Efficient topology management in future 6G networks is a fundamental challenge for dynamic network creation based on location services, where each autonomous sub-network can be tailored to specific application scenarios. This paper studies the performance of a novel topology change management system in a 6G network dynamically organized into autonomous sub-networks. We propose and analyze an algorithm for intelligent prediction of topology changes and compare it with a monitoring-based approach. A case study on edge video distribution, aligned with 3GPP and ETSI MEC (Multi-access Edge Computing) standards, demonstrates the system's practical relevance. The proposed topology change prediction algorithm optimizes and selects the best machine learning models based on the scenario under study. For link change scenario, the results show that ANN demonstrates the best performance in identifying cases with no changes, slightly outperforming random forest and XGBoost. For user mobility scenario, XGBoost is more efficient in learning patterns for topology change prediction. In terms of cost efficiency, our ML-based approach represents a significantly cost-effective alternative to traditional monitoring approaches.

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.

Visual SLAM is an essential tool in diverse applications such as robot perception and extended reality, where feature-based methods are prevalent due to their accuracy and robustness. However, existing methods employ either hand-crafted or solely learnable point features and are thus limited by the feature attributes. In this paper, we propose incorporating hybrid point features efficiently into a single system. By integrating hand-crafted and learnable features, we seek to capitalize on their complementary attributes in both key-point identification and descriptor expressiveness. To this purpose, we design a pre-processing module, which includes extraction, inter-class processing, and post-processing of hybrid point features. We present an efficient matching approach to exclusively perform the data association within the same class of features. Moreover, we design a Hybrid Bag-of-Words (H-BoW) model to deal with hybrid point features in matching and loop-closure-detection. By integrating the proposed framework into a modern feature-based system, we introduce HPF-SLAM. We evaluate the system on EuRoC-MAV and TUM-RGBD benchmarks. The experimental results show that our method consistently surpasses the baseline at comparable speed.

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 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.

Real-time pose estimation is crucial for autonomous agents for motion control and navigation, and it is essential for extended reality applications as well. As the current global positioning systems are not reliable indoors or are not precise enough, visual simultaneous localization and mapping is becoming prevalent in autonomous agents and mobile devices. The visual feature maps can be shared between agents and might be merged into a common map on a server and redistributed to all clients to enable re-localization and co-localization. However, merged maps can grow continuously in density and size that constrained agents are not able to handle anymore.In this paper, we investigate how the map density effects localization and visual odometry performance on low-cost mobile clients. We show that a sparse representation of the original VFM is necessary for real-time visual odometry but at the cost of a degraded re-localization performance.

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.

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.

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.

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 advent of programmable network switch ASICs and recent developments on other programmable data planes (NPUs, FPGAs) drive the renewed interest in network data plane programmability. The P4 language has emerged as a strong candidate to describe a protocol independent datapath pipeline. With its supported architectures, the P4 language provides an excellent way to define the packet processing and forwarding behavior, while leaving other networking components such as the traffic management engine, to non-programmable fixed function elements, based on the capabilities of most programmable devices. However, network flexibility is essential to meet the Quality of Service (QoS) requirements of traffic flows. Thus, enabling programmable control for fixed-function elements like traffic management is crucial. Towards that end we propose the use of virtual queues in the P4 pipeline, investigate the application of virtual queue-based traffic management, and portability of the approach using different P4 programmable targets. Specifically, we focus on virtual queue based Active Queue Management (AQM) for congestion policing and meeting the latency targets of distinct network slices. The solution is compared to P4 built-in functionality for bandwidth management using meters, proving also that the additional dimensions of control are achieved without compromising the processing complexity of the solution.

In recent years, a variety of visual SLAM (Simultaneous Localization and Mapping) systems have been proposed. These systems allow camera-equipped agents to create a map of the environment and determine their position within this map, even without an available GNSS signal. Visual SLAM algorithms differ mainly in the way the image information is processed and whether the resulting map is represented as a dense point cloud or with sparse feature points. However, most systems have in common that a high computational effort is necessary to create an accurate, correct and up-to-date pose and map. This is a challenge for smaller mobile agents with limited power and computing resources. In this paper, we investigate how the processing steps of a state-of-the-art feature-based visual SLAM system can be distributed among a mobile agent and an edge-cloud server. Depending on the specification of the agent, it can run the complete system locally, offload only the tracking and optimization part, or run nearly all processing steps on the server. For this purpose, the individual processing steps and their resulting data formats are examined and methods are presented how the data can be efficiently transmitted to the server. Our experimental evaluation shows that the CPU load can be reduced for all task distributions which offload part of the pipeline to the server. For agents with low computing power, the processing time for the pose estimation can even be reduced. In addition, the higher computing power of the server allows to increase the frame rate and accuracy for pose estimation.

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.

A convincing augmented reality (AR) experience requires vast computational resources, in particular for three-dimensional mapping of the environment, pose estimation and high-quality rendering of virtual objects. Even today's most powerful mobile devices can not provide such computational resources and consequently limit the achievable quality of the augmentation. To tackle this issue, all computations necessary for AR can be offloaded to the Edge Cloud, such that the mobile device merely acts as a camera and display. This approach introduces additional processing steps, namely video communication, which we carefully evaluate with respect to their influence on the quality of experience and energy consumption. In the evaluation of our prototype, we show that with a Glass-to-Glass delay of about 85 ms, our implementation is competitive against state-of-the-art solutions which run completely locally on a mobile device. Most notably, the additional steps required for offloading contribute little delay, which is often overcompensated by the faster computations in the Edge Cloud. A further benefit is that compared to performing all AR processing locally, offloading reduces the energy consumption in smartphones on average by 50 %. Moreover, the computational resources available for the AR application increase by a factor 10 to 100 through offloading. Finally, offloading enables high-quality AR applications even in low-end mobile devices.

Media technology, in particular video recording and playback, keeps improving to provide users with high-quality real and virtual visual content. In recent years, increasing the temporal sampling rate of videos and the refresh rate of displays has become one focus of technical innovation. This raises the question, how high the sampling and refresh rates should be? To answer this question, we determine the minimum temporal sampling rate at which a video should be presented to make temporal sampling imperceptible to viewers. Through a psychophysical study, we find that this minimum sampling rate depends on both the speed of the objects in the image plane and the exposure time of the recording camera. We propose a model to compute the required minimum sampling rate based on these two parameters. In addition, state-of-the-art video codecs employ motion vectors from which the local object movement speed can be inferred. Therefore, we present a procedure to compute the minimum sampling rate given an encoded video and camera exposure time. Since the object motion speed in a video may vary, the corresponding minimum frame rate is also varying. This is why the results of this paper are particularly applicable when used together with adaptive frame rate computer generated graphics or novel video communication solutions that drop insignificant frames. In our experiments, we show that videos played back at the minimum adaptive frame rate achieve an average bit rate reduction of 26% compared to constant frame rate playback, while perceptually no difference can be observed.

The Network Function Virtualization (NFV) paradigm is enabling flexibility, programmability and implementation of traditional network functions into generic hardware, in form of the so-called Virtual Network Functions (VNFs). Today, cloud service providers use Virtual Machines (VMs) for the instantiation of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a typical scenario of Service Function Chains (SFCs), many important objectives need to be met simultaneously, such as server load balancing, energy efficiency and service execution time. The well-known VNF placement problem requires solutions that often consider migration of virtual machines (VMs) to meet this objectives. Ongoing efforts, for instance, are making a strong case for migrations to minimize energy consumption, while showing that attention needs to be paid to the Quality of Service (QoS) due to service interruptions caused by migrations. To balance the server allocation strategies and QoS, we propose using replications of VNFs to reduce migrations in DC networks. We propose a Linear Programming (LP) model to study a trade-off between replications, which while beneficial to QoS require additional server resources, and migrations, which while beneficial to server load management can adversely impact the QoS. The results show that, for a given objective, the replications can reduce the number of migrations and can also enable a better server and data center network load balancing.

More and more services are provided by large data centers with a potentially very large number of physical or virtual hosts. As the number of hosted services and service consumers increases, also the number of hosts inside a data center raises to cope with the increasing end-user demand. Current data center networks are usually based on Ethernet and mechanisms like load balancing or redundancy between data centers require a transparent connection of these Ethernet networks over a Wide Area Network (WAN). Due to the large number of hosts, these interconnected data center networks face scalability problems on different protocol layers. One such issue, which is currently discussed within the IETF, is the scalability of the link layer Address Resolution Protocol (ARP). This paper studies the control traffic caused by address resolution for interconnected data centers. We develop an analytical model for the ARP traffic between data center locations that takes into account the number of hosts and connected sites. This model can then be used to quantify the ARP traffic for a data center interconnect solution. As an example, we apply our model to Virtual Private LAN Services (VPLS). In addition, we study how an ARP proxy can improve the overall scalability, and we show that a proxy significantly reduces the ARP traffic at VPLS switches.

Since its introduction OpenFlow has been used as an enabler for network experiments in a variety of fields. Although OpenFlow was initially only used in the research domain, the concept is now finding its way into data centers due to the relatively cheap hardware and high flexibility. In this paper, we take a look at the scalability and usability of OpenFlow in data centers. Based on data center traffic models and OpenFlow measurements, we evaluate whether OpenFlow is able to cope with the short flow inter-arrival times of the traffic models by means of simulation.

No abstract available.

We recently witness new services that can afford some delay until data transmission starts, but then benefit from a very large available bandwidth. A popular example is the migration of virtual machines between different sites of a geographically dispersed service provider. In this paper, we propose a subscription model for time-scheduled data transfers. Transmission requests are served consecutively, giving the flows access to the physical bandwidth. This is in contrast to today's Internet where flows are served in parallel so that they compete for the available bandwidth. We present the architecture to enable such data beams. Furthermore, we model and analyze the performance under different conditions and compare it with concurrent transmission.

IEEE 802.11s is an emerging IEEE 802.11 amendment, aiming at standardizing wireless mesh networking. As congestion is a major problem in wireless mesh networks, IEEE 802.11s addresses this problem by introducing the intra-mesh congestion control. In order to explore the potential of this mechanism, we describe two different IEEE 802.11s compliant congestion control mechanisms and discuss their respective benefits and limitations. Results from a simulative evaluation demonstrate that intra-mesh congestion control is suitable for avoiding the loss of packets which have already been forwarded over the air interface and, if appropriately implemented, increases the overall network throughput. The results do however also point out the limitations of the proposed IEEE 802.11s congestion notification format.

The complex multi-hop structure of WMNs requires a careful network planning. In this paper, we investigate the usability of Genetic Algorithms (GAs) for such a planning approach. The simplicity of GAs allow us to examine a large number of network configurations in order to optimize the network throughput and to fairly distribute the resources. This is achieved with a max-min fair share throughput distribution and by evaluating node positions, routing configurations, and channel assignments. We adapt standard genetic operators and evaluate the influence of the operators on the performance. The results show that GAs are well-suited for planning WMNs.

The problem how to determine the capacity of and achieve fairness in mesh networks is one of the key topics in practical and theoretical research on mesh networks. Max-min fairness is one way to define fairness and several algorithms how to compute max-min fair rate allocations are already published. In this paper we make two major contributions to this area of research: First, we formulate an algorithm achieving max-min fairness among end-to-end flows based on the effective load of a collision domain. This allows us to determine max-min fair rate allocations in a multi-gateway, multi-channel mesh network with equal rates for all links. Second, we extend this algorithm for heterogeneous link rates.

Wireless Mesh Networks (WMNs) are gaining an increasingly important role in next generation last mile access. They offer more flexibility compared to traditional networks but on the expense of a complex structure. Thus, planning and optimization of WMNs is a challenge. In this paper we focus on routing and channel assignment in WMNs for throughput maximization using genetic algorithms. Genetic algorithms provide a good solution for large-scale WMNs in relatively small computation time. The results prove the effectiveness of the genetic operators and show the advantages of a genetic optimization. However, these operators have to be configured carefully to avoid local optima. We will show the influence of the selection principles as well as evaluation functions on the optimization.

The IEEE 802.16 standard specifies two contention-based mechanisms for the OFDMA physical layer to transmit bandwidth requests from subscriber station to base station: the standard mechanism is based on Slotted Aloha with a truncated binary exponential backoff; the alternative one is based on CDMA. This paper describes the CDMA-based contention mechanism and presents an analytic model to compute its performance in terms of delay and consumed resources. The tunable parameters for the CDMA-based random access procedure are the number of ranging subchannels, the number of codes per ranging subchannels, and the detection threshold. An optimal configuration is derived for a given load in terms of the request arrival rate.

Wireless LAN strongly prioritizes high priority traffic over low priority best effort traffic. This causes reduced access to the medium for low priority traffic and under some conditions even leads to starvation. To compensate the throughput reduction of low priority traffic, we propose frame bursting in this paper. That means low priority traffic is sent infrequently, but many frames may be sent in a burst. Our simulation results show that the throughput of the low priority best effort traffic class can be significantly increased without disrupting high priority traffic.

The increasing broadband wireless Internet usage and the limited wireless resources require a careful network management and optimization of the wireless Internet Service Providers (ISPs). Unfortunately, those providers often just have statistics about the overall usage and limited knowledge about the detailed application distribution as well as the traffic characteristics. In this paper we present user and traffic characteristics measured at a broadband wireless Internet access. The results show that the applications change quickly but the general characteristics like packet size and TCP/UDP percentage have not changed during the last years.

In urban areas, users have become more and more accustomed to the availability of broadband access. However, in rural and suburban areas, it is often too expensive for network providers to serve every user with traditional wired broadband access such as DSL or cable modem. In such areas, WiMAX (worldwide interoperability for microwave access) networks based on the IEEE 802.16 standard are the most promising solution. In this paper, we focus on the performance of the IEEE 802.16 time division duplex (TDD) mode in rural areas with only one cell. When using TDD, the duration of the downlink and uplink subframe can be set individually, which is called adaptive time division duplex (ATDD). However, the ratio between downlink and uplink is normally set to a fixed value. We will show the performance of different fixed downlink/uplink ratios for several traffic models. Furthermore, we propose an algorithm for an adaptive downlink/uplink boundary in dependence on the current traffic load in the cell. The results show an enormous performance gain compared to the fixed settings.

WiMAX (Worldwide Interoperability for Microwave Access) is a wireless access technology that aims to provide last mile wireless broadband access for fixed and mobile users as an alternative to the wired DSL and cable access. It is specified in the IEEE 802.16 standard. The standard defines several possible bandwidth request methods that can be implemented in an actual deployment of a WiMAX network. In this paper, we will study the performance of two different bandwidth request mechanisms, namely piggyback and broadcast requests and will show in which situations piggybacking performs better than the contention based broadcast bandwidth requests.

The Global System for Mobile Communications (GSM) is the most popular standard for mobile phones in the world with about 1.57 billion customers. The short message service (SMS) in GSM allows amongst others the transmission of short text messages to mobile phones. In case of an emergency, the SMS can be used to warn a large number of individuals. However, the short message delivery is performed on best-effort basis and no quality of service (QoS) agreement is given, i.e., there is no guarantee how long it takes a message to reach the recipient or that the message will be successfully delivered. In this paper, we investigate the message loss probability and the transmission time by performing measurements with different scenarios in a public GSM network. Then, we identify problems according to the requirements for an emergency warning system. We present a solution based on the cell broadcast in GSM and show numerical results regarding the delay and the capacity of the SMS