiple users by multiple radio access technologies with the objective of minimizing the interference among network nodes. This is done by jointly optimizing the beams and link directions as well as the transmission powers, so as to ensure fair and near-optimal throughput allocation over time. For this purpose, a generalized beam-gain model for small-scale antenna arrays is proposed. We evaluate our proposed solution for realistic network scenarios in order to show its effectiveness.

Today’s data centers are hosting various applications under the same roof. The diversity among deployed applications leads to a complex traffic mix in Data Center Networks (DCNs). Reconfigurable Data Center Networks (RD-CNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations and congestion control (CC). This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates?; This paper focuses on the Transmission Control Protocol (TCP) and presents a measurement study of TCP variants in RDCNs. The quantitative analysis of the measurements shows that migrated flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.

Software-Defined Networking (SDN) lays the foundation for the operation of future networking applications. The separation of the control plane from the programmable data plane increases the flexibility in network operation. One of the most used languages for describing the packet behavior in the data plane is P4. It allows protocol and hardware independent programming. With the expanding deployment of P4 programmable devices, it is of utmost importance to understand their performance behavior and limitations in order to design a network and provide Quality of Service (QoS) guarantees. One of the most important performance metrics is the packet mean sojourn time in a P4 device. While previous works already modeled the sojourn time in P4 devices with controller feedback, those models were rather simplified and could not capture the system behavior for general cases, resulting in a potential highly inaccurate performance prediction. To bridge this gap, in this paper, we consider the system behavior of P4 devices for the general case, i.e., under general assumptions. To that end, we model the behavior with a queueing network with feedback. As it is impossible to provide closed-form solutions, we consider different approximations for the mean sojourn time. We validate our results against extensive realistic simulations, capturing different behaviors in the data and control planes. Results show that the most accurate approximation in almost all cases is the one in which the queues are decoupled and considered as independent despite the fact that there are dependencies. The level of discrepancy in the worst case does not exceed 18.2% for service times distributions with a coefficient of variation not greater than 1.

Next generation radio access networks (RANs) en-vision softwarization and programmability as the main tools to provide the quality of service (QoS) requirements of emerging applications. Consequently, software-defined radio access networks (SD-RANs) have gained increased traction as a technology to foster network management and alleviate orchestration. While there exist SD-RAN architecture concepts both with single and multiple SD-RAN controllers, currently developed prototypes only include a single controller. Such a design may be sufficient for a low number of managed devices, for instance below 50. When the number of devices increases beyond 300, the controller performance deteriorates. A distributed control plane provides a solution, but renders the management in the control plane complex and incurs additional overhead, for instance control handover. In this way, both single controller and distributed control plane approaches may have a negative impact on a user’s QoS. Yet, proper evaluations are missing and therefore the performance remains unclear. In order to investigate the effect of SD-RAN control plane on the user performance, in this work, we provide an extensive evaluation based on a 5G simulator, compliant with 3GPP standardization, as well as measurements with open-source SD-RAN controllers. Based on our simulator, we are able to demystify the user QoS depending on the control plane design choices. Our results demonstrate that having a distributed control plane with control handovers improves the user performance by at least 20% in terms of throughput, 5x regarding the packet loss ratio and 140% in terms of delay compared to a single controller approach. This confirms that the benefits of multiple controllers surpass the overhead caused by more complicated management.

Automated Web Browsing tools, such as Selenium and headless browsers, are used to collect traffic traces from networked applications, with which statistical models describing the traffic are obtained. However, we show that traces from Selenium and headless browsers have markedly different traffic characteristics than human generated traces, with potential impact on the quality of the obtained models. To overcome this limitation, we propose RLBrowse, an automated web automation framework that imitates human browsing habits by separating web automation from the browser using reinforcement learning. By separating the browser and automation tool, RLBrowse improves on 9 out of the 13 traffic trace features tested. The distribution of packet sizes in a trace improves the most, with a nearly 400 % improvement. We test RLBrowse by collecting a corpus of network packet traces on a set of human-navigated website browsing sessions, and by RLBrowse and Selenium. In the subsequent analysis, we identify key differences in the resulting packet traces.

Programmability and softwarization advocate the emerging era of open-source platforms, which embraced by both industry and academia is foreseen as a vital pillar in the construction of next generation mobile networks. Such a valuable open-source project is OpenAirInterface (OAI), which provides a standard compliant mobile network infrastructure, merely based on general purpose hardware computers. While OAI is nowadays widely used by industry and research institutes in proof-of-concept or commercial wireless testbeds, an analysis of the complex functions within the platform is yet to be performed in a large scale. We believe that further research is required to demystify the capabilities of existing tools and present guidelines that alleviate the enhancement and development of additional features. In this context, in this work we shed light on one of the crucial components of any mobile system, namely resource scheduling, while providing an analysis of the available code and instructions to ease the development of new scheduling algorithms based on OAI. Moreover, we demonstrate a performance evaluation of up to 10 UEs for existing and newly implemented scheduling algorithms. Results show, that the development of additional algorithms in OAI is achievable and the experimental behavior follows the theory. Our implementation and observations can serve as a basis for research in the field, and foster the elaboration of theoretical concepts and emerging 5G solutions in practical testbeds.

In the next-generation wireless networks, energy efficiency (EE) is a fundamental requirement due to the limited battery power and the deployment of various devices in hardly accessible areas. While a plethora of approaches have been proposed to increase users’ EE, there are still many unresolved issues stemming mainly from the limited wireless resources. In this paper, we investigate the energy-efficient resource allocation, taking into account users’ radio resource control (RRC) state. We aim to achieve max-min fairness among users in an uplink orthogonal frequency-division multiple access (OFDMA) system while fulfilling data rate requirements and transmit power constraints. In particular, we avoid waste of the energy through unnecessary state transitions when no network resources are available. We study the impact of the RRC Resume procedure on users’ EE and propose allocating resources while users are in their current RRC Connected or RRC Inactive state. The solution is obtained from a constrained optimization problem, whose output is max-min fair and energy-efficient. To that end, we use generalized fractional programming and the Lagrangian dual decomposition approach to allocate the radio resources and transmission power iteratively. Using extensive realistic simulations with input parameters from measurement data, we compare the results of our approach against benchmark models and show the performance improvements RRC state awareness brings. Specifically, using our approach, the users’ EE increases by at least 10% on average.

Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure the feedback information to be still applicable; thus, the quality-of-service parameter for such applications is the end-to-end latency over the entire loop. By modelling the communication of a feedback loop as a two-hop network, we address the problem of allocating network resources in order to minimize the delay violation probability (DVP), i.e. the probability of the end-to-end latency exceeding a target value. We investigate the influence of the network queue states along the network path on the performance of semi-static and dynamic scheduling policies. The former determine the schedule prior to the transmission of the packet, while the latter benefit from feedback on the queue states as time evolves and reallocate time slots depending on the queue’s evolution. The performance of the proposed policies is evaluated for variations in several system parameters and comparison baselines. Results show that the proposed semi-static policy achieves close-to-optimal DVP and the dynamic policy outperforms the state-of-the-art algorithms.

The architecture of 5G networks relies on partial function centralization to reduce operating costs and allow for improved interference management. Nonetheless, partial centralization results in suboptimal operation if the functional split between centralized and distributed functions is not adapted to the time-varying network conditions. Previous work has tackled the problem of selecting the optimal split for fixed conditions, yet it is unclear whether and when dynamic deployments are superior to static ones. In this work, we provide a comprehensive study on the feasibility and profitability of a 5G network featuring a dynamically-adapted functional split. We formulate the selection of this split as a novel optimization problem that combines performance and operating cost into revenue. Moreover, we base on a dedicated cost model to propose multiple adaptation strategies and evaluate them under a wide range of network designs and conditions. Finally, we show that a dynamic functional split may lead to substantially cost reductions (or revenue increases) with respect to static configurations.

Realizing Service Function Chaining with a micro-service-based architecture results in an increased number of computationally cheap Virtual Network Functions (VNFs). Pinning cheap VNFs to dedicated CPU cores can waste resources since not every VNF fully utilizes its core. Thus, cheap VNFs should share CPU cores to improve resource utilization. However, sharing cores can result in degraded performance due to interference between VNFs, even in mildly loaded scenarios. We proposeD2A, a system that combines Neural Combinatorial Optimization, Machine Learning (ML)-based Digital Twins (DTs), and Game Theory to optimize VNF assignments. Measurements in a testbed show thatD2Aincreases throughput by up to 46% and reduces latency by up to 93%, compared to three baseline algorithms. Using an ML-based DT to model VNF interference increases throughput by up to 11%, and reduces latency by up to 90% compared to an analytical model of the system.

5G radio access network (RAN) slicing envisions a solution to flexibly deploy heterogeneous services as slices sharing the same infrastructure. However, this level of flexibility renders slice isolation challenging, mainly due to the stochastic nature of wireless resources. In the state-of-the-art, RAN slicing algorithm’s efficiency with respect to slice isolation is related to the ability of meeting individual slice requirements. However, mostly an aggregated slice performance guarantee is considered instead of per user guarantees. Hence, state-of-the-art approaches might not always provide the satisfaction of all users within a slice. Indeed, our results demonstrate that if user requirements within a slice are not included in the RAN slicing algorithm, the per user quality-of-service (QoS) may not be fulfilled. In this paper, we investigate the definition of slice isolation as the ability to satisfy individual users’ throughput within slices, in a frequency selective, multi-cell wireless scenario with focus on maximizing slices’ throughput. Our problem is tackled with a Lyapunov optimization approach, which proves to always achieve slice isolation. Our results show that our solution does not only achieve 100% user QoS guarantees compared to 50% achieved in the state-of-the-art, but also doubles the throughput with increasing number of BSs.

Network softwarization is one of the key enablers of the future Internet evolution, also supporting the road from the fifth generation (5G) to the next-generation communication systems, namely 6G, with their main objective of bringing hyper-connected experience to every corner of society.

Languages for programming data planes like P4 sparked a plethora of new applications in the data plane. The dynamic, evolving environment makes it challenging to understand what performance can be expected when running a program in a specific data plane target. However, knowing this is crucial for network operators when upgrading their networks.

The increasing popularity of cloud-native approaches has led to their wide adoption in the telecommunications industry. 5G Core Networks (5GCN) are developed to take advantage of cloud-native design principles, with a high degree of functional decomposition and distributed deployment. This results in implications in inter-Network Function (NF) dependencies that need to be studied. This work focuses on investigating the effect that these dependencies have in how the resources are utilized from the 5GCN NFs. We consider a private cloud environment where a reference 5G Core implementation, namely Free5GC, is deployed and orchestrated with Kubernetes. In addition, a gNB & UE Emulator is developed to allow for the execution of different control plane procedures. Our evaluations highlight the importance of catering for the inter-NF dependencies in achieving efficient resource utilization as well as avoiding deployments where a single NF can bottleneck the entire 5GCN.

Programmable networks come with the promise of logically centralized control, in order to optimize the network's routing behavior. However, until now, controllers are heavily involved in network operations to prevent inconsistencies such as blackholes, loops, and congestion. In this paper, we propose the P4Update framework, based on the network programming language P4, to shift the consistency control and most of the routing update logic out of the overloaded and slow control plane. As such P4Update avoids high and unnecessary control plane delays by mainly scheduling and offloading the update process to the data plane.

Centralization in 5G radio access networks brings two main benefits: reducing cost and improving performance. Although an ideal, fully-centralized architecture would provide minimum cost and maximum performance, actual deployments cannot simultaneously optimize both. Previous research focuses on how to select the functional split of a 5G network to either maximize performance or minimize cost on partially-centralized architectures, without exploring which approach is the most appropriate. In this work, we investigate the trade-off between cost and performance of both approaches, in order to figure out which one is more adequate for real network operators. We provide a comprehensive study under a wide range of network conditions and show that, in general, a performance-maximizing approach is more likely to produce a higher net revenue.

Conditional handover (CHO) has been introduced in 5G to improve mobility robustness, namely, to reduce the number of handover failures by preparing target Base Stations (BSs) in advance and allowing the user to decide when to make a handover. This algorithm constantly prepares and releases BSs, thereby adapting to the fast changing radio condition. A user might make a handover to a distant BS that has a favorable channel only for a short time due to signal fluctuations. This increases the handover rate and might result in a Radio Link Failure (RLF) afterwards. Moreover, the constant preparation and release of BSs leads to an increased exchange of control messages between the user, the serving BS and all target BSs. Hence, there is a need to carefully select the target BSs. Therefore, we propose the Enhanced CHO (ECHO) scheme that uses trajectory prediction to prepare the BSs along the user's path. To achieve this, we also propose a Sequence to Sequence (Seq2Seq) mobility prediction model. ECHO with only one prepared BS (ECHO-1) outperforms CHO with three prepared BSs. ECHO-1 reduces the handover rate by 23 percent and the RLF rate by 77 percent, while also reducing the number of control messages in the network by 69 percent.

The ever-growing wireless networks demand high capacity, have strict latency requirements, and must support diverse communication services. A LiFi-WiFi heterogeneous net-work has proven to be a useful tool to satisfy the growing capacity demand. However, to leverage these co-existing, non-interfering technologies, intelligent resource management schemes have to be developed. To support diverse applications with varying delay and data rate requirements, the resource management scheme should consider the Quality of Service (QoS) while allocating wireless resources. In this work, the downlink wireless resources are allocated to users such that the average network packet delay is minimized. Users that are both capable and not capable of multi-homing are considered and a separate optimization problem is formulated for each case. These problems are then solved using a global Branch and Bound-based solver and a genetic algorithm-based Metaheuristic is also proposed. The algorithms are then evaluated with simulations and the results show that the average network packet delay is significantly lowered and each user's strict QoS requirements are satisfied even in a network with heavy traffic flow.

Cyber-physical systems (CPS) classify the set of applications where a physical, real-time process is monitored and controlled over a communication network. In CPS, providing fresh information is essential to satisfy the requirements imposed by time-critical applications. In order to quantify information freshness, age of information (AoI) has been proposed and employed as a metric for cross-layer design. In contrast to the vast majority of AoI-research, there have been a few attempts to measure AoI in real deployment scenarios. However, those contain either unalterable communication stack or are not publicly available for possible extensions. In this work, we present an open-source, experimental framework that is using software-defined radios for wireless communication. Our implementation contains centralized resource allocation using beacon packets and various conventional packet management policies such as first come first serve and last come first serve. In a case study with multiple inverted pendulums sharing a wireless channel, we show how the communication stack can be tailored to keep the information fresh in the network. We present the performance of feedback control loops in relation to AoI and show the benefit of keeping the information fresh on realistic CPS applications.

Network edge environments like in-flight or in-train communications utilize satellite-terrestrial integrated networks. These networks however suffer from limited backhaul and cache resources, leading to sustainability issues due to increasing traffic demands. The problem becomes more challenging for 5G ecosystems, where applications have distinct requirements, rendering the management and orchestration of conventional satellite-terrestrial networks harder. Therefore, software-defined networking and edge network slicing are envisioned to enhance resource management and increase flexibility of resource allocation. However, the complexity of management and orchestration increases in cases where service providers, allocated to a slice, do not share information about their users with the infrastructure providers, due to privacy or other concerns. To incorporate the aspect of slice collaboration, we define network slices with respect to their willingness of sharing user traffic statistics with the infrastructure provider. Taking in-flight entertainment and connectivity services (IFECS) as an interesting 5G use-case, we introduce a system model mimicking the practical deployment of slicing for aircrafts using satellites. We propose a mixed integer non linear program that aims at maximizing the number of slices served. Utilizing our model we evaluate the deployment cost of slices with respect to cache and backhaul resources. Our results show that uncooperative slices have a lower selection probability. Nonetheless, we demonstrate that if the slice cost is paid by slice owners, uncooperative slices increase their chances of being served by 33%. Overall, cooperative slicing can revolutionize the IFECS system as it accommodates 200% more slices compared to uncooperative slicing.

Communication networks are evolving towards a more adaptive and reconfigurable nature due to the evergrowing demands they face. A framework for measuring network flexibility has been proposed recently, but the cost of rendering communication networks more flexible has not yet been mathematically modeled. As new technologies such as software-defined networking (SDN), network function virtualization (NFV), or network virtualization (NV) emerge to provide network flexibility, a way to estimate and compare the cost of different implementation options is needed. In this paper, we present a comprehensive model of the cost of a flexible network that takes into account its transient and stationary phases. This allows network researchers and operators to not only qualitatively argue about their new flexible network solutions, but also to analyze their cost for the first time in a quantitative way.

To provide the predictability required by emerging applications, operators typically rely on policing and/or shaping at the edge to ensure that tenants do not use excess bandwidth that was not accounted for. One of the promises of 6G is to deploy applications with strict predictability requirements across subnets and even over the Internet, where policing cannot be implemented in the end hosts. This paper presents an empirical study of the ability of modern programmable network devices to implement predictable traffic policing in the network. We find out that none of the five investigated hardware switches can provide accurate traffic policing, a key requirement for providing predictable service to applications. We observe that the switches let applications send more than what they should be allowed to, reaching up to 60% and 100% relative error for the rate and burst parameters. We further uncover the fact that switches cannot police arbitrarily low bursts, e.g., not less than 13 kilobit for one of our switches. We investigate how such limitations impact the performance of state-of-the-art solutions for predictable latency such as Chameleon. We observe that, for ensuring its predictable guarantees, Chameleon rejects around 50% of the tenants it could accommodate if switches were perfect, hence decreasing by the same ratio the revenue for the operator. Based on these observations, we discuss solutions toward more accurate and predictable policing in wide-area networks.

Networking devices with programmable data planes, such as P4 programmable devices, are gaining more popularity because of the flexibility they provide in describing the packet processing behavior. Despite this attained flexibility, the performance of these devices can be the Achilles' heel in case the desired performance level is not met. To this end, we evaluate the performance of three state-of-the-art P4 devices focusing on the following properties: (i) the device's processing latency as a function of a scaled number of flows; (ii) the device's response time in reaction to control plane commands. The scalability analysis shows that different devices have different limits on the maximum number of flows they can support. On the other hand, the device's response time to control plane commands is found to be in milliseconds, which is three orders of magnitude larger when compared to the measured data plane's packet processing latency.

A LiFi-RF heterogeneous network can provide additional capacity to standalone wireless technologies due to their non-interfering nature. However, due to the properties of the short-range LiFi channel, the network is prone to transient channel variations that result in frequent, unnecessary handovers. This handover process creates an overhead and can result in the loss of connection. To ensure a stable connection for all users, a low complexity resource allocation algorithm, that considers the loss due to handovers, is proposed to minimize the number of handovers. This algorithmic approach is evaluated with simulations. For scenarios with unavoidable handovers, a system approach to manage vertical handovers is proposed to minimize the vertical handoff overhead and to offer a seamless interface switch, thereby resulting in a stable network. This protocol is implemented in hardware and the results show a negligible overhead.

In order to deliver the high data rates promised for 5G networks, mobile base stations need to be deployed in dense layouts. This results in increased inter-cell interference, which can be mitigated by leveraging centralized architectures in radio access networks. Nonetheless, centralizing all the processing requires prohibitively high link capacities for the fronthaul network connecting centralized and distributed units. In contrast, a static, partially-centralized architecture yields poor performance as it fails to adapt to instantaneous interference situations. In this work, we show that a dynamically centralized architecture enables drastic interference reductions even when using a very limited fronthaul network. We propose multiple algorithms to find the optimal centralization option and evaluate their performance on operator-grade hardware. In addition, owing to the dynamicity of the problem being solved, we provide a framework to decide on the best algorithm based on the trade-off between performance, cost, and adaptation time.

Providing predictable performance to tenants is mission critical for network hypervisors. As a hypervisor acts as an intermediary between tenants controllers and the physical infrastructure, its resources (e.g., CPU, RAM) should be provisioned and allocated carefully. Initially, we demonstrate that state-of-the-art CPU prediction approaches are not suitable for provisioning network hypervisor CPU resources, since they predict only the mean CPU utilization. However, provisioning the resources with a mean value can significantly degrade the forwarding performance of a network hypervisor. In this article, we present a novel approach which provisions network hypervisor CPU resources efficiently, while avoiding performance degradation. We take three steps to achieve our goal:(i)conducting a profound measurement campaign to determine what is the minimum amount of CPU resources that needs to be allocated to a network hypervisor in order to have no performance degradation;(ii)revealing the key properties of virtual networks that affect the CPU utilization;(iii)designing a precise CPU prediction model. Using randomly generated virtual networks and arbitrary physical topologies, we show that our prediction model exhibits an average relative error of around 4%. Further, our evaluations indicate that provisioning the CPU resources of a network hypervisor based on the proposed prediction model does not degrade the hypervisor forwarding performance. Utilizing our approach, network operators can minimize their resources consumption while still providing predictable and undegraded forwarding performance to tenants.

Data plane programmability brings network flexibility to a new level. However, it introduces the complexity of the data path's program as a new factor that influences packet forwarding latency and thus devices' performance. Accurate identification of the relation between data path complexity and packet forwarding latency enables the design and management of networks with predictable performance. In this article, we leverage the characteristics of P4 programming language to provide a method for estimating the packet forwarding latency as a function of the data path program. We analyze the impact of different P4 constructs on packet processing latency for three state-of-the-art P4 devices: Netronome SmartNIC, NetFPGA-SUME, and T4P4S DPDK-based software switch. Besides comparing the performance of these three targets, we use the derived results to propose a method for estimating the average packet latency, at compilation time, of arbitrary P4-based network functions implemented using the surveyed P4 constructs. The proposed method is finally validated using a set of realistic network functions, which shows that our method estimates the average packet latency with sub-microsecond precision.

The Softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and data-center providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on the management of softwarized networks.

Network programmability also sneaked into the mobile world leading to the emergence of Software-Defined Radio Access Network (SD-RAN) architectures. Interestingly, while only a small number of prototype architectures exist for SD-RAN, their performance evaluations are unfortunately also limited. Recent evaluations are carried out for small network dimensions of up to 50 devices, while emerging 5G/6G networks envision numbers of devices beyond 5000. Although 5G/6G applications are more stringent with respect to latency guarantees, performance evaluations of such low scale remain questionable. To fill this void, this paper presentsMARC: a novel benchmarking tool for SD-RAN architectures and their controllers. We useMARCto measure, analyze and identify performance implications for two state-of-the-art open source SD-RAN solutions:FlexRANand5G-EmPOWER. We perceive results for monitoring application scenarios considering fully centralized control. For this setting, our findings show that the proposed architectures with a single SD-RAN controller are not scalable and can even lead to unpredictable network operations. Using our tool and based on our insights, we provide and implement design guidelines for the internal working behavior of the existing controllers.

Service function chains (SFCs) are an ordered set of virtual network functions (VNFs) which can realize a specific network service. Enabled by virtualization technologies, these VNFs are hosted on physical machines (PMs), and interconnected by network switches. In today networks, these resources are usually under-utilized and/or over-provisioned, resulting in power-inefficient deployments. To improve power-efficiency, SFCs should be deployed utilizing the minimum number of PMs and network equipment, which are not concomitant. Considering the existing PM and switch power consumption models and their resource constraints, we formulate the power-aware and delay-constrained joint VNF placement and routing (PD-VPR) problem as an Integer Linear Program (ILP). Due to the NP-completeness of the problem, we proposeHolu, a fast heuristic framework that efficiently solves the PD-VPR problem in an online manner. Specifically,Holudecomposes the PD-VPR into two sub-problems and solve them sequentially:i)aVNF placementproblem that consists of mapping the VNFs to PMs using a centrality-based ranking method, andii)aroutingproblem that efficiently splits the delay budget between consecutive VNFs of the SFC, and finds a Delay-Constrained Least-Cost (DCLC) shortest-path through the selected PMs (hosting VNFs) using the Lagrange Relaxation based Aggregated Cost (LARAC) algorithm. Our simulation results indicate thatHoluoutperforms the state-of-the-art algorithms in terms of total power consumption and acceptance rate by 24.7% and 31%, respectively.

The recent trend of network softwarization suggests a radical shift in the implementation of traditional network intelligence. In Software Defined Networking (SDN), for instance, the control plane functions of forwarding devices are outsourced to the controller. Softwarized network components are expected to provide uninterrupted service during long periods of time, which makes them prone to the effects ofsoftware aging, a phenomena that has been observed in operational software systems where the failure rate increases or the performance of the software degrades with the elapsed time since the last restart. The effects of software aging in operational networks are typically mitigated bysoftware rejuvenation, i.e., planned restarts cleaning the internal system state in order to prevent or postpone aging-related failures. This article presentsARES, a three-step methodological framework for the management of the effects of software aging in softwarized networks, applied to the case study of open source SDN orchestration platforms. Using ARES, we demonstrate that software aging is a systematic problem that cannot be neglected in network orchestration systems. It stems not only from aging-related bugs and natural aging due to fragmentation, but also from design choices, e.g., when implementing distributed systems. Measurements for Open Network Operating System (ONOS) and OpenDaylight (ODL) demonstrate how “simple” and common networking tasks let network performance degrade rapidly and even lead to crashes: for instance, adding and removing 300 intents per second in ONOS significantly increases the response time by 50% per day and depletes the memory at the rate of 18GB per day. Moreover, we demonstrate a first rejuvenation approach that can mitigate the effects of aging in ONOS.

Age-of-information (AoI) is a metric quantifying information freshness at the receiver. Since AoI combines packet generation frequency, packet loss, and delay into a single metric, it has received a lot of research attention as an interface between communication network and application. In this work, we apply AoI to the problem of wireless scheduling for multi-loop networked control systems (NCS), i.e., feedback control loops closed over a shared wireless network. We model the scheduling problem as a Markov decision process (MDP) with AoI as its observable states and derive a relation of control system error and AoI. We further derive a stationary scheduling policy to minimize control error over an infinite horizon. We show that our scheduler outperforms the state-of-the-art scheduling policies for NCS. To the best of our knowledge, this is the first work proposing an AoI-based wireless scheduling policy that minimizes the control error over an infinite horizon for multi-loop NCS.

The current cellular standardization for 5G is working towards wireless advances to enable further productivity for industrial automation. However, this development will take several years. Meanwhile, the capabilities of the currently available standards should be pushed to their limits. To this end, in this work, we present results from the first inter-slot successive interference cancellation testbed using commercial off the shelf IEEE 802.15.4 sensors. Through our implementation, we have measured a throughput of 0.72 packets per slot which doubles the currently used contention-based access, Slotted ALOHA, with a limit of 0.36 packets per slot. The hardware effects of the boards, which degrade the successive interference cancellation performance from the theoretical limit of 1 packet per slot, are modeled and validated through measurements. We also propose a model that can be used to calculate the expected successive interference cancellation throughput with the specific hardware available in a factory. Furthermore, our proposed model should replace the perfect physical layer assumptions for researchers to design new MAC algorithms taking practical limitations into account.

The evolution of the Internet of Things accelerated the development of Cyber-Physical Systems. Among them, Networked Control Systems (NCS) gained notable attention thanks to their application to industrial operations. Experimental NCS require expertise from control, computation, and communication disciplines. This requirement, together with the fragmentation of implementation platforms and experimental investigations, represents a challenge for the reproducibility and comparison of research results. In this paper, we tackle this problem by proposing a novel NCS benchmarking methodology that aids the reproducibility of NCS experiments. Relying on a novel approach to model the architectural elements and the delays of NCS, the methodology defines the experiment parameters and the relevant Key Performance Indicators (KPIs) that need to be observed during its execution. Furthermore, we detail the implementation of the first reproducible benchmarking platform for NCS. The proposed platform is open-source and designed to be easily reproducible and extensible by anyone. Finally, we replicate and evaluate the platform following the proposed NCS benchmarking methodology. The experimental results evaluate and compare the KPIs during the execution of the platform in different benchmarking scenarios, proving the validity of the proposed benchmarking methodology.

Cyber-Physical Systems (CPS) are widely spreading thanks to fast-paced technological breakthroughs of microcontrollers and communication networks. Among them, Networked Control Systems (NCS) gained notable attention thanks to their application in industrial operations. In NCS, the interconnection of a control logic with sensors and actuators used to steer a physical system occurs over a communication network. Despite large research interest on NCS, the reproducibility and comparison of experimental results are difficult to achieve. This is caused by the lack of well-established models and methodologies that combine the theoretical and practical aspects of NCS. We tackle this problem by proposing and demonstrating NCSbench: the first open-source reproducible benchmarking platform for NCS. NCSbench enables the benchmarking of research experiments using a networked two-wheeled inverted pendulum robot. For each benchmarking experiment, a set of values is measured and used to quantify the key performance indicators (KPIs) of the NCS. In our demonstration, we visualize in real-time the evolution of the benchmarking KPIs on a web-based Graphical User Interface.

This paper presents Chameleon, a cloud network providing both predictable latency and high utilization, typically two conflicting goals, especially in multi-tenant datacenters. Chameleon exploits routing flexibilities available in modern communication networks to dynamically adapt toward the demand, and uses network calculus principles along individual paths. More specifically, Chameleon employs source routing on the "queue-level topology", a network abstraction that accounts for the current states of the network queues and, hence, the different delays of different paths. Chameleon is based on a simple greedy algorithm and can be deployed at the edge; it does not require any modifications of network devices. We implement and evaluate Chameleon in simulations and a real testbed. Compared to state-of-the-art, we find that Chameleon can admit and embed significantly, i.e., up to 15 times more flows, improving network utilization while meeting strict latency guarantees.

The architecture of the radio access network (RAN) in 5G features a functional split between centralized and distributed units, which can be leveraged to reduce inter-cell interference. Recent work proposes to dynamically adapt this split in accordance with the instantaneous interference situation experienced by all users. However, it is unclear whether performing this flexible adaptation is actually feasible, since the interference situation changes continuously as users move. In this work, we investigate the impact of mobility on the problem of dynamically selecting the optimal functional split. We employ a mobility simulator based on real street layouts and trace-derived traffic patterns to generate continuously varying interference situations. Then, we analyze how frequently the optimal functional split changes and how much the performance of previous splits differs from the new, optimal one. The results allow us to estimate the time required for a viable flexible functional split adaptation.

Age of information (AoI) measures information freshness at the receiver. AoI may provide insights into quality of service in communication systems. For this reason, it has been used as a cross-layer metric for wireless communication protocols. In this work, we employ AoI to calculate penalty functions for a centralized resource scheduling problem. We consider a single wireless link shared by multiple, heterogeneous control systems where each sub-system has a time-varying packet loss probability. Sub-systems are competing for network resources to improve the accuracy of their remote estimation process. In order to cope with the dynamically changing conditions of the wireless link, we define a finite horizon age-penalty minimization problem and propose a scheduler that takes optimal decisions by looking H slots into the future. The proposed algorithm has a worst-case complexity that grows exponentially with H. However, by narrowing down our search space within the constrained set of actions, we are able to decrease the complexity significantly without losing optimality. On the contrary, we show by simulations that the benefit of increasing H w.r.t. remote state estimation performance diminishes after a certain H value.

In this paper, we analyze binary-tree algorithms in a setup in which the receiver can perform multi-packet reception (MPR) of up to and including K packets simultaneously. The analysis addresses both traffic-independent performance as well as performance under Poisson arrivals. For the former case, we show that the throughput, when normalized with respect to the assumed linear increase in resources required to achieve K-MPR capability, tends to the same value that holds for the single-reception setup. However, when coupled with Poisson arrivals in the windowed access scheme, the normalized throughput increases with K, and we present evidence that it asymptotically tends to 1. We also provide performance results for the modified tree algorithm with K-MPR in the clipped access scheme. To the best of our knowledge, this is the first paper that provides an analytical treatment and a number of fundamental insights in the performance of tree-algorithms with MPR.

Today, on-board passengers desire to have in-flight services such as Voice-over-IP (VoIP) and video streaming. These services are usually hosted by geographically distributed Data Centers (DCs) that are built/rented by the airline companies. Flights can be connected to these DCs using two types of Air-to-Ground (A2G) communication alternatives: i) satellite (SC), and ii) Direct-Air-to-Ground connections (DA2G). These two options are different in terms of propagation delay, capacity, and availability. Focusing on reducing the delay of the inflight services, each airplane should be assigned to a nearby DC. However, due to the mobility of flights, a permanent DC assignment might not lead to an acceptable service delay for the flight duration. Therefore, the flight needs to be reassigned to another DC (reconfiguration) along its route, which comes with a cost. The real challenge in this work is to find the best assignments of each airplane to DC(s) and determine the required reconfigurations such that the sum of routing and reconfiguration delay is minimized. We model this problem as a Multi-Period Generalized Assignment Problem (MPGAP) and formulate it as an Integer Linear Programming (ILP) optimization model. To overcome the scalability issues of the ILP, we propose Figo, a flight control framework that solves the MPGAP problem using deep Q-learning. Considering a realistic European-based Space-Air-Ground-Integrated Network (SAGIN) and a real set of flights, we compare the performance of Figo against the optimal. The results indicate that Figo can achieve 7% optimality gap in the worst case, while reducing the runtime from hours to seconds.

In a so-called `Smart Factory', sensors, actuators, and a processing logic are interconnected via wireless communication. A popular class of industrial processes is Networked Control Systems (NCS), where the sensor, controller, and actuator of a control system are distributed over a network. Wireless brings several benefits to NCS but affects their performance. This aspect is particularly critical, as NCS pose stringent delay and reliability requirements to data packets in order to fulfil a desired Quality of Control (QoC). Industrial Wireless Sensor Networks (IWSN) is a candidate communication technology to haul NCS traffic. IWSN, however, suffer from packet loss caused by the harsh industrial environment. The characterization of the impact of delay and packet loss on the QoC of NCS is a challenging task, as it requires the analysis of mutually dependent random processes. We tackle this investigation deriving a delay-reliability model for IWSN based on the Loop Success Probability, a metric that associates the network performance to the QoC of the NCS. Initially, the effect of Loop Success Probability on QoC is evaluated, then, it is mathematically related to the end-to-end delays of IWSN packets. The model provides a connection between IWSN parameters and QoC and is used to define their operating regions. Via measurements of an IWSN testbed and a simulated NCS, we prove the validity of the proposed model.

In future industrial scenarios, Wireless Sensor Networks (WSN) are envisioned to support the traffic of Networked Control Systems (NCS). WirelessHART is a prevalent WSN protocol that uses the Time Slotted Channel Hopping (TSCH) medium access to cope with the delay and reliability requirements of NCS in the harsh industrial environment. In TSCH, time slots and frequencies can be scheduled by a network coordinator to provide Quality of Service (QoS). In contrast to previous works that consider the end-to-end delay requirement of a flow of packets, we focus on a finite sequence of time-critical packets. These packets may belong to a time-critical message whose latency could significantly impact the NCS. Given an end-to-end delay deadline, our objective is to minimize the Delay Violation Probability (DVP) for a finite sequence of packets by dynamically scheduling the time slots in each frame. This is a challenging task as DVP depends on the instantaneous state of the network and requires its transient analysis. In this work, we model the wireless NCS as a two-queue lossy wireless network and propose the first transient analysis of DVP for a finite sequence of time-critical packets. Noting that DVP cannot be directly used for dynamic resource allocation, we propose a heuristic algorithm by relating DVP with the network's throughput. The proposed heuristic maximizes the expected throughput, is computed by solving a finite-horizon Markov Decision Process (MDP), and can be implemented at the network coordinator. Using simulation we demonstrate that the MDP-based heuristic achieves lower DVP compared to the classical MaxWeight and Weighted-Fair Queuing.

We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V) communication in the absence of a base station. Each vehicle autonomously selects transmission resources from a pool of shared resources to disseminate Cooperative Awareness Messages (CAMs). This is a consensus problem where each vehicle has to select a unique resource. The problem becomes more challenging when---due to mobility---the number of vehicles in vicinity of each other is changing dynamically. In a congested scenario, allocation of unique resources for each vehicle becomes infeasible and a congested resource allocation strategy has to be developed. The standardized approach in 5G, namely semi-persistent scheduling (SPS) suffers from effects caused by spatial distribution of the vehicles. In our approach, we turn this into an advantage. We propose a novel Distributed Resource Allocation mechanism using multi-agent reinforcement Learning (DIRAL) which builds on a unique state representation. One challenging issue is to cope with the non-stationarity introduced by concurrently learning agents which causes convergence problems in multi-agent learning systems. We aimed to tackle non-stationarity with unique state representation. Specifically, we deploy view-based positional distribution as a state representation to tackle non-stationarity and perform complex joint behavior in a distributed fashion. Our results showed that DIRAL improves PRR by 20% compared to SPS in challenging congested scenarios.

Facing fast changing network traffic, Software Defined Networking (SDN) endows the capability of efficient traffic forwarding and control plane resource management which results in flexibility, in comparison to networking with legacy rigid hardware. Meanwhile, flexibility has become an implicit target of many novel network algorithms and designs. This paper answers an interesting research question: "Can we optimize the flexibility as an objectiveƒ" We study the impact of Data Centers (DCs) location on the flexibility of dynamic control plane. The flexibility is revealed when the control plane can adapt itself with controller migration and switch re-assignment in a timely manner for a new group of flows to satisfy the requirements of flow setup. We propose a model, named FLEXDC, for static DC placement and dynamic controller placement to optimize the control plane’s flexibility. We also design heuristics to speed up the decision process. Our simulation over real network topology with synthetic flows shows the improved flexibility of the dynamic control plane. Furthermore, we can save up to 2 DCs while achieving the same flexibility, compared with a naive approach.

In the field of networking research, increased flexibility of new system architecture proposals, protocols, or algorithms is often stated to be a competitive advantage over its existing counterparts. However, this advantage is usually claimed only on an argumentative level and neither formally supported nor thoroughly investigated due to the lack of a unified flexibility framework. As we will show in this paper, the flexibility achieved by a system implementation can be measured, which consequently can be used to make different networking solutions quantitatively comparable with each other. The idea behind our mathematical model is to relate network flexibility to the achievable subset of the set of all possible demand changes, and to use measure theory to quantify it. As increased flexibility might come with additional system complexity and cost, our framework provides a cost model which measures how expensive it is to operate a flexible system. The introduced flexibility framework contains different normalization strategies to provide intuitive meaning to the network flexibility value as well, and also provides guidelines for generating demand changes with (non-)uniform demand utilities. Finally, our network flexibility framework is applied on two different use-cases, and the benefits of a quantitative flexibility analysis compared to pure intuitive arguments are demonstrated.

https://www.youtube.com/watch?v=SQmgSOi5oos

With the move from traditional hardware appliance based network functions to Network Function Virtualization, software development is decoupled from the hardware. However, as a network function is no longer optimized for hardware, beneficial features of networking hardware may not be used any more. Solutions such as SDN or NIC offloading aim to overcome this antipodes by integrating networking hardware into the packet processing pipelines. On the one hand, offloading traffic of network functions to hardware can increase throughput and reduce resource consumption. On the other hand, the number of parallel flows in a network can be very high, exhausting the capacity of the tables of the networking hardware and force the system to fall back to software processing. Fortunately, it is known that a large portion of the flows in the Internet are mice flows, whereas the majority of the traffic is constituted by elephant flows. If the elephant flows can be detected efficiently, the hardware tables can be used more efficiently as a larger share of the traffic can be offloaded. We introduce a machine learning based approach that takes its decision with the first packet of a flow. A fundamentally different approach is using packet sampling for the offloading decision. We are evaluating both approaches in terms of complexity, offloaded share of the traffic and table occupation. The results show that a machine learning based offloading decision is possible with the first packet. The sampling approach only reaches a comparable performance at very high sampling rates.

In the context of the 5G ecosystem, the integration between the terrestrial and satellite networks is envisioned as a potential approach to further enhance the network capabilities. In light of this integration, the satellite community is revisiting its role in the next generation 5G networks. Emerging technologies such as Software-Defined Networking (SDN) which rely on programmable and reconfigurable concepts, are foreseen to play a major role in this regard. Therefore, an interesting research topic is the introduction of management architecture solutions for future satellite networks driven by means of SDN. This anticipates the separation of the data layer from the control layer of the traditional satellite networks, where the control logic is placed on programmable SDN controllers within traditional satellite devices. While a centralized control layer promises delay reductions, it introduces additional overheads due to reconfiguration and migration costs. In this paper, we propose a method to quantify the overhead imposed on the network by the aforementioned parameters while investigating the use-case scenario of an SDN-enabled satellite space segment. We make use of an optimal controller placement and satellite-to-controller assignment which minimizes the average flow setup time with respect to varying traffic demands. Furthermore, we provide insights on the network performance with respect to the migration and reconfiguration cost for our proposed SDN-enabled architecture. Finally, we compare our proposed space segment SDN-enabled architecture with alternative solutions in the state-of-the-art given the aforementioned performance metrics.

In Software Defined Networking (SDN), network programmability is enabled through a logically centralized control plane. Production networks deploy multiple controllers for scalability and reliability reasons, which in turn rely on distributed consensus protocols to operate in a logically centralized manner. However, bugs in distributed control plane can have disastrous effects on the data plane, e.g., losing traffic by installing paths containing blackholes. In this paper we study the prevalence of issues in state-of-the-art distributed frameworks in SDN, by analyzing 500+ issues reported in two of the largest open source SDN controller platforms: Open Network Operating System (ONOS) and OpenDaylight (ODL), during the period between 2014-2019. We identify system vulnerabilities, localize dependability bottlenecks, and provide stochastic models for a holistic assessment of system dependability.

Direct communication among user equipments, called Device-to-Device (D2D) communication, is envisioned in academia and industry to enhance future cellular networks for increased spectral efficiency. Mode selection considers the question which links should be offloaded to use direct communication mode and is one of the core questions in D2D research. In this work, we ask the question which information needs to be exchanged between a remote mode selection controller and Medium Access Control (MAC) layer to allow optimal operation without the burden of a full-fledged cross-layer optimization. We formulate mode selection as utility maximization with cross-layer decomposition and solve it using the Generalized Benders Decomposition technique. The result is a nested offloading structure, in which the network controller regularly updates its mode selection decision while in the meantime, the network operates in a particular fashion, gathering information to enable better future mode selection decisions. We show that the proposed structure allows achieving an optimal mode selection configuration within a finite number of optimization steps for any non-negative utility function. Due to our general network model, the solution is applicable to a variety of system set-ups, including overlay and underlay D2D, with and without power control and single or multi-antenna systems.

To meet their increasingly stringent dependability requirements, communication networks need to be predictable, both in terms of correctness and performance. In principle, Software-Defined Networks (SDN) enable such more predictable networks, however, these networks still depend the underlying switches. This paper presents an empirical study of the predictability of SDN switches. Our extensive benchmarking of seven hardware OpenFlow switches from four different manufacturers raises several concerns regarding the dependability of these switches. We uncover several incorrect and unpredictable behaviors and performance issues. In particular, we identify unpredictable behaviors related to the management of flows and buffers, and observe that existing quality-of-service mechanisms, such as priority queuing, introduce unexpected overheads. The latter, in turn, can lead to violations of latency guarantees. Based on our insights, we discuss first solutions toward more predictable architectures.

P4 programmable data planes are becoming more popular due to the flexibility they provide in describing the packet processing pipeline. P4 successfully abstracts the processing pipeline of data planes using a limited set of constructs. The performance variation as a function of the configured P4 pipeline is an important aspect that should be studied. Analyzing the impact of different P4 constructs on packet latency helps in understanding the overall performance of P4 programmable devices. In this paper, we analyze the impact of a basic set of P4 constructs on packet processing latency to derive the influential parameters. We use the derived results to propose a method for estimating the packet latency of P4-based network functions implemented using the surveyed P4 constructs. Finally, we validate the accuracy of the proposed method by applying it to realistic network functions.

Along with many other novel features, the fifth generation of mobile networks (5G) aims at highly flexible and dynamic network management, as well as reduced cost for operators. In order to enable both features, rapid and efficient adaptation to environmental changes is needed. This requires a complete knowledge of the characteristics of the user traffic at all time scales, but state-of-the-art research clearly differentiates between large-scale and small-scale traffic behavior. In this work, we propose a traffic model that connects large-scale and small-scale phenomena. We show that the standard small-scale models may produce inaccurate results in case of network congestion. We propose a strategy to mitigate this problem and evaluate it through simulations.

The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance camera(s) in the communication environment and utilizes Convolutional Neural Networks (CNNs) to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors' best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.

A predictable network performance is mission critical for many applications and yet hard to provide due to difficulties in modeling the behavior of the increasingly complex network equipment. This paper studies the problem of providing deterministic latency guarantees in small networks based on low-capacity hardware (e.g., in-cabin and industrial networks): such networks are of increasing importance, need to meet stringent performance requirements, but have hardly been explored so far. Our main contribution is the design, implementation, and evaluation of Loko, a system which provides predictable latency guarantees in programmable networks using low-cost hardware. Loko relies on a novel measurement-based methodology and uses deterministic network calculus to derive a reliable performance model of a given switch. To this end, we also show that state-of-the-art models in the literature like QJump and Silo fall short to model the behavior of such switches, due to incorrect architectural and performance assumptions. As a case study, we implement Loko for the Zodiac FX switch. Our experiments are encouraging: we find that the derived models are indeed accurate, allowing Loko to provide deterministic end-to-end guarantees with low-cost programmable devices.

The 3rd Generation Partnership Project (3GPP) proposes a centralized architecture for the 5G radio access network (RAN) in order to reduce costs and mitigate inter-cell interference, which helps to increase user data rates. However, the limited capacity of current fronthaul networks renders it impossible for many RANs to be fully centralized. Instead, the operators can opt for a partially centralized architecture, in which only some of the functions of the RAN's processing chain are centralized. Previous work has tackled the optimal selection of these functions in a static or semi- static manner. In this paper, we present a 5G RAN that is able to dynamically adapt the subset of centralized functions to maximize data rates at runtime. We analyze the dynamics of a dense 5G RAN to derive a maximum convergence time for the selection algorithms and show that a dynamic functional split significantly improves data rates with respect to statically centralized solutions.

In the 5G radio access network (RAN), the functions of the next-generation eNodeB (gNodeB) are split into a centralized and a distributed unit. Depending on how the split is performed, the amount of traffic generated between the units can be too high to be supported by the current infrastructure. Therefore, a careful characterization of this traffic is needed for every split option. Among the wide array of options, the MAC-PHY split proves to be both promising, as a balanced trade-off between centralization and decentralization, and difficult to characterize, due to the high amount of low level interactions between MAC and PHY layers. Indeed, the MAC-PHY split is frequently considered in the literature as a possible implementation option for the 5G RAN, yet there is no detailed study about the capacity it requires. This paper remedies that by offering a comprehensive analysis of the downlink traffic of a MAC-PHY split for 5G networks. This analysis is backed with both simulative data and measurements from a physical implementation.

Byzantine Fault Tolerance (BFT) enables correct operation of distributed, i.e., replicated applications in the face of malicious take-over and faulty/buggy individual instances. Recently, BFT designs have gained traction in the context of Software Defined Networking (SDN). In SDN, controller replicas are distributed and their state replicated for high availability purposes. Malicious controller replicas, however, may destabilize the control plane and manipulate the data plane, thus motivating the BFT requirement. Nonetheless, deploying BFT in practice comes at a disadvantage of increased traffic load stemming from replicated controllers, as well as a requirement for proprietary switch functionalities, thus putting strain on switches' control plane where particular BFT actions must be executed in software. P4BFT leverages an optimal strategy to decrease the total amount of messages transmitted to switches that are the configuration targets of SDN controllers. It does so by means of message comparison and deduction of correct messages in the determined optimal locations in the data plane. In terms of the incurred control plane load, our P4-based data plane extensions outperform the existing solutions by ~33.2% and ~40.2% on average, in random 128-switch and Fat-Tree/Internet2 topologies, respectively. To validate the correctness and performance gains of P4BFT, we deploy bmv2 and Netronome Agilio SmartNIC-based topologies. The advantages of P4BFT can thus be reproduced both with software switches and "commodity" P4-enabled hardware. A hardware- accelerated controller packet comparison procedure results in an average 96.4% decrease in processing delay per request compared to existing software approaches.

The performance of cloud computing depends heavily on the networking infrastructure, which carries the data traffic between the VMs, as well as the essential control and management traffic. Meanwhile, networks in data centers are becoming softwarized with the Software Defined Networking (SDN) paradigm to fuel their programmability and flexibility. However, since SDN switches need to contact the controller frequently for their configuration and flow rules setup, the overall data traffic forwarding performance can suffer, if the control and management plane gets congested. In order to proactively avoid such congestion, we need to study the involved traffic in detail. In this work, we perform an elaborate traffic evaluation on a real OpenStack cloud deployment and evaluate the types of exchanged messages in the control and management plane and their respective traffic volume. The evaluation reveals that the control and management traffic scales with the number of VMs and the VM-related events, and therefore provides guidelines for planning and operating the cloud networking infrastructure.

Wireless Avionics Intra-Communications (WAIC) is an emerging standard to migrate regulatory and safety-related applications from wired networks to wireless means in next generation aircraft. Removing most of the cables from the fuselage will benefit to the overall efficiency of aircraft. On the other hand, it is a difficult task to achieve the Quality of Service (QoS) performance of wired networks by wireless technologies. Also, the Radio Altimeter (RA) interference in the same spectrum is another obstacle to overcome. Current Long Term Evolution (LTE) and emerging Fifth Generation (5G) technologies advertise promising specifications that can meet the requirements of WAIC networks. Therefore, the goal of this study is to evaluate the feasibility of supporting high data rate WAIC applications with LTE/5G by means of analytical and experimental studies. Measurements conducted in unoccupied mockup cabin and outside environment show that at least one cellular technology can meet the requirements of high data rate WAIC classes in the absence of RA interference. In coupling scenarios, robustness strategies should be considered to improve the network resilience. Finally, a network architecture based on the experimental results is presented to show an example usage scenario of a high data rate WAIC network.

In order to scale web or other services, the load on single instances of the respective service has to be balanced. Many services are stateful such that packets belonging to the same connection must be delivered to the same instance. This requires stateful load balancers which are mostly implemented in software. On the one hand, modern packet processing frameworks supporting software load balancers, such as the Data Plane Development Kit (DPDK), deliver high performance compared to older approaches. On the other hand, common Network Interface Cards (NICs) provide additional matching capabilities that can be utilized for increasing the performance even further and in turn reduce the necessary server resources. In fact, offloading the packet matching to hardware can free up CPU cycles of the servers. Therefore, in this work, we propose the Hybrid NIC-offloading Load Balancer (HNLB), a high performance hybrid hardware-software load balancer, utilizing the NIC-offloading hardware matching capabilities. The results of our performance evaluations show that the throughput using NIC offloading can be increased by up to 50%, compared to a high performance software-only implementation. Furthermore, we investigated the limitations of our proposed approach, e.g., the limited number of possible concurrent connections.

Enabling closed control loops via wireless communication has attracted a lot of interest recently and is investigated under the name cyber-physical systems. Under cyber-physical systems one challenging scenario is multiple loops sharing a wireless medium, and the age of the control information has to be minimized without sacrificing reliability to guarantee the control stability. The number of transmitting devices depends on the control parameters thus, it is stochastic. Wireless uplink resource allocation given low latency constraints for unknown number of devices is a hard problem. For this problem, random access is the most prominent way to minimize latency, but reliability is sacrificed. However, as reliability is also critical for such applications, improved random access algorithms with hard latency guarantees are needed. Currently available random access algorithms with hard latency guarantees have low throughput and some of them are limited to low number of active devices. In this work, we provide a high-throughput random access algorithm with hard latency-constraints (SICQTA) that scales to any number of active devices. This algorithm, making use of feedback, has a varying throughput between 0.69 and 1 depending on the number of devices, which is unprecedented in the state of the art up to our best knowledge.

Enabling autonomous driving and industrial automation with wireless networks poses many challenges, which are typically abstracted through reliability and latency requirements. One of the main contributors to latency in cellular networks is the reservation-based access, which involves lengthy and resource-inefficient signaling exchanges. An alternative is to use grant-free access, in which there is no resource reservation. A handful of recent works investigated how to fulfill reliability and latency requirements with different flavors of grant-free solutions. However, the resource efficiency, i.e., the throughput, has been only the secondary focus. In this work, we formulate the throughput of grant-free access under reliability-latency constraints, when the actual number of arrived users or only the arrival distribution are known. We investigate how these different levels of knowledge about the arrival process influence throughput performance of framed slotted ALOHA with K-multipacket reception, for the Poisson and Beta arrivals. We show that the throughput under reliability-latency requirements can be significantly improved for the higher expected load of the access network, if the actual number of arrived users is known. This insight motivates the use of techniques for the estimation of the number of arrived users, as this knowledge is not readily available in grant-free access. We also asses the impact of estimation error, showing that for high reliability-latency requirements the gains in throughput are still considerable.

Network slicing is envisioned as a tool for 5G networks to provide network flexibility and isolation among different logical networks. While network slicing is well investigated in the fixed-network side, in the Radio Access Network (RAN) there remain challenging problems, which originate mainly from the stochastic nature of the wireless channels and complex resource coupling between slices. In this work, we investigate a network slicing problem for the downlink RAN of a cellular network. Our target is the reduction of resource usage while guaranteeing slice isolation and simultaneously accounting for each slice's average rate and delay requirements. We tackle the problem with a Lyapunov optimization approach, leading to a simple resource assignment procedure that we can prove to achieve isolation while satisfying all slice guarantees. The proposed procedure leads to a functional split, where resources are scheduled within each slice by a slice manager, while a Software-Defined RAN (SD-RAN) controller dynamically re-assigns resources to each slice. We verify our approach through extensive simulations and provide insight on how to fine-tune available system parameters.

Distributed Software Defined Networking (SDN) controllers aim to solve the issue of single-point-of-failure and improve the scalability of the control plane. Byzantine and faulty controllers, however, may enforce incorrect configurations and thus endanger the control plane correctness. Multiple Byzantine Fault Tolerance (BFT) approaches relying on Replicated State Machine (RSM) execution have been proposed in the past to cater for this issue. The scalability of such solutions is, however, limited. Additionally, the interplay between progressing the state of the distributed controllers and the consistency of the external reconfigurations of the forwarding devices has not been thoroughly investigated. In this work, we propose an agreement-and-execution group-based approach to increase the overall throughput of a BFT-enabled distributed SDN control plane. We adapt a proven sequencing-based BFT protocol, and introduce two optimized BFT protocols that preserve the uniform agreement, causality and liveness properties. A state-hashing approach which ensures causally ordered switch reconfigurations is proposed, that enables an opportunistic RSM execution without relying on strict sequencing. The proposed designs are implemented and validated for two realistic topologies, a path computation application and a set of KPIs: switch reconfiguration (response) time, signaling overhead, and acceptance rates. We show a clear decrease in the system response time and communication overhead with the proposed models, compared to a state-of-the-art approach.

People desire to be connected, no matter where they are. Recently, providing Internet access to on-board passengers has received a lot of attention from both industry and academia. However, in order to guarantee an acceptable Quality of Service (QoS) for the passenger services with low incurred cost, the path to route the services, as well as the datacenter (DC) to deploy the services should be carefully determined. This problem is challenging, due to different types of Air-to-Ground (A2G) connections, i.e., satellites and Direct Air-To-Ground (DA2G) links. These A2G connection types differ in terms of cost, bandwidth, and latency. Furthermore, due to the flights' movements, it is important to consider adapting the service location accordingly. In this work, we formulate two Mixed Integer Linear Programs (MILPs) for the problem of Joint Service Placement and Routing (JSPR): i) Static (S-JSPR), and ii) Mobility-Aware (MA-JSPR) in Space-Air-Ground Integrated Networks (SAGIN), with the objective of minimizing the total cost. We compare S-JSPR and MA-JSPR using comprehensive evaluations in a realistic European-based SAGIN. The obtained results show that the MA-JSPR model, by considering the future flight positions and using a service migration control, reduces the long-term total cost notably. Also, we show S-JSPR benefits from a low time-complexity and it achieves lower end-to-end delays comparing to MA-JSPR model.

Network Function Virtualization (NFV) aims to virtualize compute resources for packet processing in order to gain flexibility and reduce costs. In order to increase the resource utilization, multiple VNFs are co-located on one single server. Current virtualization techniques do not fully isolate all resources, thus co-location of VNFs causes interference effects. It has been shown that these interference effects can degrade the performance of Virtualized Network Functions (VNFs) in terms of throughput and delay severely. In this work we aim to gather the potential that lies in reduction of the interference due to the shared Last Level Cache (LLC). CPU caches are used to improve the access times to memory that is needed regularly for the execution of a program. Intel Cache Allocation Technology (CAT) provides the means to allocate the cache and isolate VNFs from each other. The results show that the scheduler can decrease the CPU utilization by up to 20%. We can show which factors influence the gain of LLC scheduling in NFV deployments. In order to show this we propose a scheduler which optimally allocates the LLC in order to reduce the maximum CPU utilization of all cores.

Software-Defined Networking (SDN) has been identified as a key enabler for 5G networks to enhance the network capabilities by introducing flexibility and programmability. While SDN has been widely exploited in the core network side, it still remains an open research question in the Radio Access Network (RAN). Initial works highlight the benefits of SDN in RAN and investigate the idea of separating the control plane from the data plane of the Base Stations (BS) by means of SDN. The pioneer Software-Defined RAN (SD-RAN) controllers are available, nonetheless there exist no tools which can shed light on their performance and help to understand their limitations. We introduce SDRBench, a novel SD-RAN controller benchmark tool to fill this void. In this work, we evaluate FlexRAN, which is the first open source SD-RAN platform. In our tool, a Python-based instance of a FlexRAN agent is created and it communicates messages with the controller according to the FlexRAN protocol. The benchmark is used to uncover the limits of the SD-RAN controller.

Virtualization and multi-tenancy are attractive paradigms to improve the utilization of computing infrastructures and hence to reduce costs. In order to provide a high degree of resource sharing without sacrificing predictable cloud application performance, strict performance isolation needs to be ensured. This is non-trivial and requires models which account for all components where applications may interfere: similarly to security, the predictability of cloud application performance can only be as good as the least predictable component in the model. This paper identifies a new source of potential performance interference that has been overlooked so far: the network hypervisor - a critical component in any multi-tenant network. We present a first measurement study of the performance implications of the network hypervisor in Software-Defined Networks (SDNs). For the purpose of our study, we developed a new open-source benchmarking tool for OpenFlow control and data planes. We show that cloud application performance may appear unpredictable if the network hypervisor is not accounted for: the performance does not only depend on the specific hypervisor implementation and workload (e.g., OpenFlow message types), but also on the number of tenants and the size of the network. Hence, our results suggest that hypervisors should be included in our performance models, and their performance benchmarked and compared similarly to other crucial software components such as the SDN controller.

For communication networks research, flexibility of network design and networking solutions is considered a competitive advantage. However, this advantage is typically only claimed on an argumentative level and neither formally supported nor thoroughly investigated. To support the claim of flexibility and to make the flexibility of different solutions comparable, its degree must be quantified and thus made measurable. In this work, we propose a mathematical basis to quantify a degree of flexibility achieved by communication networks. We motivate that flexibility can be cast to the “size” of a set of achievable demand changes. Consequently, we propose the use of mathematical measure theory to quantify achieved networking flexibility. We derive several implications on the basic structure of flexibility, extend the insights towards a utility of flexibility and develop systematic approaches for both analytical and empirical measurement of flexibility. We apply the insights to several use-cases, showing that flexibility is in fact not as straight-forward to argue as it seems at first glance.

Automation is a key part of the new industrial revolution, that will be enabled by the deployment of thousands of sensors and actuators. The flexible deployment of these devices requires wireless connectivity which is labeled as industrial internet of things, IIoT. The sporadic activity pattern of IIoT devices naturally suggest the use of random access techniques, albeit posing new and unexplored challenges for the current wireless networks. On top of the demand for new access protocols, the latency-reliability requirements further challenge the existing random access protocols. In this work we investigate the adaptation of a well known modern random access algorithm, Irregular Repetition Slotted ALOHA (IRSA) to IIoT in 5G New Radio. The key contribution of the paper is the proposed system level protocol, Adaptive-Multichannel IRSA, that can fulfill the latency-reliability requirements. On top of this, the definition and solution of the resource allocation problem as a resource efficiency optimization guarantees that the algorithm minimizes the system resources. We show that for a set of specific requirements, AMC-IRSA can fulfill the requirements in a lot resource efficient manner. Lastly, we analyze most critical parameters to consider for integration of IRSA for 5G NR.

Analog beamformer with large-scale antenna arrays has been widely considered in millimeter wave (mmWave) communication systems because of its superiority in hardware cost and energy consumption compared with traditional fully digital beamforming schemes. In this paper, we introduce an efficient dynamic subarray analog beamforming architecture with low-resolution phase shifters (PSs) for mmWave multiuser multipleinput single-output (MU-MISO) systems. In an effort to mitigate the performance loss due to the use of low- resolution PSs, each user can dynamically select a non-overlap subarray from total transmit antennas and use corresponding subarray analog beamformer to transmit signals. This dynamic subarray analog beamforming architecture can utilize the multi- antenna/multiuser diversities by dynamically adapting to channel state information (CSI) of users. An efficient dynamic subarray analog beamformer design algorithm is also presented, which aims at maximizing the sum-rate of the MU-MISO system. Simulation results demonstrate that the proposed dynamic analog beamforming solution can significantly outperform the conventional fixed-subarray schemes.

The recent ongoing development of electrical vehicles (EVs) offers vast benefits not only in environmental protection and economics, but also in demand response (DR) management on consumer side. Adopting EVs in DR enables householders to alleviate the load burden while reducing electric bill simultaneously. In this paper, we utilize EVs as temporary energy storage facilities to assist the power transaction, which ensures the flexibility and economic benefit. An innovative EVs assisted DR strategy including a neighbor energy sharing (NES) model is proposed, to jointly optimize the load distribution via vehicle to home (V2H) and vehicle to neighbor (V2N) connections, and economic cost for a residential network with multi-household. The effectiveness of the proposed DR strategy is verified by numerical results in terms of load balancing and cost reduction. It also significantly outperforms the previous DR approaches.

This paper investigates analog beamforming with large-scale antenna arrays for single-group multicast transmission. We focus on the max-min fair (MMF) problem and aim to design the analog beamformer with infinite and finite resolution phase shifters (PSs), respectively, to maximize the minimum signal-to-noise ratio (SNR) over all users subject to a transmit power constraint. However, the constant magnitude and infinite/finite phase constraints imposed by PSs frustrate the access of an optimal solution of analog beamformer. We thus formulate a sub-optimal MMF problem alternatively and propose a low-complexity algorithm, which iteratively determines each element of analog beamformer to conditionally maximize the minimum SNR among users. The computational complexities of our proposed algorithms are linear in the number of antennas. Simulation results illustrate that our proposed analog beamformer design can achieve satisfactory performance which is close to the full-digital case and outperform the other state-of-the-art schemes.

Research in large-scale networking systems has been shaped and will continue to be guided by specific characteristics of applications and the underlying platforms and infrastructures. On the one hand, applications are growing at an accelerated pace, which is fundamentally unpredictable in both breadth and depth. On the other hand, the underlying networking has been the focus of a huge transformation enabled by new models resulting from virtualization and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and more recently, edge cloud and fog networking, or network slicing[1],[2]. This evolution towards enhanced design opportunities along with increasing complexity in networking and its applications has fueled the need for improved network automation in agile infrastructures. At the same time, their complexity has dramatically increased. The networking dynamics have had the effect of making it even more important and challenging to design scalable network measurement and analysis techniques and associated tools. Critical applications such as resource allocation, network monitoring, security enforcement, or dynamic network management require real-time mechanisms for online analysis as well as efficient techniques for offline deep analysis of massive historical data.

Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular “tuning knobs” in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly “self-driving” networks.

With a centralized control over the forwarding devices and the embedded flows, Software Defined Networking promises to increase the flexibility of communication networks. Meanwhile, a dynamic control plane would adapt itself in a timely manner to sustain flow setup performance in the face of traffic variations. Such adaptation depends on a careful decision of the controller placement, which is challenging because we need to consider two contradictory objectives, namely the cost of operating the control plane and the cost of its adaptation. In this work, we model the problem of operating the control plane as a multi-period offline optimization problem to minimize the total cost induced by the flow setup performance and the control plane adaptation. We leverage the lookahead control scheme and decompose the intractable offline problem into smaller instances, which are solved in an online fashion efficiently with an algorithm based on simulated annealing. We perform extensive simulations on real world topologies and show that our proposed algorithm can reduce the total cost by up to 20% compared with the reference algorithms. Further, we analyze the need of frequent control plane adaptation, and compare different control plane design choices according to a novel flexibility measure.

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

The problem of wireless M2M communication is twofold: the reliability aspect and the scalability aspect. The solution to this problem demands a delay constrained random access protocol. To this end, we propose admission control based traffic-agnostic delay-constrained random access (AC/DC-RA) protocol. Our main contribution is enabling the stochastic delay constraints agnostic to the traffic, such that the stochastic delay constraint is valid with respect to a varying number of arrivals. We achieve this with an admission control decision that uses a novel collision estimation algorithm for an active number of arrivals per contention resource. We use an adaptive contention resolution algorithm to react to the varying number of arrivals. Using these tools, the admission control solves the stability problem. We show with simulations that the AC/DC-RA provide stochastic delay constrained random access in a traffic agnostic way to sustain a stable performance against Poisson and Beta arrivals without any modification to the protocol.

The design of the 5G next-generation radio access network (NG-RAN) proposes the division of the next- generation eNodeB (gNB) into centralized and distributed units. Centralization should facilitate coordination of RAN functions between gNBs, but the actual benefits of it are still unclear. In this paper, we provide a study of the feasibility and benefits of coordinated downlink scheduling. We first analyze the time constraints that a coordinated scheduler has to face from a theoretical perspective, and we back them with an experimental proof-of-concept. Then, we present a lightweight scheme for coordinated link adaptation that matches the previous constraints. We show that coordination is indeed feasible with state- of-the-art technology, although very limited by time constraints. Finally, we show the results of our experimental testbed, which successfully implemented the described coordination scheme under the predicted constraints.

The standard interference (SI) framework denotes a set of mathematical properties claimed capable of describing interference in numerous wireless technologies. By showing that interference behaves "standard", so the idea, known algorithms for power control and interference management can be reapplied to novel technologies. Even further, interference across physical (PHY) layers can be managed with a single technique based on SI, as long as all involved technologies behave standard. Although some examples for technologies with standard interference are well-known, e.g., single-channel communication and beamforming, a systematic search for systems with standard properties has not been done in literature. In this work, we present proofs for the standard behavior of interference in numerous wireless technologies. We show that apart from common single-input single-output (SISO) links, also links with transmit- and receive beamforming, successive interference cancellation (SIC), amplify and forward (AF) relaying and fullduplex (FD) communication exhibit standard properties. Further, the standard property can be claimed for links with probabilistic channels and/or transmissions, such as ALOHA based networks. Finally, we investigate multi-channel interference. By leveraging the newly proven result that standard properties can directly be deduced from the associated rate function, we find that interference in multi-channel and multi-input multi-output (MIMO) systems, too, exhibit the standard properties. The variety of SI systems motivates that although abstract, standard interference has the potential to create a generalized interference management framework, capable of incorporating most existing communication types.

Software Defined Networking (SDN) has been identified as a potential approach to achieve a more flexible control and management of the traditional satellite systems and enhance the opportunities for future services including the possibility of a hybrid satellite/terrestrial network. Given the renewed interest towards Low-Earth-Orbit (LEO) constellations, an interesting research topic is the design of a suitable network management model taking into account user specific metrics. In this paper, we address this issue while investigating the use-case scenario of an SDN-enabled satellite space segment. A Dynamic Controller Placement Problem (DCPP) is considered for a LEO constellation where the traffic demands change dynamically based on users' geographical position and time zone. To this end, we develop a mathematical model and formulate it as an Integer Linear Programming (ILP) guaranteeing an optimal controller placement and satellite-to-controller assignment minimizing the average flow setup time with respect to the traffic dynamics. We show results for the DCPP regarding the average flow setup time. Furthermore, a comparison with respect to the static approach is investigated and the proposed SDN-enabled LEO constellation architecture is compared with alternative architectures proposed in the state of the art.

The Network Function Virtualization (NFV) is very promising for efficient provisioning of network services and is attracting a lot of attention. NFV can be implemented in commercial off-the-shelf servers or Physical Machines (PMs), and many network services can be offered as a sequence of Virtual Network Functions (VNFs), known as VNF chains. Furthermore, many existing network devices (e.g., switches) and collocated PMs are underutilized or over-provisioned, resulting in low power-efficiency. In order to achieve more energy efficient systems, this work aims at designing the placement of VNFs such that the total power consumption in network nodes and PMs is minimized, while meeting the delay and capacity requirements of the foreseen demands. Based on existing switch and PM power models, we formulate an Integer Linear Programming (ILP) model to find the optimal solution. We also propose a heuristic based on the concept of Blocking Islands (BI), and a baseline heuristic based on the Betweenness Centrality (BC) property of the graph. Both heuristics and the ILP solutions have been compared in terms of total power consumption, delay, demands acceptance rate, and computation time. Our simulation results suggest that BI-based heuristic is superior compared with the BC-based heuristic, and very close to the optimal solution obtained from the ILP in terms of total power consumption and demands acceptance rate. Compared to the ILP, the proposed BI-based heuristic is significantly faster and results in 22% lower end-to-end delay, with a penalty of consuming 6% more power in average.

The routing algorithms used by current operators aim at coping with the demanded QoS requirements while optimizing the use of their network resources. These algorithms rely on the optimal substructure property (OSP), which states that an optimal path contains other optimal paths within it. However, we show that QoS metrics such as queuing delay and buffer consumption do not satisfy this property, which implies that the used algorithms lose their optimality and/or completeness. This negatively impacts the operator economy by causing a waste of network resources and/or violating Service Level Agreements (SLAs). In this paper, we propose a new so-called Mn taxonomy defining new metric classes. An Mn metric corresponds to a metric which requires the knowledge of the n previously traversed edges to compute its value at a given edge. Based on this taxonomy, we present three solutions for solving routing problems with the newly defined classes of metrics. We show that state-of- the-art algorithms based on the OSP indeed lose their original optimality and/or completeness properties while our proposed solutions do not, at the price of an increased computation time.

Random Access Channel (RACH) has been identified as one of the major bottlenecks for accommodating massive number of Machine-to-Machine (M2M) devices in LTE networks, especially in the case of bursty arrivals of connection requests. As a consequence, the burst resolution problem has sparked a large number of works analyzing and optimizing the expected performance of RACH. In this paper, we go beyond the study of performance in expectation by investigating the probabilistic performance limits of RACH with access class barring. We model RACH as a queuing system, and apply stochastic network calculus to derive probabilistic performance bounds for burst resolution time, i.e., the time it takes to connect a burst of M2M devices to the base station. We illustrate the accuracy of the proposed methodology and its potential applications in performance assessment and system dimensioning.

Network virtualization enables increasingly diverse network services to cohabit and share a given physical infrastructure and its resources, with the possibility to rely on different network architectures and protocols optimized towards specific requirements. In order to ensure a predictable performance despite shared resources, network virtualization requires a strict performance isolation and hence, resource reservations. Moreover, the creation of virtual networks should be fast and efficient. The underlying NP-hard algorithmic problem is known as the Virtual Network Embedding (VNE) problem and has been studied intensively over the last years. This paper presents NeuroViNE, a novel approach to speed up and improve a wide range of existing VNE algorithms: NeuroViNE is based on a search space reduction mechanism and preprocesses a problem instance by extracting relevant subgraphs, i.e., good combinations of substrate nodes and links. These subgraphs can then be fed to an existing algorithm for faster and more resource-efficient embeddings. NeuroViNE relies on a Hopfield network, and its performance benefits are investigated in simulations for random networks, real substrate networks, and data center networks.

Emerging technologies enforce strict requirements on future wireless networks such as massive connectivity that cannot be supported with scheduled access. Contention based Non-Orthogonal Multiple Access is a novel technique to overcome strict massive connectivity requirements by efficient use of wireless resources. However, most of the solutions proposed in this direction assumes different loads which would degrade the performance significantly if they would not hold. To stress these assumptions a resource efficiency metric is defined and state of the art solutions are evaluated for varying load regarding this metric. It is shown that the resource efficiency problem in the state of the art can be improved with multiplicity estimation, and hence, we propose Multiplicity estimating Random Access protocol, that adapts to the dynamic loads. This adaptation is evaluated through analytical calculation against the state of the art and it is shown that resource efficiency against with a slight decrease in the metric any load from 1 up to > 103users is supported. In addition, we show how this protocol can be dimensioned and integrated to contention based NOMA.

Current approaches to tackle the single point of failure in SDN entail a distributed operation of SDN controller instances. Their state synchronization process is reliant on the assumption of a correct decision-making in the controllers. Successful introduction of SDN in the critical infrastructure networks also requires catering to the issue ofunavailable,unreliable(e.g. buggy), andmaliciouscontroller failures. We propose MORPH, a framework tolerant to unavailability and Byzantine failures, which distinguishes and localizes faulty controller instances and appropriately reconfigures the control plane. Our controller-switch connection assignment leverages the awareness of the source of failure to optimize the number of active controllers and minimize the controller and switch reconfiguration delays. The proposed re-assignment executes dynamically after each successful failure identification. We require$2F_{M}+F_{A}+1$controllers to tolerate$F_{M}$malicious and$F_{A}$availability-induced failures. After a successful detection of$F_{M}$malicious controllers, MORPH reconfigures the control plane to require asinglecontroller message to forward the system state. Moreover, we outline and present a solution to the practical correctness issues related to thestatefulnessof the distributed SDN controller applications, previously ignored in the literature. We base our performance analysis on a resource-aware routing application, deployed in an emulated testbed comprising up to 16 controllers and up to 34 switches, so to tolerate up to 5 unique Byzantine and additional 5 availability-induced controller failures (a total of 10 unique controller failures). We quantify and highlight the dynamic decrease in the packet and CPU load and the response time after each successful failure detection.

Scalability of the control plane in a software-defined network (SDN) is enabled by means of decentralization of the decision-making logic, i.e., by replication of controller functions to physically or virtually dislocated controller replicas. Replication of a centralized controller state also enables the protection against controller failures by means of primary and backup replicas responsible for managing the underlying SDN data plane devices. In this paper, we investigate the effect of the deployed consistency model on scalability and correctness metrics of the SDN control plane. In particular, we compare the strong and eventual consistency, and make a case for a novel adaptive consistency approach. The existing controller platforms rely on either strong or eventual consistency mechanisms in their state distribution. We show how an adaptive consistency model offers the scalability benefits in terms of the total request-handling throughput and response time, in contrast to the strong consistency model. We also outline how the adaptive consistency approach can provide for correctness semantics that are unachievable with the eventual consistency paradigm in practice. The adaptability of our approach provides a balanced and tunable tradeoff of scalability and correctness for the SDN application implemented on top of the adaptive framework. To validate our assumptions, we evaluate and compare the different approaches in an emulated testbed with an example of a load balancer controller application. The experimental setup comprises up to five extended OpenDaylight controller instances and two network topologies from the area of service provider and data center networks.

Industrial control systems are foreseen to operate over hybrid wired/wireless networks. While the controller will be deployed in the wired network, sensors and actuators will be deployed in a wireless sensor network (WSN). To support QoS for control systems, an end-to-end delay bound and a target reliability must be provided in both wireless and wired domains. However, for industrial WSNs, guaranteeing reliability is a challenging task because of low-power communications and the harsh wireless environment. In this work, we present the first QoS framework for arbitrary hybrid wired/wireless networks, which guarantees that the delay bound and the target reliability of each application are provided. As part of this framework, we propose the first reliability-based scheduler for WSN able to achieve a target reliability in the presence of dynamic interference. Simulations of the proposed scheduler prove its suitability in different interference scenarios and motivate further work.

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special section was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

Software defined networking (SDN) promises unprecedented flexibility and ease of network operations. While flexibility is an important factor when leveraging advantages of a new technology, critical infrastructure networks also have stringent requirements on network robustness and control plane delays. Robustness in the SDN control plane is realized by deploying multiple distributed controllers, formed into clusters for durability and fast-failover purposes. However, the effect of the controller clustering on the total system response time is not well investigated in current literature. Hence, in this work we provide a detailed analytical study of the distributed consensus algorithm RAFT, implemented in OpenDaylight and ONOS SDN controller platforms. In those controllers, RAFT implements the data-store replication, leader election after controller failures and controller state recovery on successful repairs. To evaluate its performance, we introduce a framework for numerical analysis of various SDN cluster organizations w.r.t. their response time and availability metrics. We use Stochastic Activity Networks for modeling the RAFT operations, failure injection and cluster recovery processes, and using real-world experiments, we collect the rate parameters to provide realistic inputs for a representative cluster recovery model. We also show how a fast rejuvenation mechanism for the treatment of failures induced by software errors can minimize the total response time experienced by the controller clients, while guaranteeing a higher system availability in the long-term.

In software defined networking (SDN), critical control plane functions are offloaded to a software entity known as the SDN controller. Today's SDN controllers are complex software systems, owing to heterogeneity of networks and forwarding devices they support, and are inherently prone to bugs. Our previous work showed that software reliability growth models (SRGM) can model the stochastic nature of bug manifestation process open source SDN controllers. In this paper, we focus on different applications of our SRGM framework crucial for an efficient management of SDN-based networks. We provide guidelines for network operators to decide when the controller software is mature enough to be deployed in operational environment, based on the reliability requirements of network applications, and quantify the marginal benefits of the prolonged testing phase on the software quality. We show how the accuracy of software reliability prediction in the early phase of the software lifecycle can be improved by extrapolating the behavior of previous controller software releases. We also propose software maturity metrics that can be used by operators to discriminate between the competing SDN controller designs, i.e., ONOS and OpenDaylight, when software reliability is a major concern.

Low task execution time and low energy consumption of collaborating mobile human users and robots are important requirements of emerging human-agent-robot teamwork (HART)-centric Tactile Internet applications. In particular, task migration among mobile HART members has emerged as an important research topic, taking different task types, task deadlines, collaborative node capabilities, and mobility patterns into account. We propose a context-aware task migration scheme for efficiently orchestrating the real-time collaboration among human mobile users, central and decentralized computational agents (cloud/cloudlets), and collaborative robots (cobots) across converged fiber-wireless (FiWi) communications infrastructures. We investigate the problem of whether and, if so, when and where a HART-centric task should be best migrated to. For resource-efficient task execution, the migration decision is made according to given task processing capabilities of cloud/cloudlet agents and cobots, task execution deadline, energy consumption of involved cobots and mobile devices, and task migration latency. We evaluate the performance of our context-aware HART-centric task migration scheme and compare it to conventional task execution without migration. Towards this end, we develop an analytical framework for quantifying its performance in terms of a variety of task migration key performance metrics, including task migration gain-overhead ratio, deadline-miss ratio, task response time, and energy consumption efficiency.

We investigate the problem of how to maximize the frequency reuse factor in a cellular network with device-to-device links and dynamic power control. Recent works in the literature mostly assume fixed powers, which we find to enable only around 70% of achievable reuse, or limit the reuse factor a priori. The problem is formulated as a mixed-integer nonlinear problem and identified as non-deterministic polynomial-time complete. Nevertheless, we present a full analysis of the involved effects and identify the main driver of complexity, that a priori, the effect of binary link activation and deactivation on the spectral radius of the relative gain matrix cannot be properly bounded. We identify a core parameter that we call relative interference pressure and that reflects the severity of interference caused by a link with dynamic power control. Based on relative interference pressure, we propose two solution algorithms, the worst pressure shutdown and the $\tilde {\varepsilon }$ Pressure Packing algorithm. Both algorithms are thoroughly evaluated with simulations and compared with state-of-the-art methods. The proposed algorithms maximize reuse nearly optimally, enabling the remaining 30% of frequency reuse.

Software Defined Networking (SDN) exposes critical networking decisions, such as traffic routing or enforcement of the critical security policies, to a software entity known as the SDN controller. Controller software, as written by humans, is intrinsically prone to bugs, which may impair the network performance as a whole, if activated. Software reliability growth models (SRGM) are often used to estimate and predict the reliability of the software in the operational phase based on the fault report data during the testing phase. These models can be used to predict the number of residual bugs in the software, as well as failure intensity, software reliability and optimal software release time. In this paper we analyze ten releases of ONOS open source controller, whose uncensored fault reports are available online.

Next generation telecommunication systems are required to efficiently support orders of magnitude larger amount of devices per cell than the current LTE networks. This requirement is causing major design challenges for the Random Access Channel (RACH), especially for Machine-to-Machine (M2M) applications. On the other hand, due to the increasing spectrum demands, LTE vendors are exploring unlicensed spectrum. For example, MulteFire has been recently standardized as an LTE- based technology for standalone deployment in unlicensed 5GHz frequency bands. It is reasonable to expect that the coexistence with Wi-Fi and standalone LTE in the unlicensed spectrum, will amplify Random Access problem and worsen RACH performance. Henceforth, in this paper, we quantify the Wi-Fi-LTE coexistence and its impact on the RACH performance. We consider a synchronized activation of a large amount of UEs in an MulteFire/LTE unlicensed cell, and analyze the time it takes to connect all of them to the base station. Our results confirm that the presence of Wi-Fi substantially degrades RACH performance, with an increase of almost 50% per additional Wi-Fi station. Furthermore, we illustrate applications of our evaluation for RACH resource dimensioning and network planning.

Automotive Machine Type Communication (MTC) features three groups of applications: safety, road traffic efficiency and infotainment. The concrete set of automotive applications is yet uncertain. This application uncertainty and, thus communication traffic uncertainty, directly translates into the need for a highly flexible traffic model. Traffic model is required in order to chose appropriate communication technology, to perform accurate network dimensioning and for resource scheduling. The challenge for automotive MTC traffic modeling is in high individual and aggregated traffic complexity, e.g., bursty arrival distributions, as well as traffic correlation in space and time. To flexibly address this challenge, we present a traffic model based on Coupled Markovian Arrival Process (CMAP). CMAP models traffic of each individual user, and can include time and space correlation of users. We evaluate CMAP performance in learning multimodal complex inter-arrival time distributions and compare it to the reference traffic models that are limited to exponential inter-arrival time modeling. We show that, for bursty traffic, exponential inter-arrival time distribution results in significant traffic overestimation, while CMAP accurately captures the traffic.

Software-Defined Networking (SDN) controllers are network entities that act as strategic control points in an SDN network. Controller placement studies mostly aim at optimizing network performance in terms of control latency, reliability and resilience, given network characteristics that are static. Yet dynamic traffic conditions, if not adapted by the controller placement properly, may cause high end-to-end flow setup time. For reactive controllers, the end-to-end flow setup time of a flow implies the difference between sending time at the source and receiving time at the sink of the first packet in that flow. Therefore, end-to-end flow setup time indicates the amount of time needed to set up forwarding rules in all involved switches and acts as a primary concern in terms of service establishment of network operators. In this paper, we analyze the controller placement for dynamic traffic flows based on a combined controller placement model: controller locations and switch-to-controller assignments are simultaneously optimized for minimum average flow setup time with respect to different traffic conditions inside the network. Linearization method is applied to transform the problem into a Mixed Integer Programming (MIP) problem which can be solved optimally. Two derivatives are also presented for comparison, one optimizing only controller locations and the other optimizing only switch-to-controller assignments. Our simulations cover two real network topologies and we explain the effects of the models have on the flow setup time with respect to dynamic flows. For low flow densities, the controller placement that adapts to flows could reduce the average flow setup time by about 50% compared to the static placement. However, when densities are high, the need of changing controller placement to guarantee flow setup performance is marginal.

State synchronisation in clustered Software Defined Networking controller deployments ensures that all instances of the controller have the same state information in order to provide redundancy. Current implementations of controllers use a strong consistency model, where configuration changes must be synchronised across a number of instances before they are applied on the network infrastructure. For large deployments, this blocking process increases the delay of state synchronisation across cluster members and consequently has a detrimental effect on network operations that require rapid response, such as fast failover and Quality of Service applications. In this paper, we introduce an adaptive consistency model for SDN Controllers that employs concepts of eventual consistency models along with a novel `cost-based' approach where strict synchronisation is employed for critical operations that affect a large portion of the network resources while less critical changes are periodically propagated across cluster nodes. We use simulation to evaluate our model and demonstrate the potential gains in performance.

With Network Function Virtualization (NFV), network functions are deployed as modular software components on the commodity hardware, and can be further chained to provide services, offering much greater flexibility and lower cost of the service deployment for the network operators. At the same time, replacing the network functions implemented in purpose built hardware with software modules poses a great challenge for the operator to maintain the same level of performance. The grade of service promised to the end users is formalized in the Service Level Agreement (SLA) that typically contains the QoS parameters, such as minimum guaranteed data rate, maximum end to end latency, port availability and packet loss. State of the art solutions can guarantee only data rate and latency requirements, while service availability, which is an important service differentiator is mostly neglected. This paper focuses on the placement of virtualized network functions, aiming to support service differentiation between the users, while minimizing the associated service deployment cost for the operator. Two QoS-aware placement strategies are presented, an optimal solution based on the Integer Linear Programming (ILP) problem formulation and an efficient heuristic to obtain near optimal solution. Considering a national core network case study, we show the cost overhead of availability-awareness, as well as the risk of SLA violation when availability constraint is neglected. We also compare the proposed function placement heuristic to the optimal solution in terms of cost efficiency and execution time, and demonstrate that it can provide a good estimation of the deployment cost in much shorter time.

This paper is motivated by the emerging vision of an automated and data-driven optimization of communication networks, making it possible to fully exploit the flexibilities offered by modern network technologies and heralding an era of fast and self-adjusting networks. We build upon our recent study of machine-learning approaches to (statically) optimize resource allocations based on the data produced by network algorithms in the past. We take our study a crucial step further by considering dynamic scenarios: scenarios where communication patterns can change over time. In particular, we investigate network algorithms which learn from the traffic distribution (the feature vector), in order to predict global network allocations (a multi-label problem). As a case study, we consider a well-studied fc-median problem arising in Software-Defined Networks, and aim to imitate and speedup existing heuristics as well as to predict good initial solutions for local search algorithms. We compare different machine learning algorithms by simulation and find that neural network can provide the best abstraction, saving up to two-thirds of the algorithm runtime.

Developing models to generate realistic graphs of communication networks often requires a deep understanding and extensive analysis of the underlying network structure. Since deployed communication networks are dynamic, the findings a generator is based on might lose validity. We alleviate the need for extensive analysis of graphs by estimating parameters of a probabilistic model. The model parameters encode the structure of the graph, which is thus learned in an unsupervised fashion. Synthetic graphs can be generated from the model and will have the structure previously inferred. For this, we use the Stochastic-Block-Model (SBM) and the Degree-Corrected-Block-Model (DCBM), a variant allowing for heavy tailed degree distributions. The models originate in the social sciences and separate a graph into groups of nodes. To show the applicability of the models to the task of synthetic graph generation in the domain of communication networks, we use one router level and one IP-to-IP communication graph. We assert the quality of the generated models by evaluating a number of graph features and comparing our results to those obtained with the network generator Orbis. We find our approach to be on par with, or even outperforming Orbis. Furthermore, the models are able to capture large-scale structure in communication networks.

Software Defined Networking (SDN) paved the way for a logically centralized entity, the SDN controller, to excerpt near real-time control over the forwarding state of a network. Network hypervisors are an in-between layer to allow multiple SDN controllers to share this control by slicing the network and giving each controller the power over a part of the network. This makes network hypervisors a critical component in terms of reliability and performance. At the same time, compute virtualization is ubiquitous and may not guarantee statically assigned resources to the network hypervisors. It is therefore important to understand the performance of network hypervisors in environments with varying compute resources. In this paper we propose an online machine learning pipeline to synthesize a performance model of a running hypervisor instance in the face of varying resources. The performance model allows precise estimations of the current capacity in terms of control message throughput without time-intensive offline benchmarks. We evaluate the pipeline in a virtual testbed with a popular network hypervisor implementation. The results show that the proposed pipeline is able to estimate the capacity of a hypervisor instance with a low error and furthermore is able to quickly detect and adapt to a change in available resources. By exploring the parameter space of the learning pipeline, we discuss its characteristics in terms of estimation accuracy and convergence time for different parameter choices and use cases. Although we evaluate the approach with network hypervisors, our work can be generalized to other latency-sensitive applications with similar characteristics and requirements as network hypervisors.

State-of-the-art communication networks feature complex Quality of Service and mobility management policies, resulting in tremendous signaling growth. The mobility management related signaling is subject to a further increase due to automotive users or connected cars, which demand more mobility management efforts due to higher speeds. In this paper, we approach signaling reduction by adaptively forming Tracking Area Lists (TALs) based on mobility prediction and variable TAL forms. We show with simulations on mobility traces, that for individual LTE core network elements we can achieve a reduction of 33% of signaling traffic due to mobility management.

Guaranteeing reliability in highly interfered environments is a challenging requirement of current and future wireless applications. A promising state-of-the-art solution for low-power wireless technologies, e.g., wireless sensor networks (WSNs), is frequency hopping aided with black- and white-listing of channels. Both methods, although increase reliability, sacrifice frequency resources. Extensive measurements of channels' packet drop probabilities show that interfered channels are not fully blocked. Motivated by this discovery, we propose the whitening - a methodology for reliable hopping sequence design without resource sacrifice. We model the efficiency of interfered ISM band channels, and study the gains and trade-offs of applying whitening in different scenarios. Application reliability is achieved by granting re-transmissions within a time deadline. Simulations and measurements, performed on the exemplary use case of Time Slotted Channel Hopping WSNs, show that the proposed methods outperform state-of-the-art solutions in the presence of interference in terms of reliability.

Software Defined Networking (SDN) introduces a new network architecture offering means of programmability through an externalized centralized control plane. As a result most security research addresses attacks against this central entity. Contrary to that, attacks against the data plane in SDN did not perceive a broad attention in the scientific community so far. In this work we discuss Denial of Service attacks against the data plane and their impact. We propose a tailored statistical detection approach as well as a lightweight countermeasure. We evaluate the detection by simulation and an analytical approach. Throughout this evaluation, we highlight the trade-off between detection speed and adaptability and show a way to tune the solution analytically. Our results show, that we can detect and mitigate attacks against the data plane in a lightweight and dependable way.

Accommodating Machine-to-Machine applications and their requirements is one of the challenges on the way from LTE towards 5G networks. The envisioned high density of devices, alongside with their sporadic and synchronized transmission patterns, might create signaling storms and overload in the current LTE network. Here, we address the notorious random access (RA) challenge, namely, scalability of the radio link connection establishment protocol in LTE networks. We revisit the binary countdown technique for contention resolution (BCCR), and apply it to the LTE RA procedure. We analytically investigate the performance gains and trade-offs of applying BCCR in LTE. We further simulatively compare BCCR RA with the state-of-the-art RA techniques, and demonstrate its advantages in terms of delay and throughput.

In this paper we analyze a distributed packet drop decision, the deadline based discard DbD, in a lossy wireless sensor network environment. We point out that if the non-stale packet success rate is the single utility function for low latency high reliability applications then we can use the packet delay to evaluate the usefulness under the scope of deadline. The technique results in no decrease in utility function but possible gains. In case the metric is used in a centralized perspective, it even provides gains through decrease in expected serving times of a packet. We show analytically and simulatively that, it is possible to reduce energy consumption, increase resource usage and decrease required buffer size in both distributed and central view. A practical example for energy saving with actual wireless sensor data is also given as a conclusion.

This paper presents a hierarchical cluster-based protocol for LTE connection establishment of a massive number of Machine Type User Equipments (mUEs). The protocol facilitates spatial reuse of random access channel resources. Our contributions are (i) modeling and analysis of the connection request aggregation, and a study on its influence on the connection from clusterhead to base station, and (ii) an accurate joint medium access model of a random access procedure within a finite-user cluster, considering the cross-impact and interrelation between aggregation and random access procedures. A byproduct of the joint analysis is an accurate finite-user model of random access without aggregation. The models are verified with the simulations and compared to the state-of-the-art. They allow accurate performance predictions, and provide insights on the dimensioning and resource allocation for clusters.

With the rapid growth of user traffic, service innovation, and the persistent necessity to reduce costs, today's mobile operators are faced with several challenges. In networking, two concepts have emerged aiming at cost reduction, increase of network scalability and deployment flexibility, namely Network Functions Virtualization (NFV) and Software Defined Networking (SDN). NFV mitigates the dependency on hardware, where mobile network functions are deployed as software virtual network functions on commodity servers at cloud infrastructure, i.e., data centers. SDN provides a programmable and flexible network control by decoupling the mobile network functions into control plane and data plane functions. The design of the next generation mobile network (5G) requires new planning and dimensioning models to achieve a cost optimal design that supports a wide range of traffic demands. We propose three optimization models that aim at minimizing the network load cost as well as data center resources cost by finding the optimal placement of the data centers as well the SDN and NFV mobile network functions. The optimization solutions demonstrate the trade-offs between the different data center deployments, i.e., centralized or distributed, and the different cost factors, i.e., optimal network load cost or data center resources cost. We propose a Pareto optimal multi-objective model that achieves a balance between network and data center cost. Additionally, we use prior inference, based on the solutions of the single objectives, to pre-select data center locations for the multi-objective model that results in reducing the optimization complexity and achieves savings in run time while keeping a minimal optimality gap.

Industrial networks require real-time guarantees for the flows they carry. That is, flows have hard end-to-end delay requirements that have to be deterministically guaranteed. While proprietary extensions of Ethernet have provided solutions, these often require expensive forwarding devices. The rise of software-defined networking (SDN) opens the door to the design of centralized traffic engineering frameworks for providing such real-time guarantees. As part of such a framework, a network model is needed for the computation of worst-case delays and for access control. In this paper, we propose two network models based on network calculus theory for providing deterministic services (DetServ). While our first model, the multi-hop model (MHM), assigns a rate and a buffer budget to each queue in the network, our second model, the threshold-based model (TBM), simply fixes a maximum delay for each queue. Via a packet-level simulation, we confirm that the delay bounds guaranteed by both models are never exceeded and that no packet loss occurs. We further show that the TBM provides more flexibility with respect to the characteristics of the flows to be embedded and that it has the potential of accepting more flows in a given network. Finally, we show that the runtime cost for this increase in flexibility stays reasonable for online request processing in industrial scenarios.

Softwarization of networks is an important trend, enabled by the NV (Network Virtualization), SDN (Software-Defined Networking), and NFV (Network Function Virtualization) paradigms and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

The allocation of physical resources to virtual networks, i.e., the virtual network embedding (VNE), is still an on-going research field due to its problem complexity. While many solutions for the online VNE problem exist, only few have focused on methods that can be generally applied for optimization of online embeddings. In this paper, we propose an admission control based on a Recurrent Neural Network (RNN) to improve the overall system performance for the online VNE problem. Before running a VNE algorithm to embed a virtual network request, the RNN predicts whether the request will be accepted by the VNE algorithm based on the current state of the substrate and the virtual network request (VNR). The RNN prevents VNE algorithms from spending time on VNRs that are either infeasible or that cannot be embedded in acceptable time. In order to train and operate the RNN efficiently, we additionally propose new representations for substrate networks and virtual network requests. The representations are based on topological and network resource features to represent the substrate network and the VNRs with low computational complexity. Via simulations, we show that our admission control reduces the overall computational time for the online VNE problem by up to 91 % while preserving VNE performance on average. Using our new substrate and request representations, the RNN achieves an accuracy ranging between 89 % and 98 % for different VNE algorithms, substrate sizes, and VNR arrival rates.

It has been postulated in literature that three gain types are introduced into the radio access part of cellular networks by device-to-device communication, namely the proximity gain, hop gain and reuse gain. These gain types have further been shown to be captured by a resource efficiency metric. We investigate the influence of mode selection and scheduling on the resource efficiency gains in the D2D overlay case. We formulate mode selection as a resource efficiency maximization problem and find that for proper mode selection, the achieved resource efficiencies of schedulers must be anticipated correctly. Because this is very complex, we propose a simple, channel state based mode selection scheme and investigate how well it performs using different schedulers. We show that our proposed scheme is optimal for schedulers that do not leverage frequency diversity and optimizes a tight lower bound for those that do leverage diversity. Simulations show that in any case, our scheme produces higher D2D-gains than the state of the art.

Control over shared communication networks is a key challenge in design and analysis of cyber-physical systems. The quality of control in such systems might be degraded due to the congestion while accessing the scarce communication resources. In this paper, we consider a multiple-loop networked control system (NCS), where all control loops share a communication network. Medium Access Control (MAC) is performed in contention-based fashion using a multi-channel slotted ALOHA protocol, where each control loop decides locally whether to attempt a transmission based on some error thresholds. We further introduce a local event-based resource-aware scheduling design with an adaptive choice of the error thresholds for a transmission. This leads to a hybrid channel access mechanism where the control loops are deterministically categorized into two sets of eligible and ineligible sub-systems for transmission in an event-based fashion, before a random process to select the available channels. In addition, employing the introduced policy, we show the stability of the resulting NCS in terms of Lyapunov stability in probability. We illustrate numerically the efficiency of our proposed approach in terms of reducing the average networked-induced error variance, and show the superiority of the adaptive event-based scheduler compared to the scheduling design with non-adaptive thresholds.

Device-to-Device (D2D) communication is envisioned to enhance the functionality of future cellular networks. Dynamic frequency reuse in D2D enhanced networks can increase the frequency reuse factor beyond one and has achieved much attention in the last years. To leverage frequency reuse, a proper reuse link selection must be performed. We investigate a core problem of reuse link selection, namely how to find the maximum number of reuse links in a network. Efficient, optimal solutions for this problem have not been found for over a decade in arbitrary wireless network scenarios. We investigate different decision metrics for reuse link selection under dynamic power control and propose a metric that outperforms the known state of the art solutions.

Industrial networks demand centrally controlled quality of service (QoS), often in the form of hard real-time guarantees. Software-defined networking (SDN) provides a convenient paradigm for central QoS control. However, existing SDN-based solutions cannot guarantee hard real-time QoS as they rely on a control loop over the forwarding (data) and control planes. We propose a novel SDN-based QoS control framework that maintains an accurate network model through network calculus to avoid a control loop over forwarding and control planes, allocates resources to and routes flows over a network of “queue links,” whereby each physical network link houses multiple queue links (with different QoS levels), and manages QoS through a function split between delay-constrained least-cost routing on the network of queue links and the resource allocation to the queue links. This function split greatly reduces the computational complexity while achieving hard real-time QoS with high bandwidth utilization. Our evaluation results indicate that our function split approach allows for online runtime admission control and can achieve bandwidth utilization above 80% while meeting deterministic real-time QoS requirements.

Software defined networking (SDN) network hypervisors provide the functionalities needed for virtualizing software-defined networks. Hypervisors sit logically between the multiple virtual SDN networks (vSDNs), which reside on the underlying physical SDN network infrastructure, and the corresponding tenant (vSDN) controllers. Different SDN network hypervisor architectures have mainly been explored through proof-of-concept implementations. We fundamentally advance SDN network hypervisor research by conducting a model-based analysis of SDN hypervisor architectures. Specifically, we introduce mixed integer programming formulations for four different SDN network hypervisor architectures. Our model formulations can also optimize the placement of multi-controller switches in virtualized OpenFlow-enabled SDN networks. We employ our models to quantitatively examine the optimal placement of the hypervisor instances. We compare the control plane latencies of the different SDN hypervisor architectures and quantify the cost of virtualization, i.e., the latency overhead due to virtualizing SDN networks via hypervisors. For generalization, we quantify how the hypervisor architectures behave for different network topologies. Our model formulations and the insights drawn from our evaluations inform network operators about the trade-offs of the different hypervisor architectures and help choosing an architecture according to operator demands.

This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares algorithm. We estimate abnormal flows as outlier sparse flows via sparsity maximization in the underlying under-constrained linear-inverse problem. A major advantage is that our algorithm estimates normal flows by low-dimensional matrices with time-directional features as well as the spatial correlation of multiple links without using the past observed measurements and the past model parameters. Extensive numerical evaluations show that the proposed algorithm achieves faster convergence per iteration of model approximation and better volume anomaly detection performance compared to state-of-the-art algorithms.

Virtualization of software defined networks enables tenants to bring their own controller and manage their virtual resources with the full programmability provided by Software Defined Networking (SDN). Distributed SDN hypervisors are proposed to provide an efficient platform for the virtualization of physical SDN networks. They address the issue of scalability that a centralized hypervisor could suffer from. As virtualization provides the possibility to change virtual SDN networks on run time, a hypervisor layer needs efficient mechanisms to dynamically adapt to the changing requirements. Existing proposals provide only a static configuration setup for their distribution of the hypervisor instances. However, in order to satisfy the dynamics of virtual SDN networks, management protocols are needed to support dynamic changes. In this paper, we propose a control path migration protocol for distributed hypervisors. Such protocol is needed to support the dynamic adaptation of the virtualization layer. Our protocol is providing the missing procedure that allows a dynamic change of control connections between virtual SDN networks and the tenants' controllers, respectively. We provide a proof of concept implementation for our proposal. Through measurements in a real testbed setup, we show that our protocol is efficient in terms of control latency overhead and provides transparency to the controllers of the virtual SDN networks.

We present a demonstration of HyperFlex [1], a Software-Defined Networking (SDN) virtualization architecture with flexible hypervisor function allocation guaranteeing control-plane virtualization. Network Virtualization (NV) is expected to overcome the ossification of today's communication networks. SDN is seen as an enabler for programmable network control. In order to fully virtualize software-defined networks, not only the virtualization of the data-plane, but also the virtualization of the control-plane has to be considered. HyperFlex is a virtualization hypervisor that ensures isolated control-plane slices for virtual SDN networks. Control-plane isolation also protects the hypervisor resources from exhaustion. Furthermore, virtualization hypervisors have to be scalable and flexible in order to provide the best possible performance for the virtual software-defined networks. They should be able to adapt to the current state of the network and the divergent demands of virtual SDN networks. HyperFlex distributes the hypervisor functions flexibly and dynamically in order to adapt to the current network state.

Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.

Video content and, in particular, YouTube's content account for the largest amount of today's Internet traffic. However, little is known about the behavior of video streaming services for different kinds of network environments and under varying network conditions. Due to network operators' lack of knowledge about the transmitted content, network resources may not be optimally used in general. Thus, we propose a dyadic measurement system composed of application, i.e., client-based and network-based monitoring for YouTube's video traffic. Using our proposed monitoring methodology, we analyze the behavior of YouTube's HTTP-based adaptive video streaming mechanisms. In detail, we quantify via experimental measurements on real network traffic YouTube's behavior for different videos under static and varying network conditions. Our measurement results show that in case of varying network conditions, YouTube demands different video qualities in parallel in order to adapt to the network situation. However, this behavior can result in up to 33 % of redundant network traffic, i.e., downloaded video content of different quality levels for the same play time. Due to our findings, network operators should try to optimize the allocation of network resources for video content in a way that avoids varying network conditions, resulting in less waste of network resources.

In this paper, we demonstrate an implementation of a Network Services Abstraction Layer (NSAL) on top of the network control and management plane. Furthermore, we introduce a unified data model for both Software Defined Networking (SDN) and legacy devices that allows managing and configuring both networks in a unified way in order to achieve Quality of Service (QoS) for time-critical tasks (e.g. VoIP). Due to the unified data model, network operators are able to manage their network through one interface. We demonstrate a use case by implementing a VoIP scheduling application on top of the NSAL and evaluate VoIP call quality in a distributed heterogeneous network.

Software-defined Networking (SDN) is a promising and powerful concept to introduce new dimensions of flexibility and adaptability in today's communication networks. In particular, the realization of Quality of Service (QoS) concepts becomes possible in a flexible and dynamic manner with SDN. Although concepts of QoS are well researched, they were not realized in communication networks due to high implementation complexity and realization costs. Using SDN to realize QoS mechanisms enables emerging concepts, such as application-aware resource management solutions. These emerging concepts demand latency or data rate guarantees for end-user applications, e.g., video streaming or gaming. However, the impact of dynamic QoS management on network traffic has not been studied in detail yet. This paper provides a first study of the impact on dynamic QoS mechanisms and their realizations for OpenFlow-enabled SDN switches. Although SDN and, in particular, OpenFlow as one dominant realization claim to provide a standardized interface to control network traffic, our measurement results show a noticeable diversity for different OpenFlow switches. In detail, our investigations reveal a severe impact on the performance of TCP-based network traffic among different switches. These observations of switch diversity may provide SDN application developers insights when realizing QoS concepts in an SDN-based network.

Industrial networks demand centrally controlled Quality of Service (QoS), often in the form of hard real-time guarantees. We propose a novel SDN-based QoS control paradigm that (i) maintains an accurate network model through network calculus to avoid a control loop over forwarding and control planes, (ii) routes flows over a network of "queue links", whereby each physical network link houses multiple queue links (with different QoS levels), and (iii) manages QoS through delay-constrained least-cost (DCLC) routing on the network of queue links.

Device-to-Device (D2D) communication in cellular networks allows dynamic frequency reuse even within a cell, which can increase the frequency reuse factor beyond one. To perform dynamic reuse, it must be feasible for all links to maintain a certain signal quality despite the presence of interference. This can be ensured using reuse feasibility tests. However, state of the art testing algorithms show erroneous behavior when infeasibility occurs due to limited transmission powers. In this paper we develop two testing algorithms, based on nonnegative matrix theory, that explicitly take limited transmission powers into account and show how these can be implemented in protocols. The protocols are compared with the state of the art in terms of control channel usage, speed and accuracy of decision making. We find that the proposed protocols estimate reuse feasibility more accurately than the state of the art and are comparable in terms of speed and control channel usage.

The increasing popularity of mobile video streaming applications has led to a high volume of video traffic in mobile networks. As the base station, for instance, the eNB in LTE networks, has limited physical resources, it can be overloaded by this traffic. This problem can be addressed by using Scalable Video Coding (SVC), which allows the eNB to drop layers of the video streams to dynamically adapt the bitrate. The impact of bitrate adaptation on the Quality of Experience (QoE) for the users depends on the content characteristics of videos. As the current mobile network architectures do not support the eNB in obtaining video content information, QoE optimization schemes with explicit signaling of content information have been proposed. These schemes, however, require the eNB or a specific optimization module to process the video content on the fly in order to extract the required information. This increases the computation and signaling overhead significantly, raising the OPEX for mobile operators. To address this issue, in this article, a content-aware (CA) priority marking and layer dropping scheme is proposed. The CA priority indicates a transmission order for the layers of all transmitted videos across all users, resulting from a comparison of their utility versus rate characteristics. The CA priority values can be determined at the P-GW on the fly, allowing mobile operators to control the priority marking process. Alternatively, they can be determined offline at the video servers, avoiding real-time computation in the core network. The eNB can perform content-aware SVC layer dropping using only the priority values. No additional content processing is required. The proposed scheme is lightweight both in terms of architecture and computation. The improvement in QoE is substantial and very close to the performance obtained with the computation and signaling-intensive QoE optimization schemes.

Today's Internet does not provide an exchange of information between applications and networks, which may result in poor application performance. Concepts such as application-aware networking or network-aware application programming try to overcome these limitations. The introduction of Software-Defined Networking (SDN) opens a path towards the realization of an enhanced interaction between networks and applications. Hence, a more dynamic and demand-based allocation of network resources to heterogeneous applications can be realized. The implementation of the resource management action, however, may have an impact on the data transport and application quality. This paper summarizes resource management mechanisms provided by current SDN approaches based on OpenFlow and exemplary evaluates implementation prospects and challenges.

The number of electronic control units in today's vehicles is permanently increasing. The complexity of the in-car communication also increases because of the growing demand for information exchange. In the past decades rising demands have been countered with adding bus-segments. In order to reduce the complexity and the costs for the future in-car communication infrastructure, power line communication (PLC) provides an alternative. In this paper, we focus on PLC which can reduce the cabling to a minimum. After summarizing the requirements for future in-car systems, the HomePlug Green PHY standard, originally designed for Smart Grid applications, is analyzed according to its applicability to the in-car communication. Due to the availability of standard components, also cost reasons motivate the use of the HomePlug Green PHY standard. To cope with the small message size of in-car communication, we propose a concept for data transfer inside the Frame Control to reduce protocol overhead. In addition to CSMA/CA based medium access, we evaluate and compare two further collision-free variants requiring only slight protocol modifications. As one evaluation result, a priority-based access scheme shows the most promising results.

Device-to-Device (D2D) communication is the latest of a series of attempts to combine cellular and ad-hoc networks and is seen as a promising concept in the wireless research community. However, the commonly used evaluation metrics for optimization do not capture the expected advantages of D2D. In this paper, a flow-based resource efficiency is defined and applied to a D2D scenario. It is shown that the already formulated gain-expectations proximity-, hop- and reuse-gain can be captured using this efficiency metric. Applying the metric to D2D, it is found that the three gains can be traded off against each other. As a proof of concept, D2D is re-evaluated in a single cell setup. It is motivated that around each D2D transmitter there are two regions, the Tradeoff- and the Win-Win-Region, in which D2D communication is advantageous.

Device-to-device (D2D) communication as an underlay to future cellular networks has been recently considered as an efficient cell offloading and capacity increasing solution. In this paper, we propose to use the D2D underlay as a carrier for automotive safety applications with very strict quality of service and reliability requirements. We propose a location dependent resource allocation scheme (LDRAS) for mobile D2D communications that fulfills the requirements of such services, while reducing the signaling overhead and guaranteeing a certain maximum interference level within the primary network and the D2D underlay, respectively. The former is ensured by applying persistent resource allocation to the vehicular D2D network. The latter is achieved with a spatial reuse scheme with fixed resource reservation, exploiting the localized nature of vehicle-to-vehicle communications. Initial simulation results, comparing the proposed LDRAS to a state-of-the-art radio resource management algorithm, are provided as a proof-of-concept and illustrate the benefits of our solution.

In this paper a novel mobility management scheme is proposed that is based on optical packet switching. Optical Packet based Mobility Management (OPMM) is proved to be more efficient for mobile traffic transport compared to conventional GPRS Tunneling Protocol (GTP) regarding packet overhead, transport latency and energy consumption. Meanwhile, OPMM reduces the complexity of the mobility management procedure. The system design is validated in a simulator and the performance is compared with the standard 3GPP implementation in realistic scenarios with a reference architecture.

Providing smooth Handover (HO) for video applications over wireless networks is often difficult due to the different amount of available resources from cell to cell. In this paper, we propose a QoE-based scheme that statically or dynamically reserves resources in order to maintain unperceivable quality fluctuation during HO for video delivery in LTE mobile networks. Through simulations, we show that reallocating the wireless resource in a cross-layer fashion is not enough to maintain the video quality within unperceivable range. Reserving fixed amount of resources solves the problem, however, it leads to inefficient resource utilization and overall service quality degradation. To reduce unnecessary resource reservation, we propose reserving the actual resource demand dynamically upon a prior knowledge of HOs. A comparison between our proposed schemes and a conventional QoE-based scheme is done by performing simulations using an OPNET LTE simulator. Results show that our proposed schemes lead to significant improvements of HO user-perceived quality.

This paper presents a Quality of Experience (QoE) driven approach for multi-user resource optimization in Dynamic Adaptive Streaming over HTTP (DASH) over next generation wireless networks. Our objective is to enhance the user experience in adaptive HTTP streaming by jointly considering the characteristics of the media content and the available wireless resources in the operator network. Specifically, we propose a proactive QoE-based approach for rewriting the client HTTP requests at a proxy in the mobile network. The advantage of the proposed approach is its applicability for over-the-top (OTT) streaming as it requires no adaptation of the media content. We compare our proposed scheme to both reactive QoE-optimized and to standard-DASH HTTP streaming. Our contributions are: 1) We first show that standard OTT DASH leads to unsatisfactory performance since the content agnostic resource allocation by the LTE scheduler is far from optimal, and we can achieve a clear QoE improvement when considering the content characteristics. 2) We additionally show that proactively rewriting the client requests gives control of the video content adaptation to the network operator which has better information than the client on the load and radio conditions in the cell. This results in additional gains in user perceived video quality. 3) A standard unmodified DASH client remains unaware of the proposed rewriting of the HTTP requests and can decode and play the redirected media segments.

Mounting capital and operational expenditure and reducing revenue per bit is straining the resources of mobile communication network operators. The future mobile communication networks will have to be dynamic; to adapt and re-adapt to the prevailing network conditions to increase operational efficiency. This paper presents the various use cases for such a Reconfigurable Mobile Network (RMN). We believe that the best way of currently achieving this reconfigurability is via virtualization techniques. Virtualization has been touted as the main solution that will ease the revolution to the future Internet and possibly result in a greener and more efficient operation of the current Internet. This paper investigates whether the concept of network virtualization can be re-applied to a mobile operator network to create RMN. We present detailed use cases for a reconfigurable mobile network and discuss the derived requirements on the RMN infrastructure. We see that a number of requirements, such as absolute isolation, may be technically infeasible in the current end-to-end mobile network.

Centralized approaches to solve resource allocation problems for wireless real-time multimedia communications have been intensively studied but require the availability of meta information about the multimedia content and channel information of all users. In this paper, we propose a Quality of Experience (QoE) driven bidding game for de-centralized uplink resource allocation among multiple mobile video producers. Different from previous works, the price per resource unit is defined on a Mean Opinion Score (MOS) scale and users bid for the resources that maximize their own utility function. Simulations in an LTE environment show the benefits of our distributed approach in terms of convergence time and QoE performance, compared to a centralized greedy scheme and a state-of-the-art game-theoretic approach.

Managing interference by Coordinated Multi-Point (CoMP) transmission/reception is an effective mechanism to achieve high data rates in future cellular networks, like Long Term Evolution (LTE)-Advanced. For CoMP, sets of Base Stations (BSs) have to be selected to jointly serve User Equipments (UEs). These sets are typically selected based on wireless characteristics only. However, using CoMP also poses strict capacity and latency requirements on the backhaul network, which are difficult to fulfill even with future optical technologies. Hence, these requirements additionally need to be taken into account when deciding which BSs jointly serve a given UE. We have developed a BSs selection heuristic for CoMP that takes into account both aspects: the wireless channels and the backhaul network status. This heuristic can also identify, for a particular wireless channel situation, which bottlenecks in the backhaul network make a desired BSs selection infeasible. We exploit this to dynamically adapt the backhaul network to the wireless requirements. We call this network reconfiguration. Our simulations show that the heuristic's solution quality is close to the optimum while execution time and memory consumption are reduced by multiple orders of magnitude compared to solving the problem via mathematical optimization. This allows real-world deployment of the heuristic. In addition, we simulate the network reconfiguration in a future backhaul network scenario based on Passive Optical Networks (PONs). The results illustrate how our approach helps to better exploit available backhaul resources.

A handful of proposals address the problem of bootstrapping a large DHT network from scratch, but they all forgo the standard DHT join protocols in favor of their own distributed algorithms that build routing tables directly. Motivating their algorithms, the proposals make a perfunctory claim that the standard join protocols are not designed to handle the huge number of concurrent join requests involved in such a bootstrapping scenario. Moreover, the proposals assume a pre-existing unstructured overlay as a starting point for their algorithms. We find the assumption somewhat unrealistic. We take a step back and reexamine the performance of the standard DHT join protocols. Starting with nothing other than a well-known bootstrap server, when faced with a large number of nodes joining nearly simultaneously, can the standard join algorithms form a stable DHT overlay? If so, how quickly? Our simulation results show that Chord and Kademlia's join protocols can actually handle the bootstrapping scenario quite well. For 10,000 nodes joining at a rate of 1,000 nodes per second, Chord and Kademlia took less than 20 and 15 seconds, respectively, to form a stable overlay. The Chord join protocol, however, requires a slight modification for fast bootstrapping. We elucidate the reason why the modification is necessary.

This paper proposes a novel approach for Quality of Experience (QoE) driven cross-layer optimization for wireless video transmission. We formulate the cross-layer optimization problem with a constraint on the temporal fluctuation of the video quality. Our objective is to minimize the temporal change of the video quality as perceivable quality fluctuations negatively affect the overall quality of experience. The proposed QoE scheme jointly optimizes the application layer and the lower layers of a wireless protocol stack. It allocates network resources and performs rate adaptation such that the fluctuations lie within the range of unperceivable changes. We determine corresponding perception thresholds via extensive subjective tests and evaluate the proposed scheme using an OPNET High Speed Downlink Packet Access (HSDPA) emulator. Our simulation results show that the proposed approach leads to a noticeable improvement of overall user satisfaction for the provided video delivery service when compared to state-of-the-art approaches.

The increasing popularity of user-generated content and the high quality upstreaming capabilities of mobile phones indicate a prevalence of video traffic in the uplink of next generation mobile net works. Need arises for optimizing the network resource allocation while preserving the user satisfaction. In this paper, we propose a service-centric approach for uplink distribution of real-time user generated content based on the Quality of Experience (QoE) and popularity of the video content. In case of limited network resources, the proposed approach assigns more resources for popular contents while maintaining a minimum guaranteed QoE for the less popular ones. We compare our service-centric approach with a QoE-driven one that does not consider video popularity and evaluate both approaches for the uplink of an LTE system. The simulation results show that a significant gain in terms of average user satisfaction can be achieved.

We consider the joint upstreaming of live and on-demand user-generated video content over LTE using a Quality-of-Experience driven approach. We contribute to the state-of-the-art work on multimedia scheduling in three aspects: 1) we jointly optimize the transmission of live and time-shifted video under scarce uplink resources by transmitting a basic quality in realtime and uploading a refined quality for on-demand consumption. 2) We propose a producer-consumer deadline-aware scheduling algorithm that incorporates both the physical state of the mobile producer (e.g., cache fullness) and the scheduled playout time at the end-user. 3) We show that the scheduling decisions in 1) and 2) can be determined locally for each mobile producer. We additionally present an analytical framework for de-centralized scalable video transmission and prove that there exists an optimal solution to our problem. Simulation results for LTE uplink further demonstrate the significance of our proposed optimization on the overall user experience.

Coordinated Multi-Point (CoMP) transmission/reception, and especially Joint Processing (JP), is a promising solution for managing interference in cellular mobile access networks. Its successful deployment, however, strongly depends on the capability of the backhaul infrastructure as strict capacity and latency requirements have to be fulfilled.

Coordinated Multi-Point (CoMP) transmission/ reception is a promising solution for interference management in wireless cellular systems. Its successful deployment, however, strongly depends on the capability of the mobile backhaul network architecture to support the capacity, latency, and synchronization requirements of cooperation. In this paper, we deal with the “feasibility” aspects related to CoMP transmission/reception. We analyze how different backhaul topologies and technologies can support Base Station (BS) cooperation. We study, for different traffic scenarios and backhaul connectivity levels, which BS clusters are actually feasible compared to the ones desirable from the Radio Access Network (RAN) perspective. We found out that a significant mismatch exists between the desired wireless cluster, as defined by the RAN, and the feasible one, as allowed by the backhaul characteristics. Based on these findings, we explore different approaches to this problem, highlighting how the adoption of layer-2 switching techniques and multicast capabilities can already improve the cooperation feasibility. Finally, we propose an algorithm that includes the backhaul network feasibility information in the wireless cluster formation process. As a result, our system avoids unnecessary signaling and user data exchange among BSs which would have not been eligible for taking part in the desired cooperative cluster.

In peer-to-peer content delivery systems, such as BitTorrent, there may exist nodes that are non-cooperative and do not contribute their upload bandwidth to the system while still downloading content from others. The current widely used countermeasures against this freeriding behavior have been shown to be ineffective. In this paper, we address the problem by leveraging the trust latent in the social networks and explicitly incorporating the social links as part of the BitTorrent content distribution infrastructure. Our extensive system evaluation produces several insights. First, the social network topology alone without the trackers is an efficient and scalable content distribution medium. Second, thanks to the cooperative social links, BitTorrent's robustness to freeriding significantly improves. Finally, we find that a hybrid solution in which peers download from both their friends and other peers obtained from the trackers has the highest robustness to freeriding, shortest download completion times and the most balanced upload bandwidth utilization.

Caching is a long investigated topic in the context of P2P networks and distributed hash tables (DHTs). Most of the proposed caching algorithms are user-centric in the sense that they focus on minimizing the search size or latency as the target metrics. In contrast, we take the network operator-centric view in this paper. We set the total traffic generated by the DHT search as the metric of interest and provide a simple algorithm to achieve a maximum reduction of the traffic. The key of the algorithm is that every node maintains statistics about routed queries and replicates its cached objects only when the popularity of queries for those objects are above a certain threshold. Through extensive simulations, we determine the optimal value of the popularity threshold and show the traffic reduction in comparison with state of the art DHT caching solutions. Our algorithm achieves an average traffic reduction of around 30% as compared to caching along the entire search paths, while maintaining good performance in terms of user-oriented measures such as the number of search hops, or equivalently, the search latency.

This paper explores the effect of varying peers' local cache sizes on user perceived video quality in Video-on-Demand p2p streaming. First, we ascertain the relationship between the average video distortion on peers and peer cache size. Second, we analyze the aggregate access network usage of uplink bandwidth for different peer cache sizes. Finally, we measure the p2p server bandwidth usage as the peer cache size is changed. After finding analytical fits to these three functions we compute the optimal peer cache size that minimizes the total cost of p2p VoD video streaming. Our extensive simulations highlight the key role peer cache sizes play in the interplay between the video quality delivered to users and the amounts of p2p server and peer bandwidth usage. Subsequent data analysis provided in this work presents a framework for computing an optimum operating point for p2p VoD systems in terms of server and peer bandwidth required.

We present a novel approach for the temporal synchronization of multiple videos which is based on cross-correlating bit rate profiles. The proposed scheme determines the temporal offset without major restrictions on viewing angles, camera properties and camera motion. We propose two extensions of the basic algorithm which reduce the influence of camera motion and distracting background objects. Additionally, we describe how to optimally combine different bit rate components in order to further improve the reliability of the synchronization scheme. The proposed approach, when combined with the three extensions, leads to a reliable, robust, and frame accurate temporal alignment of videos at remarkably low complexity.

Wireless ad hoc networks are inherently vulnerable, as any node can disrupt the communication of potentially any other node in the network. Many solutions to this problem have been proposed. In this paper, we take a fresh and comprehensive approach that addresses simultaneously three aspects: security, scalability and adaptability to changing network conditions. Our communication protocol, Castor, occupies a unique point in the design space: it does not use any control messages except simple packet acknowledgments, and each node makes routing decisions locally and independently without exchanging any routing state with other nodes. Its novel design makes Castor resilient to a wide range of attacks and allows the protocol to scale to large network sizes and to remain efficient under high mobility. We compare Castor against four representative protocols from the literature. Our protocol achieves up to two times higher packet delivery rates, particularly in large and highly volatile networks, while incurring no or only limited additional overhead. At the same time, Castor is able to survive more severe attacks and recovers from them faster.

This paper proposes a Quality of Experience (QoE) based rate adaptation scheme selection approach for multi-user wireless video delivery. Transcoding and packet dropping are used as examples of rate adaptation schemes, and we investigate their impact on user perceived video quality. In the presence of constrained computation resources, the most suitable rate adaptation scheme is determined for each video stream such that the overall quality degradation is minimized. The proposed scheme selection approach is integrated with QoE-based resource allocation in presence of constrained transmission resources. Simulation results obtained from an emulated High Speed Downlink Packet Access (HSDPA) show that the QoE-based approach leads to significant improvements of user perceived quality compared to other approaches including a non-optimized HSDPA systems

Locality-awareness is considered as a promising approach to increase the efficiency of content distribution by peer-to-peer (P2P) networks, e.g., BitTorrent. It is intended to reduce the inter-domain traffic which is costly for Internet service providers (ISPs) and simultaneously increase the performance from the viewpoint of the P2P users, i.e, shorten download times. This win-win situation should be achieved by a preferred exchange of information between peers which are located closely to each other in the underlying network topology. A set of studies shows that these approaches can lead to a win-win situation under certain conditions, and to a win-no lose situation in most cases. However, the scenarios used assume mostly homogeneous peer distributions and that all peers have the same access speed. This is not the case in practice according to several measurement studies. Therefore, we extend previous work in this paper by studying scenarios with real-life, skewed peer distributions and heterogeneous access bandwidths of peers. We show that even a win-no lose situation is difficult to achieve under those conditions and that the actual impact for a specific peer depends heavily on the used locality-aware peer selection and the concrete scenario. Therefore, we conclude that current proposals need to be refined so that users of P2P networks can be sure that they also benefit from their use. Otherwise, a broad acceptance of the concept of locality-awareness in the user community of P2P networks will not take place.

Thanks to its in-network drop-based adaptation capabilities, H.264 Scalable Video Coding is perceived as an effective approach for delivering video over networks characterized by sudden large bandwidth fluctuations, such as Wireless LANs. Performance may be boosted by the adoption of application-aware/cross-layer schedulers devised to intelligently drop video data units (NALUs), so that i) decoding dependencies are preserved, and ii) the quality perceived by the end users is maximized. In this paper, we provide a theoretical formulation of a QoE utility-optimal cross-layer scheduling problem for H.264 SVC downlink delivery over WLANs. We show that, because of the unique characteristics of the WLAN MAC operation, this problem significantly differs from related approaches proposed for scheduled wireless technologies, especially when the WLAN carries background traffic in the uplink direction. From these theoretical insights, we derive, design, implement and experimentally assess a simple practical scheduling algorithm, whose performance is very close to the optimal solution.

We are observing an increasing trend of connecting embedded sensors and sensor networks to the Internet and publishing their output on the Web. We believe that this development is a precursor of a Web of Things, which gives real-world objects and places a Web presence that not only contains a static description of these entities, but also their real-time state. Just as document searches have become one of the most popular services on the Web, we argue that the search for real-world entities (i.e., people, places, and things) will become equally important. However, in contrast to the mostly static documents on the current Web, the state of real-world entities as captured by sensors is highly dynamic. Thus, searching for real-world entities with a certain state is a challenging problem. In this paper, we define the underlying problem, outline the design space of possible solutions, and survey relevant existing approaches by classifying them according to their design space. We also present a case study of a real-world search engine called Dyser designed by the authors.

This paper presents mDHT, a novel architectural enhancement to DHT using multicast service discovery. In mDHT, a group of host computers in a subnet participate in a DHT overlay as a single node. A query is routed from subnet to subnet until it reaches the final destination subnet, where it is resolved among the hosts using link-local multicast. Under a reasonable deployment assumption, mDHT offers many benefits over standard DHTs, such as locality, easy bootstrapping, high availability, and near imperviousness to node churn.

Although hierarchical P2P systems have been found to outperform flat systems in many respects, current P2P research does not focus on strategies to build and maintain such systems. Available solutions assume either no or little coordination between peers, that could lead the system toward satisfying a globally defined goal (e.g., minimizing traffic). In this paper we focus on hierarchical DHTs and provide a full set of algorithms to build and maintain such systems, that mitigate this problem. In particular, given the goal state of minimizing the total traffic without overloading any peer, our algorithms dynamically adjust the system state as to keep the goal met at any time. The algorithms are fully decentralized and probabilistic, all decisions taken by the peers are based on their partial view on a set of system wide parameters. Thus, they demonstrate the main principle of self-organization - the system behavior emerges from local interactions. Our simulations, run in a range of realistic settings, confirm a good performance of the algorithms.

We propose a novel framework for cooperation among mobile prosumers (CAMP) that aims at establishing semantic relations between multimedia content acquired by mobile users, focussing especially on audio-visual media. We discuss the basic properties of such a system and describe a number of possible applications. We also address technical requirements and possible solutions, and show a preliminary implementation of a cooperative video sharing platform.

This paper describes the H.264 scalable video coding streaming evaluation framework (SVEF). This is the first open-source framework for experimental assessment of H.264 scalable video coding (SVC) delivery over real networks. Effectively adapting of the transport of an H.264 SVC stream to time-varying, bandwidth constrained, and loss prone networks is an important research area. However, very little experimental work has been performed due to the unavailability of real-time H.264 SVC players, the limitations of existing decoding software libraries when challenged with network-imparied received SVC streams (e.g., affected by random loss of Network Abstraction Layer Units - NALUs), and the lack of solutions for SVC streaming support. SVEF overcomes these issues by developing missing components and by integrating them in a hybrid online/offline experimental framework. We believe SVEF will be of significant help to the research community interested in experimentally benchmarking their own proposed SVC adaptation approaches and delivery mechanisms. As a proof-of-concept of SVEF, we provide the experimental performance evaluation of an SVC cross-layer in-network scheduler in a wireless LAN hot spot scenario.

In ProtoPeer, the developer can switch between the simulation of a P2P system to its deployment on the actual network without changing a single line of code. This dramatically speeds up the implement-evaluate-reimplement cycle and allows for rapid system prototyping. Most of the major bugs and performance problems are caught early on during the simulation while the more time-consuming live deployment is used for the accurate evaluation of the final system.

Simulators are a commonly used tool in peer-to-peer systems research. However, they may not be able to capture all the details of a system operating in a live network deployment. Transitioning from simulation to the actual system implementation is a non-trivial and time-consuming task, a problem that we propose to solve in this paper. We present ProtoPeer, a peer-to-peer systems prototyping toolkit that allows for switching between the event-driven simulation and live network deployment without changing any of the application code. ProtoPeer exports a set of APIs for message passing, message queuing, timer operations as well as overlay routing and managing the overlay neighbors. Users can plug in their own custom implementations of most of the parts of ProtoPeer including custom network models for simulation and custom message passing over transports other than the default TCP/UDP.

Our demonstration shows Chordella, a Peer-to-Peer System that is particularly designed for heterogeneous environments such as in wireless networks. We use a pool of standard personal computers distributed over the Internet which act as reliable nodes and form the backbone of the network as superpeers. The mobile nodes are represented by mobile phones running a lightweight implementation of our system able to use the superpeers as proxies. The demo shows the effectiveness of the approach with a mobile picture sharing application benefiting from several improvements such as Load Balancing and Optimal Operation Point Selection algorithms.

The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where high-level state information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects, creatures). A key service for the WoT is searching for entities which exhibit a certain dynamic state at the time of the query, which is a challenging problem due to the dynamic nature of the sought state information and due to the potentially huge scale of the WoT. Below we report on our initial efforts to construct such a search engine and the underlying WoT.

Hierarchical DHT (HDHT) systems, outperforming flat DHTs with respect to scalability and network locality, became an important P2P research area in recent years. Appropriate load balancing algorithms, which are available only for flat DHTs so far, are also required for the reliability and scalability of HDHTs. However, their impact is different. In HDHTs, failures caused by overloaded nodes in higher hierarchical layers affect larger portions of the network than overloaded nodes in lower layers. In comparison to flat DHTs, HDHTs offer an additional dimension of balancing load, i.e., through varying relevant parameters of the hierarchical organization. This makes load balancing in HDHTs significantly different from load balancing in flat DHTs. In this paper, we exploit this possibility and present a novel load balancing algorithm for a two-tier HDHT system. Analytically and by simulations we show that our algorithm provides good load balancing performance, while at the same time generating less overhead than, e.g., the renowned "power of two choices" algorithm.

The recent disruption of the Skype voice-over-IP system, triggered by a massive reboot of the hosts around the world, brought to light the importance of efficient bootstrapping in a large-scale peer-to-peer network. Thus far, the problem of bootstrapping a DHT network from a near-total failure has received limited attention from the research community. We present an outline of our plan to investigate DHT bootstrapping mechanisms for large-scale deployments on the Internet.

Cross-layer optimization (CLO) promises significant gains in comparison to a conventional system design, which does not allow for information exchange across layers. One of the key challenges in CLO is the exchange of parameters between optimizer and layers. In this paper a model-based approach is presented that drastically reduces the amount of parameters that are to be exchanged. The optimizer employs models of the respective layers that emulate the communication system within the optimizer. The layers then only need to pass a small number of model parameters to the optimizer. This general concept is applied to CLO between application (APP) layer and medium access control (MAC) layer of a radio communications system. Our proposed model for the MAC layer is suitable for a transmitter without instantaneous channel state information (CSI). Simulation results demonstrate that the proposed model-based CLO is able to exploit the available diversity to enhance the system capacity. Dependent on the application characteristics, the same perceived quality in terms of mean opinion score (MOS) is maintained, while increasing the number of served users by up to 25%. Compared to known CLO approaches, much fewer parameters need to be exchanged.

In this paper we introduce the concept of multiple description video transcoding (MDVT). MDVT converts a single description encoded video into two or more descriptions at an intermediate node in the network. The objective of our MDVT approach is to adapt the video transmission to a multi-radio environment where two or more independent transmission paths exist between the intermediate node and the receiver. The sender does not have to be aware of the transcoding process and the multi-path transmission. MDVT can for instance be applied for multi-mode terminals that are simultaneously connected to two wireless access technologies, e.g., UMTS and WLAN. We compare MDVT with multiple description coding at the sender (MDC-S) as well as with MDC at the intermediate node (MDC-I) where the incoming single description video is decoded and re-encoded into multiple descriptions and the transmission is optimized separately for each path. We present a fast greedy method that can be used to perform multiple description video transcoding in real-time at low complexity. Our experimental results show that we can achieve performance similar to MDC-S where the sender has to be aware of the availability of multiple paths. Compared to MDC-I we observe more than 2 dB gain in reconstruction quality.

We address the problem of rate allocation and network/path selection for multiple users, running simultaneous applications over multiple parallel access networks. Our joint optimization problem consists of finding the appropriate application rate allocation and network parameters for each individual user, such that an overall quality metric is maximized. We compare our solution to other solutions based on throughput optimization strategies through extensive simulations, and we show the superiority of our approach. Furthermore, our solution proves to be more robust in dynamic systems, when clients can join/leave the access networks.

Presence has taken shape as a way to present a comprehensive view of the communications capabilities of a user or resource. Standardized protocols allow for receiving presence data from a variety of sources, such as a user's communication devices, cellular provider, online calendar and sensors in his environment. This data can describe many different aspects of his communication capability, such as his device characteristics, activities, and even physical locations. The large number of presence sources may lead to irrelevant or inconsistent data. Also, the data may be incomplete and not as usable as it could be to the presence watcher. This paper proposes the use of presence composition to remove unusable data and create new more usable data. We discuss the details of this composition and present a format with which a user can specify a policy for composition.

Abstract — We address the problem of video streaming over multiple parallel networks. In the context of multiple users, accessing different types of applications, we are looking for efficient ways of allocating network resources and selecting network paths for each application, in order to maximize the overall systems performance. Our optimization joint problem consists of finding the appropriate application rate allocation and network parameters for each individual user, such that a universal system quality metric is maximized. A specific mapping between the requirements of each considered application and the overall quality metric is introduced, and our results are compared to other solutions based on throughput optimization strategies. The superiority and robustness of our approach is shown through extensive simulations in constant and dynamic systems, when clients can join/leave the access networks. Furthermore, we introduce heuristic algorithms which can obtain good results and are inexpensive in terms of computation and execution time. I.

Mobile object tracking is the process of tracking objects which are moving constantly in their environment and thus change their location and context. We present in this paper a decentralized data management solution based on peer-to-peer (P2P) concepts and in particular a distributed hash table (DHT). Sensor network nodes run a DHT system, to which tracked objects or sensors insert their data about the objects. This distributed data storage can be queried by the application according to the DHT principles in order to retrieve the current status of an object without stability or bottleneck problems. Moreover, for scalability, our concept comprises a two tier architecture where we separate local tracking systems, which deal with frequent updates in a constrained local environment, and global tracking. In this way a second tier DHT based P2P system interconnects all local P2P systems for a global query resolution.

Service usage in emerging ubiquitous environments includes seamless and personalized usage of public and private devices discovered in the vicinity of a user. In our work, we describe an architecture for device discovery, device configuration, and the transfer of active sessions between devices. The presented architecture uses the Session Initiation Protocol (SIP) as a standardized, widely used signaling protocol for IP-based multimedia services. Our solution includes support of simple existing devices, split of sessions between devices, user-control of location-based behavior, and handling of security and privacy concerns. We present the implementation and show the feasibility of our work with analytical evaluation and measurements.

This paper proposes a cross-layer optimization framework that provides efficient allocation of wireless network resources across multiple types of applications to maximize network capacity and user satisfaction. We define a novel optimization scheme based on the Mean Opinion Score (MOS) as the unifying metric. Our experiments, applied to scenarios where users simultaneously run three types of applications, such as realtime voice, video conferencing and file download, confirm that MOS-based optimization leads to significant improvement in terms of user perceived quality when compared to throughput-based optimization.

With the convergence of the Internet and the mobile communications world, Internet-based services may become available on any user device. In order to make efficient use of such a ubiquitous environment the user demands to access his personalized services from any place, anytime and on any device regardless of the type of access network. In particular, we consider the situation that a user transfers a running session between devices. We refer to this as session mobility. Providing such ubiquitous services transparently to the user, is not only challenging from a networking point of view but also poses severe requirements on the management operations and in particular accounting needed for a commercial service rollout. We describe an accounting architecture for session mobility. It allows the network operators to determine, collect and evaluate data on service usage when users transfer their ongoing communication session(s) from one device to another or a set of devices within its domain or across multiple operators' environments. In particular, we describe the interactions required between the signaling protocol and the accounting protocol in support of session mobility.

Flat DHT architectures have been the main focus of the research on DHT design so far. However, there have been also a number of works proposing hierarchical DHT organizations and pointing their advantages. They mostly rely on the intuitive understanding that hierarchy is desirable in any complex system. In this paper we formalize this intuition within a general cost-based framework. We provide a cost model of a specific hierarchical DHT organization composed of superpeers and leafnodes, and show that the costs of running the network are not necessarily minimized for flat DHT organization, providing thus a formal motivation for hierarchical DHTs. We further hint on what distributed algorithms can be applied in practice to reach optimal operating point of the network

We propose an application-driven multi-user resource allocation and frame scheduling concept for wireless video streaming. Our approach is based on joint optimization of the application layer, the data link layer and the physical layer. For this, key parameters from these three layers are abstracted. The abstracted parameters at the application layer describe the rate-distortion characteristics of the pre-encoded video streams. At the lower layers they describe the current transmission characteristics of all users. The outcome of the joint optimization leads to adaptive resource allocation at the lower layers and an adaptive decision on which frames to send on the application layer. We show that for our scenario the expected video quality at the client side can be described analytically which leads to low complexity joint optimization. The performance of our approach is demonstrated using a real-time testbed implementation.

Cross-layer design (CLD) is a new paradigm for network architecture that allows us to make better use of network resources by optimizing across the boundaries of traditional network layers. Previous work has shown that applying CLD to mobile multimedia communication systems may lead to significant performance improvements. In this paper we also consider the other side of the coin, i.e., the additional computation and communication overhead introduced by CLD. We evaluate the performance improvements and the cost of cross-layer optimization using a wireless multi-user video streaming example.

In this paper we study the case of situational reasoning on ontological descriptions in context-aware applications. We therefore discuss the logical foundations of W3C's standard ontology language OWL and examine how this modeling language can be used to express a user's situation. In a practical scenario and through the use of standard semantic Web components and toolkits, we exemplify how basic reasoning services can be utilized to build and maintain well-founded context models.

We present a location-based, ubiquitous service architecture, based on the session initiation protocol (SIP) and a service discovery protocol that enables users to enhance the multimedia communications services available on their mobile devices by discovering other local devices, and including them in their active sessions, creating a "virtual device." We have implemented our concept based on Columbia University's multimedia environment and we show its feasibility by a performance analysis.

The utilization of peer-to-peer (P2P) overlay networks on mobile ad hoc networks (MANET) introduces new services and possibilities such as content based routing and location based services (LBS). A simple layering of both networks is inefficient and does not scale well because the virtual P2P overlay network does not match the frequently changing physical network topology of the MANET. The mobile peer-to-peer (MPP) protocol stack offers a very promising concept by introducing a cross-layer communication channel between the physical network layer and the virtual P2P network layer. This reduces significantly the messaging overhead and increases the search success rate, as we can prove in this work. Therefore, we describe an implementation of the MPP protocol stack in the Network Simulator 2 (ns-2). With this implementation, we prove the significant performance improvements which can be achieved with MPP, to allow P2P networking in mobile ad hoc environments.

Growing numbers of pervasive devices are gaining access to the Internet. However, much of the existing rich multimedia content cannot be handled by mobile client devices with limited communication, processing, storage and display capabilities. In this paper, we propose new ways to enhance the universal access to multimedia content through Web Services and Semantic Web concepts. A semantic-based personalized delivery concept is drafted that makes use of these emerging technologies together with rather classical multimedia transcoding ideas. Instead of large, monolithic portal applications designed for multi-purpose adaptation and a single-source delivery, we propose to shift multimedia adaptation functionality to a portfolio of adequately selected Web Services. Web Services accessible through standard interfaces that allow for multimedia format conversion and composition can allow for a more flexible, application-independent adaptation and thus ease multimedia service provisioning essentially.

We describe a real-time LAN-based testbed that allows us to investigate the behavior of streaming media applications under various network conditions. For commercially available streaming media applications, we are interested to see how they perform over next-generation wireline and wireless networks. For future streaming media applications, the testbed is an invaluable tool for the development and verification of new algorithms. Our testbed implementation is based on Linux Divert Sockets and supports a straightforward integration of various packet erasure and delay models. Individual IP-packets are diverted to a user process where they are delayed or deleted according to the desired channel model. The testbed has been used to investigate the flow-control behavior of existing streaming media systems over wireless networks. Our experiments confirm that flow-control algorithms that consider lost packets to be the result of network congestion, as employed today in wireline streaming, are not suited for wireless networks, where loss is mainly due to link impairments.

Among the different solutions providing multimedia teleconferencing services over IP networks, two approaches are of major importance: H.323 standardized by the ITU-T and SIP standardized by the IETF. Some comparisons on a system level have been published, but the service architectures have rarely been addressed in the literature. In the near future an important driver for multimedia business will be the services and the mechanisms for their fast and efficient service development and deployment. We provide a comparison of the methods to implement services in SIP and H.323 focusing on the service architectures and their capabilities regarding the implementation of new features. While H.323 is still the more mature standard-albeit SIP tends to keep up-the two protocols have basic differences in the supplementary services architectures. H.323/H.450 has been defined as a sophisticated enterprise solution and is well suited to support complex multimedia calls involving widely interoperable supplementary services. SIP on the other side represents a more universal approach. SIP uses more generic syntax and semantics regarding feature definition and session description, which carries the danger to cause interworking problems. The advantage of SIP is its general applicability for signaling non-VoIP sessions for example the establishment of a PSTN session as standardized in the PINT approach.