While traditional handovers (THOs) have served as a backbone for mobile connectivity, they increasingly suffer from failures and delays, especially in dense deployments and high-frequency bands. To address these limitations, 3GPP introduced Conditional Handovers (CHOs) that enable proactive cell reservations and user-driven execution. However, both handover (HO) types present intricate trade-offs in signaling, resource usage, and reliability. This paper presents unique, countrywide mobility management datasets from a top-tier mobile network operator (MNO) that offer fresh insights into these issues and call for adaptive and robust HO control in next-generation networks. Motivated by these findings, we propose CONTRA, a framework that, for the first time, jointly optimizes THOs and CHOs within the O-RAN architecture. We study two variants of CONTRA: one where users are a priori assigned to one of the HO types, reflecting distinct service or user-specific requirements, as well as a more dynamic formulation where the controller decides on-the-fly the HO type, based on system conditions and needs. To this end, it relies on a practical meta-learning algorithm that adapts to runtime observations and guarantees performance comparable to an oracle with perfect future information (universal no-regret). CONTRA is specifically designed for near-real-time deployment as an O-RAN xApp and aligns with the 6G goals of flexible and intelligent control. Extensive evaluations leveraging crowdsourced datasets show that CONTRA improves user throughput and reduces both THO and CHO switching costs, outperforming 3GPP-compliant and Reinforcement Learning (RL) baselines in dynamic and real-world scenarios.

Serverless computing promises fine-grained resource elasticity and billing, making it an attractive way to build complex applications as multi-stage workflows. Nonetheless, existing workflow orchestration ignores the heterogeneous demands of the computation and communication parts within a stage, potentially resulting in resource inefficiency on either side. In this paper, we advocate forcomputation-communication-separated orchestrationto unleash hybrid resource (i.e., compute and network) elasticity. We present HyFaaS, a serverless workflow orchestrator that improves performance while ensuring cost efficiency. It seamlessly decouples computation and communication as a series of hybrid stages re-expressed within HyDAG, a novel workflow abstraction. HyFaaS uses a gray-box profiling model to identify their Pareto-optimal saturated configurations, and then deploys the saturated workflow to juggle communication and scaling overheads through two-level HyDAG partitioning. Along with event-driven runtime fine-tuning, HyFaaS further scales down the non-critical stages to reduce cost via branch-aware coordination. Experimental results show that HyFaaS surpasses existing solutions by 32.7%–50.4% on end-to-end latency, while lowering cost by up to 1.37×.

Programmable data plane devices have enabled various in-network applications that rely on locally stored state for delivering low-latency and high-throughput services. However, these applications are susceptible to network failures, which can disrupt state access and network functionality. Timely and reliable failure detection is therefore a critical component of a stateful data plane. In this paper, we propose a data plane framework, FASTR, that enables microsecond-scale fast failure detection between directly connected switches. FASTR can achieve sub- $10 \mu$ s detection latency by implementing a heartbeat mechanism in the data plane. In addition, FASTR also incorporates traffic-awareness to reduce overhead and priority queuing to avoid false alarms. We validate FASTR with hardware experiments, demonstrating that it can consistently detect failures within $10 \mu$ s using a $4 \mu$ s interval while remaining robust to network congestion.

The emergence of softwarized network devices, like programmable switches and smart NICs, has brought about new and advanced network functionalities. Intelligent decision-making becomes possible at line rate by offloading network functionality from the network control-plane to the programmable data-plane. In this paper, we offload fine-grained Distributed Denial of Service (DDoS) attack detection to the data-plane. The state-of-the-art in this regard, mainly aims to embed Machine Learning (ML) models into the data-plane without compromising on inference accuracy. Besides accuracy, we must consider multiple other factors, like traffic feature availability and false positive rates. To that end, we propose O’MINE: ONE MODEL IS NOT ENOUGH, a novel collaborative detection mechanism comprising lightweight ML models. This maximises the detection accuracy while keeping the false positive rate (FPR) low. We use three state-of-the-art datasets to evaluate the O’MINE algorithm and its ML models. Our results show that O’MINE can detect DDoS attacks with high accuracy (≈98% and ≈96% with full and scarce training data, respectively) and low FPR (≈0.22% and ≈0.72% with full and scarce training data, respectively), outperforming the state-of-the-art. Lastly, O’MINE only consumes a few device resources (≈6% of LUT and ≈4% of FF) on the Xlinx Alevo U250 FPGA we have used for inference at line rate.

Telecommunication services are essential in ensuring the operation of numerous critical infrastructures. While mobile network security increased with the advancement of generations, emerging concepts such as the Open Radio Access Network (O-RAN) are transforming the traditional operation of Radio Access Networks (RANs). Novel concepts and technologies are finding their way into RANs with a focus on softwareization and virtualization. This increases the overall attack surface and introduces new attack vectors not necessarily found in traditional RANs. This paper shows that Denial of Service (DoS) attacks leveraging subscription mechanisms can compromise O-RAN implementations. We present a novel DoS attack targeting the Near Real-Time (Near-RT) RAN Intelligent Controller (RIC). By deploying a malicious xApp, we demonstrate how an adversary can flood the Near-RT RIC with excessive subscription requests, leading to service disruption. This attack exploits the lack of rate-limiting mechanisms within the Service Model (SM), a critical component of the Near-RT RIC responsible for handling E2 subscription requests. We systematically evaluate various attack scenarios and investigate the underlying vulnerabilities exposed. Furthermore, we propose and assess countermeasures to safeguard publicly accessible O-RAN systems from such threats.

System call interposition is a widely used technique to trace and modify application behavior. Over the years, numerous interposition mechanisms have been proposed, each with distinct strengths and trade-offs. Recently, advances in binary rewriting—specifically targeting x86–64 syscall and sysenter instructions—have led to new techniques that take important steps forward, with some claiming to support general-purpose use.

Handovers (HOs) are the cornerstone of modern cellular networks for enabling seamless connectivity to a vast and diverse number of mobile users. However, as mobile networks become more complex with more diverse users and smaller cells, traditional HOs face significant challenges, such as prolonged delays and increased failures. To mitigate these issues, 3GPP introduced conditional handovers (CHOs), a new type of HO that enables the preparation (i.e., resource allocation) of multiple cells for a single user to increase the chance of$\mathbf{H O}$success and decrease the delays in the procedure. Despite its advantages, CHO introduces new challenges that must be addressed, including efficient resource allocation and managing signaling/communication overhead from frequent cell preparations and releases. This paper presents a novel framework aligned with the O-RAN paradigm that leverages meta-learning for CHO optimization, providing robust dynamic regret guarantees and demonstrating at least 180% superior performance than other 3GPP benchmarks in volatile signal conditions.

Open RAN systems, with their virtualized base stations (vBSs), offer increased flexibility and reduced costs, vendor diversity, and interoperability. However, optimizing the allocation of radio resources in such systems raises new challenges due to the volatile vBSs operation, and the dynamic network conditions and user demands they are called to support. Leveraging the novel O-RAN multi-tier control architecture, we propose a new set of resource allocation threshold policies with the aim of balancing the vBSs’ performance and energy consumption in a robust and provably optimal fashion. To that end, we introduce an online learning algorithm that operates under minimal assumptions and without requiring knowledge of the environment, hence being suitable even for “challenging” environments with non-stationary or adversarial demands and conditions. We also develop a meta-learning scheme that utilizes other available algorithmic schemes, e.g., tailored for more “easy” environments, by choosing dynamically the best-performing algorithm; thus enhancing the system’s effectiveness. We prove that the proposed solutions achieve sub-linear regret (zero optimality gap), and characterize their dependence on the main system parameters. The performance of the algorithms is evaluated with real-world data from a testbed, in stationary and adversarial conditions, indicating energy savings of up to 64.5% compared with several state-of-the-art benchmarks.

Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby providing only a partial view of HOs. This paper presents the first countrywide analysis of HO performance, from the perspective of a top-tier MNO in a European country. We collect traffic from approximately 40M users for 4 weeks and study the impact of the radio access technologies (RATs), device types, and manufacturers on HOs across the country. We characterize the geo-temporal dynamics of horizontal (intra-RAT) and vertical (inter-RATs) HOs, at the district level and at millisecond granularity, and leverage open datasets from the country's official census office to associate our findings with the population. We further delve into the frequency, duration, and causes of HO failures, and model them using statistical tools. Our study offers unique insights into mobility management, highlighting the heterogeneity of the network and devices, and their effect on HOs.

Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed; one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facilitate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusability between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid.

The conventional definition of fairness in congestion control is flow rate fairness. However, Internet users typically care about flow completion times (FCTs) and flow rate fairness does not lead to equitable FCTs for different users. Therefore, we reconsider what it means for congestion control to be fair and posit a novel stance on fairness: it is fair when no flow unnecessarily prolongs another flow. Based on this stance, we propose an evaluation framework for congestion control fairness that uses slowdown (normalized FCT) as the metric.

Recent years have witnessed video streaming gradually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the serverless computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel computing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimization scheme to address video bitrate adaptation. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state-of-the-art solutions.

To cater to constantly changing network needs, enabling stateful reconfiguration of Network Functions (NFs) is crucial. Recently, there has been growing interest in offloading NFs to programmable network devices. Unfortunately, it is currently not possible to maintain the full state of NFs during a switch reconfiguration without consuming network resources from and to neighboring switches. In this paper, we present State4, a framework that maintains the state of P4 programs during the reconfiguration of a P4-programmab1e network device, by only using a small amount of local resources on the switch undergoing reconfiguration. State4 acts on both the in-switch control-plane and the data-plane. By utilizing the in-switch local controller, State4 requires no external network resources to achieve reconfiguration while preserving states. As such, State4 enables on-the-fly reconfiguration of stateful NFs, at minimal traffic disruption, where previously traffic had to be re-routed.

With the emergence of remote education and work in universities due to COVID-19, the 'zoomification' of higher education, i.e., the migration of universities to the clouds, reached the public discourse. Ongoing discussions reason about how this shift will take control over students' data away from universities, and may ultimately harm the privacy of researchers and students alike. However, there has been no comprehensive measurement of universities' use of public clouds and reliance on Software-as-a-Service offerings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and October 2022. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K., the Netherlands, THE Top100) frequently outsources universities' core functions and services---starting long before the COVID-19 pandemic. We attribute this clustering to several socio-economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals' privacy towards questions of academic independence and integrity.

Tactile Internet based Cyber-Physical Systems (TCPS) are highly sensitive to component and communication latencies and packet drops. Building a high performing TCPS, thus, necessitates experimenting with different hardware, algorithms, access technologies, and communication protocols. To facilitate such experiments, we have developed TCPSbed, a modular testbed for TCPS. TCPSbed facilitates the integration of different components, both real and simulated, to realize different TCPS applications and evaluate their latency and control performances. TCPSbed’s latency analyzer tool employs a novel method to isolate latencies of individual TCPS components such as the latencies contributed by actuation, sensing, algorithms, and by the network, all in an online fashion. TCPSbed’s method of analyzing stability is also novel. It involves the use of the step response analysis method, a classic control-theoretic method used for analyzing the stability of generic control systems. TCPSbed’s support for edge intelligence modules enables prediction of command and feedback signals at the network’s edge allowing TCPS applications to perform well in adverse network conditions. TCPSbed’s source-code, made available through our GitHub pageTactileInternet, allows developers to extend its features and functionalities further. In this paper, we describe the architecture and implementation details of TCPSbed and demonstrate its features through several proof-of-concept experiments.

Network outages have significant economic and societal costs. While network operators have become adept at managing smaller failures, this is not the case for larger, regional failures such as natural disasters. Although it is not possible, and certainly not economic, to prevent all potential disaster damage and impact, we can reduce their impact by adding cost-efficient, geographically redundant, cable connections to the network.In this paper, we provide algorithms for finding cost-efficient, disaster-aware cable routes based on empirical hazard data. In contrast to previous work, our approach finds disaster-aware routes by considering the impact of a large set of input disasters on the network as a whole, as well as on the individual cable. For this, we propose the Disaster-Aware Network Augmentation Problem of finding a new cable connection that minimizes a function of disaster impact and cable cost. We prove that this problem is NP-hard and give an exact algorithm, as well as a heuristic, for solving it. Our algorithms are applicable to both planar and geographical coordinates. Using actual seismic hazard data, we demonstrate that by applying our algorithms, network operators can cost-efficiently raise the resilience of their network and future cable connections.

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-of-the-art in terms of network utility, delay, and cost.

Networks often need to concurrently process millions of flows with varying Quality-of-Service (QoS) requirements. Doing so by deploying flow-specific rules at network nodes would require significant memory and overhead.In this paper, we take a fundamentally different approach, called P4QoS, by embedding QoS requirements in the packets themselves and leveraging P4-programmable network switches to process the traffic based on them. We illustrate and evaluate our approach with latency as our QoS metric, but our concept can be applied to other metrics as well. Our evaluation, both in software (Mininet) and in hardware (Intel Tofino), shows that P4QoS can satisfy application-specific QoS requirements with negligible memory overhead.

The concept of network slicing enables operators to provision multiple virtual networks on top of a single (shared) physical infrastructure. Adding elasticity to slicing, i.e., the ability to on-demand provision/release dedicated network resources, improves resource utilization. However, efficiently allocating and scaling slice resources, while maintaining state consistency, is challenging. Especially with P4-programmab1e network devices that process packets at Tbps speeds, controller-driven scaling of network functions would be too time-consuming, and data-plane scaling is needed. In this paper, we address this need, by developing a custom scaling protocol and framework that can consistently, with negligible delay, scale network slices and functions transparently to the slice end-users. We compare, via emulation and experiments on programmable hardware, our approach to state-of-the-art scaling techniques and demonstrate significant slice resource utilization improvements and scaling duration reductions.

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks. However, SDN is not widely adopted in constrained networks, such as Wireless Sensor Networks (WSN), due to excessive control overhead, lossy medium, and in-band control channels. Therefore, a key challenge to enable Software-Defined Wireless Sensor Networks (SD-WSN) is to reduce the number of control messages required to configure the data plane. In this paper, we propose a cluster-based flow control approach in hybrid SDNs. Our approach is hybrid in the sense that it takes advantage of distributed legacy routing and centralized SDN routing. In addition, it makes a trade-off between the granularity of flow control and the communication overhead induced by the SDN controller. The approach partitions a network into clusters with minimum number of border nodes. Instead of handling the individual flows of each node, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes, while the flows inside each cluster are controlled by a distributed legacy WSN routing algorithm. Our proof-of-concept implementations in both software and hardware show that our approach is efficient with respect to reducing the number of nodes that must be managed and the number of control messages. In comparison to benchmark solutions with and without clustering, our solution reduces communication costs for flow configuration in an SD-WSN at least by 27% and at most by 88% respectively, without degrading packet delay nor delivery rate.

To evaluate the expected availability of a backbone network service, the administrator should consider all possible failure scenarios under the specific service availability model stipulated in the corresponding service-level agreement. Given the increase in natural disasters and malicious attacks with geographically extensive impact, considering only independent single component failures is often insufficient. This paper builds a stochastic model of geographically correlated link failures caused by disasters to estimate the hazards an optical backbone network may be prone to and to understand the complex correlation between possible link failures. We first consider link failures only and later extend our model also to capture node failures. With such a model, one can quickly extract essential information such as the probability of an arbitrary set of network resources to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a disaster. Furthermore, we introduce standard data structures and a unified terminology on Probabilistic Shared Risk Link Groups (PSRLGs), along with a pre-computation process, which represents the failure probability of a set of resources succinctly. In particular, we generate a quasilinear-sized data structure in polynomial time, which allows the efficient computation of the cumulative failure probability of any set of network elements. Our evaluation is based on carefully pre-processed seismic hazard data matched to real-world optical backbone network topologies.

Congestion control algorithms are usually developed in isolation without thoroughly investigating their co-existence and interactions with other protocols and/or congestion control algorithms. As a result, flows using different algorithms and/or having different Round-Trip Times may overpower each other, resulting in unfair resource distribution, with a subset of the flows usually claiming most of the capacity. To solve the aforementioned problem, we make use of programmable switches and the network programming language P4 to enforce fairness from within the network itself, instead of from the congestion control algorithms ran at the end-points. Our solution P4air continuously monitors the properties of all flows that pass through a switch and groups them based on the behavior of the congestion control algorithms used. Furthermore, for each group, it applies appropriate measures to suppress the aggressive flows and boost smaller flows. Our experiments, using modern programmable hardware (Barefoot Tofino switch), demonstrate significant performance gains for P4air in terms of fairness compared to state-of-the-art solutions.

Low-power wireless networks are an integral part of the Internet of Things, composed of resourceconstrained devices harvesting ambient information.The appearance of unidirectional links is characteristic of low power wireless networking due to physical effects, device heterogeneity and manufacturing imperfections.Despite the prevalence of unidirectional links, most routing and radio duty cycling protocols designed for these networks do not account for such links.We provide unidirectional-link-capable protocols and study the impact of using such links on network performance indicators, such as the data delivery ratio, delay and energy consumption.Our protocols are flexible and flooding-free, leveraging centralized knowledge provided by the Software-Defined Networking paradigm.Our experiments reveal that, while unidirectional links must be detected, using them for routing enhances network performance only if the unidirectional links are long.

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurements---a common alternative---are impractical due to the large geographical areas involved.

Switches that can be (re)programmed through the network programming language P4 are able to completely change - even while in the field - the way they process packets. While powerful, P4 code is inherently static, as it is written and installed to accommodate a particular network requirement. Writing new P4 code each time new requirements arise may be complex and limits our agility to deal with changes in network traffic and services. In this paper, we present P4I/O, a new approach to data-plane programmability based on the philosophy of Intent-Based Networking. P4I/O provides an intent-driven interface that can be used to install and/or remove P4 programs on the switches when needed and which is easy to use. In particular, to realize P4I/O, we (1) describe an extensible Intent Definition Language (IDL), (2) create a repository of P4 code templates, which are parsed and merged based on the intents, (3) provide a technique to realize the resulting P4 program in a programmable switch, while accommodating intent modifications at any time, and finally (4) implement a proof-of-concept to demonstrate that intent modifications can be done on-the-fly.

It is often assumed that a network will not be struck by multiple disasters in a relatively short period of time; that is, a subsequent disaster will not strike within the recovery phase of a previous disaster. However, recent events have shown that combinations of disasters are not implausible. This realization calls for a new perspective on how we assess the vulnerability of our networks and shows a need for a framework to assess the vulnerability of networks to successive independent disasters. We propose a network and disaster model capable of modeling a sequence of disasters in time, while taking into account recovery operations. Based on that model, we develop both an exact and a Monte Carlo method to compute the vulnerability of a network to successive disasters. By applying our approach to real empirical disaster data, we show that the probability of a second disaster striking the network during recovery can be significant even for short repair times. Our framework is a first step towards determining the vulnerability of networks to such successive disasters.

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks, for instance in terms of reduced management complexity and more fine-grained traffic engineering. However, the design and implementation of SDN within wireless sensor networks (WSN) have received far less attention. Unfortunately, because of the multi-hop type of communication in WSN, a direct reuse of the wired SDN architecture could lead to excessive communication overhead. In this paper, we propose a cluster-based flow management approach that makes a trade-off between the granularity of monitoring by an SDN controller and the communication overhead of flow management. A network is partitioned into clusters with a minimum number of border nodes. Instead of having to handle the individual flows of all nodes, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes. Our proof-of-concept implementations in software and hardware show that, when compared with benchmark solutions, our approach is significantly more efficient with respect to the number of nodes that must be managed and the number of control messages exchanged.

In order to evaluate the expected availability of a service, a network administrator should consider all possible failure scenarios under the specific service availability model stipulated in the corresponding service-level agreement. Given the increase in natural disasters and malicious attacks with geographically extensive impact, considering only independent single link failures is often insufficient. In this paper, we build a stochastic model of geographically correlated link failures caused by disasters, in order to estimate the hazards a network may be prone to, and to understand the complex correlation between possible link failures. With such a model, one can quickly extract information, such as the probability of an arbitrary set of links to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a failure, etc. Furthermore, we introduce a pre-computation process, which enables us to succinctly represent the joint probability distribution of link failures. In particular, we generate, in polynomial time, a quasilinear-sized data structure, with which the joint failure probability of any set of links can be computed efficiently.

In this paper, we introduce NDNFlow: an open-source software implementation of a Named Data Networking based forwarding scheme in OpenFlow-controlled Software-Defined Networks (SDNs). By setting up an application-specific communication channel and controller layer parallel to the application agnostic OpenFlow protocol, we obtain a mechanism to deploy specific optimizations into a network without requiring a full network upgrade or OpenFlow protocol change. Our open-source software implementation consists of both an NDN-specific controller module and an NDN client plug-in. NDNFlow allows OpenFlow networks with NDN capabilities to exploit the benefits of NDN, by enabling the use of intermediate caches, identifying flows of content and eventually performing traffic engineering based on these principles.

Network-as-a-Service (NaaS) is a cloud-based service model that offers on-demand network connectivity and the provisioning and management of network services. However, the actual orchestration of dynamically allocating underlying resources to customer requirements is not trivial. In this paper, we propose an SDN-based approach to support the NaaS model. We implement a proof-of-concept (PoC) on a physical testbed and validate it through experimental performance evaluation.

In data‐communication networks, network reliability is of great concern to both network operators and customers. On the one hand, the customers care about receiving reliable services and, on the other hand, for the network operators it is vital to determine the most vulnerable parts of their network. In this article, we first study the problem of establishing a connection over at most (partially) link‐disjoint paths and for which the total availability is no less than ( ). We analyze the complexity of this problem in generic networks, shared‐risk link group networks and multilayer networks. We subsequently propose a polynomial‐time heuristic algorithm and an exact integer nonlinear program for availability‐based path selection. The proposed algorithms are evaluated in terms of acceptance ratio and running time. Subsequently, in the three aforementioned types of networks, we study the problem of finding a (set of) network cut(s) for which the failure probability of its links is largest. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(4), 306–319 2015

Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.

Due to their importance to society, communication networks should be built and operated to withstand failures. However, cost considerations make network providers less inclined to take robustness measures against failures that are unlikely to manifest, like several failures coinciding simultaneously in different geographic regions of their network. Considering networks embedded in a two-dimensional plane, we study the problem of finding a critical region-a part of the network that can be enclosed by a given elementary figure of predetermined size-whose destruction would lead to the highest network disruption. We determine that only a polynomial, in the input, number of nontrivial positions for such a figure needs to be considered and propose a corresponding polynomial-time algorithm. In addition, we consider region-aware network augmentation to decrease the impact of a regional failure. We subsequently address the region-disjoint paths problem, which asks for two paths with minimum total weight between a source (s) and a destination (d) that cannot both be cut by a single regional failure of diameter D (unless that failure includes s or d). We prove that deciding whether region-disjoint paths exist is NP-hard and propose a heuristic region-disjoint paths algorithm.

Solving network flow problems is a fundamental component of traffic engineering and many communications applications, such as content delivery or multi-processor scheduling. While a rich body of work has addressed network flow problems in "deterministic networks" finding flows in "stochastic networks" where performance metrics like bandwidth and delay are uncertain and solely known by a probability distribution based on historical data, has received less attention. The work on stochastic networks has predominantly been directed to developing single-path routing algorithms, instead of addressing multi-path routing or flow problems. In this paper, we study constrained maximum flow problems in stochastic networks, where the delay and bandwidth of links are assumed to follow a log-concave probability distribution, which is the case for many distributions that could represent bandwidth and delay. We formulate the maximum-flow problem in such stochastic networks as a convex optimization problem, with a polynomial (in the input) number of variables. When an additional delay constraint is imposed, we show that the problem becomes NP-hard and we propose an approximation algorithm based on convex optimization. Furthermore, we develop a fast heuristic algorithm that, with a tuning parameter, is able to balance accuracy and speed. In a simulation-based evaluation of our algorithms in terms of success ratio, flow values, and running time, our heuristic is shown to give good results in a short running time.

We present OpenNetMon, an approach and open-source software implementation to monitor per-flow metrics, especially throughput, delay and packet loss, in OpenFlow networks. Currently, ISPs over-provision capacity in order to meet QoS demands from customers. Software-Defined Networking and OpenFlow allow for better network control and flexibility in the pursuit of operating networks as efficiently as possible. Where OpenFlow provides interfaces to implement fine-grained Traffic Engineering (TE), OpenNetMon provides the monitoring necessary to determine whether end-to-end QoS parameters are actually met and delivers the input for TE approaches to compute appropriate paths. OpenNetMon polls edge switches, i.e. switches with flow end-points attached, at an adaptive rate that increases when flow rates differ between samples and decreases when flows stabilize to minimize the number of queries. The adaptive rate reduces network and switch CPU overhead while optimizing measurement accuracy. We show that not only local links serving variable bit-rate video streams, but also aggregated WAN links benefit from an adaptive polling rate to obtain accurate measurements. Furthermore, we verify throughput, delay and packet loss measurements for bursty scenarios in our experiment testbed.

It is important that our vital networks (e.g., infrastructures) are robust to more than single-link failures. Failures might for instance affect a part of the network that resides in a certain geographical region. In this paper, considering networks embedded in a two-dimensional plane, we study the problem of finding a critical region - that is, a part of the network that can be enclosed by a given elementary figure (a circle, ellipse, rectangle, square, or equilateral triangle) with a predetermined size - whose removal would lead to the highest network disruption. We determine that there is a polynomial number of non-trivial positions for such a figure that need to be considered and, subsequently, we propose a polynomial-time algorithm for the problem. Simulations on realistic networks illustrate that different figures with equal area result in different critical regions in a network.

With the increasing deployment of wavelength-division multiplexing (WDM) optical networks, the need for advanced lightpath provisioning algorithms and protocols in a multi-domain setting is becoming evident. In order to increase efficiency by relaxing the wavelength continuity constraint in WDM optical networks, wavelength converters are often placed at certain nodes in the network. In this paper, we study the efficiency of using converters in a multi-domain setting. We have made simple but important modifications to existing optical inter-domain routing protocols in order to utilize the power of wavelength converters and have tested their performance. These modifications can be seamlessly integrated into these protocols (i.e., without changing their algorithmic aspects) to significantly reduce their blocking ratio. We also show that there is a clear performance difference among the considered protocols.

Physical impairments, such as noise and signal distortions, negatively affect the quality of information transfer in optical networks. The effect of physical impairments predominantly augments with distance and bit rate of the signal to the point that it becomes detrimental to the information transfer. To reverse the effect of physical impairments, the signal needs to be regenerated at nodes that have regeneration capabilities. Regenerators are costly and are, therefore, usually only sparsely placed in the network, in which case it is referred to as a translucent network. This paper deals with two problems in translucent networks, namely: (1) how to incorporate impairment awareness in the routing algorithms, and (2) how many regenerators to place inside the network and where. We propose exact and heuristic algorithms for impairment-aware path selection and, through simulations, show that our heuristic TIARA is computationally efficient and performs very close to our exact algorithm EIARA. Subsequently, we propose a greedy algorithm for placing regenerators that, contrary to previous proposals, is suitable for multiple impairment metrics, has polynomial complexity for a single impairment metric, and is cheaper in terms of the number of regenerators needed.

Recently, there has been a growing interest in academic and commercial environments for video-on-demand (VoD) using peer-to-peer (P2P) technology. Unlike centralized solutions for VoD services, P2P technology lets the clients distribute video content among themselves. In this paper,we propose an analytical model for P2PVoD and we compare that model to a realistic P2PVoD simulator. With our model, parameters that affect the system performance can be observed, and the system stability can be investigated. Our model leads to design rules for achieving a good and stable system performance. This work is, to our knowledge,the first analytical work to model mesh-based P2PVoD.

Dynamic networks appear in several contexts: QoS rout-ing faces the difficult problem of accurately and efficiently maintaining, distributing and updating network state infor-mation, and in wireless ad hoc networking, signal strength fluctuations complicate the choice of stable paths. In this paper we will focus on the stability of paths in a network with dynamically changing link weights. The level of path stability has a direct relation to the number of updates that are necessary to maintain an accurate view of the network state. If a small change in the network state does not affect the shortest path, then such a change need not be distrib-uted throughout the network. We evaluate path stability by adding noise and observing the change in paths.

Finding a path in a network based on multiple constraints (the MCP problem) is often considered an integral part of quality of service (QoS) routing. QoS routing with constraints on multiple additive measures has been proven to be NP-complete. This proof has dramatically influenced the research community, resulting into the common belief that exact QoS routing is intractable in practice. However, to our knowledge, no one has ever examined which "worst cases" lead to intractability. In fact, the MCP problem is not strong NP-complete, suggesting that in practice an exact QoS routing algorithm may work in polynomial time. The goal of this paper is to argue that in practice QoS routing may be tractable. We will provide properties, an approximate analysis, and simulation results to indicate that NP-completeness hinges on four conditions, namely: 1) the topology; 2) the granularity of link weights; 3) the correlation between link weights; and 4) the constraints. We expect that, in practice, these conditions are manageable and therefore believe that exact QoS routing is tractable in practice.

The underlying concepts of an exact QoS routing algorithm are explained. We show that these four concepts, namely 1) nonlinear definition of the path length; 2) a /spl kappa/-shortest path approach; 3) nondominance; and 4) look-ahead, are fundamental building blocks of a multiconstrained routing algorithm. The main reasons to consider exact multiconstrained routing algorithms are as follows. First, the NP-complete behavior seems only to occur in specially constructed graphs, which are unlikely to occur in realistic communication networks. Second, there exist exact algorithms that are equally complex as heuristics in algorithmic structure and in running time on topologies that do not induce NP-complete behavior. Third, by simply restricting the number /spl kappa/ of paths explored during the path computation, the computational complexity can be decreased at the expense of possibly loosing exactness. The presented four concepts are incorporated in SAMCRA, a self-adaptive multiple constraints routing algorithm.

Finding a path in a network based on multiple constraints (the MCP problem) is often referred to as QoS routing. QoS routing with constraints on multiple additive metrics has been proven to be NP-complete. This proof has dramatically influenced the research community, resulting in the common belief that exact QoS routing is intractable in practice. Hence, many heuristics for this problem were proposed, while hardly any exact algorithms. However, to our best knowledge, no one has ever examined which "worst-cases" cause NP-complete behavior. In fact, the MCP problem is not strong NP-complete, suggesting that in practice an exact QoS algorithm may work in polynomial time, making guaranteed QoS routing possible. The goal of this paper is to provide some properties and simulation results that indicate that NP-complete behavior hinges on a specific correlation structure between the link weights, which will be hardly ever encountered in practice.