Leveraging a dataset of almost half a billion packets with high-precision packet times and sizes, we extract characteristics of the bursts emitted over Starlink’s Ethernet interface. The structure of these bursts directly reflects the physical layer reception of OFDMA frames on the satellite link. We study these bursts by analyzing their rates, and thus indirectly also the transition between different physical layer rates. The results highlight that there is definitive structure in the transition behavior, and we note specific behaviors such as particular transition steps associated with rate switching, and that rate switching occurs mainly to neighboring rates. We also study the joint burst rate and burst duration transitions, noting that transitions occur mainly within the same rate, and that changes in burst duration are often performed with an intermediate short burst in-between. Furthermore, we examine the configurations of the three factors burst rate, burst duration, and inter-burst silent time, which together determine the effective throughput of a Starlink connection. We perform pattern mining on these three factors, and we use the patterns to construct a dynamic N-gram model predicting the characteristics of the next upcoming burst, and by extension, the short-term future throughput. We further train a Deep Learning time-series model which shows improved prediction performance.

The deployment of Cellular Internet of Things (CIoT) is expected to reach over six billion devices by 2030. Many of these devices will be located in remote areas where replacing or recharging their batteries would be difficult and expensive. Therefore, it is crucial to configure these devices for efficient energy use to avoid frequent battery replacements or recharging. However, optimizing the energy consumption of CIoT devices, considering their applications and operating environmental conditions, presents a complex challenge. In response to this challenge, we propose the Gradient-Boosted Learning Optimization for Battery Efficiency (GLOBE) framework for dynamic configuration of Narrowband Internet of Things (NB-IoT) devices. GLOBE adjusts the radio layer of NB-IoT devices based on data transmission patterns and network conditions, enabling swift and automated reconfiguration. Our results demonstrate that GLOBE reduces energy consumption by 30% to 75% compared to baseline configurations, offering significant benefits for both network operators and end devices by improving energy efficiency.

The Narrowband Internet of Things (NB-IoT) technology has an important role in the mobile cellular ecosystem, enabling massive Machine Type Communication (mMTC) services. NB-IoT propagation was preliminarily analyzed via a measurement campaign carried out in 2020 in the city of Oslo, Norway. This investigation resulted in Oslo-2020, the first NB-IoT-specific Alpha-Beta-Gamma (ABG) path loss (PL) model, which showed higher prediction accuracy compared to models developed for different technologies but often used for NB-IoT. In this paper, to further investigate NB-IoT PL in urban scenarios, we analyze new measurement campaigns performed in 2020-2021 and 2023 in the city of Rome, Italy. First, we use the 2020-2021 measurements to derive Rome-2021, a new NB-IoT-specific ABG PL model. We show that Rome-2021 preserves the statistical properties of Oslo-2020 (e.g., the Gaussianity of the PL exponent distribution across base stations), although the moments of the distributions are different due to city-specific environmental characteristics. We also use new data on signal losses due to outdoor-to-indoor propagation to refine the analysis of this scenario. Finally, we propose a methodology to combine Oslo-2020 and Rome-2021 into a more general model. Our methodology uses so-called Mixture Distributions (MDs), thus leveraging the shared statistical properties between Oslo-2020 and Rome-2021. By using the 2023 measurements, we show that our MD-based approach estimates PL model parameters with higher accuracy compared to Oslo-2020 and Rome-2021 models used separately, thus providing an effective solution for predicting NB-IoT urban PL in lack of site-specific measurements and information.

Mobile Augmented Reality (MAR) applications pose unique challenges due to computation intensity, constrained device resources, and high interactive rendering requirements. The emergence of 5 G and edge computing offers opportunities to offload computation to the edge and cloud, indirectly enhancing the computing capability and usage duration of MAR devices. However, existing general task offloading and multipath transmission techniques do not address the challenges in offloading path selection with multiple edges, dynamic resource competition awareness, and spatial computation with strong task dependencies. This paper contributes FPSelector, a flexible path selector for MAR offloading. We present a two-tier MAR-specific offloading scheme with multiple edge nodes. In offloading decisions, we design a reinforcement learning model to generate the selection policy for each packet of an AR data stream. This model incorporates an action masking mechanism, a comprehensive reward function, and state features complemented by a resource prediction module, making FPSelector aware of dynamic heterogeneous environments. Moreover, we propose an online learning strategy to facilitate real-time selection. To validate its efficacy, we compare FPSelector's performance against leading schedulers under various scenarios, demonstrating a notable reduction of 9.9% and 9.6% in overall completion time for 4 K and 8 K video-based MAR applications compared to its closest competitor.

The promises of multipath transport are to aggregate bandwidth, improve resource utilisation and enhance reliability. In this article, we demonstrate that the way multipath coupled congestion control is defined today leads to a suboptimal resource utilisation when network paths are disjoint, i.e., they do not share a bottleneck link. With growing interest in standardising Multipath QUIC (MPQUIC), we have implemented the practical Shared Bottleneck Detection (SBD) algorithm from RFC8382 in MPQUIC (MPQUIC-SBD). Through extensive experiments, we evaluate MPQUIC-SBD in the context of video streaming with various Adaptive Bitrate (ABR) algorithms, addressing both ABR classes of rule- and learning-based solutions. We demonstrate that MPQUIC-SBD accurately detects shared bottlenecks over 90% of the time, depending on the ABR algorithm, as the size of the video segments increases. In non-shared bottleneck scenarios, when MPQUIC-SBD detects that its QUIC subflows do not share the same network resources, it decouples their congestion windows accordingly, enabling video throughput gains of up to 37% compared to MPQUIC. These gains translate directly into improved video quality metrics, including higher bitrate, better resolution and reduced buffering, resulting in an enhanced quality of experience for users.

Nowadays, the focus in 5G networks has shifted from Quality of Service (QoS) to Quality of Experience (QoE) characterisation and prediction. As a matter of fact, mobile operators are increasingly interested in measuring and/or predicting QoE Key Performance Indicators (KPIs) on their 5G networks. In this context, a recent methodology by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T) allows to characterize the level of interactivity achievable by real-time services on 5G networks, by computing a synthetic QoE KPI referred to as interactivity score (i-score). The i-score, defined as the measurable latency, continuity, and reliability of a given service, is computed by using a model that takes into account three QoS KPIs, i.e., packet trip time, jitter, and loss rate. In this paper, aiming at assessing the effectiveness of the ITU-T methodology in characterizing 5G network performance, we analyze a large-scale measurement campaign executed over two commercial 5G Non-Standalone (NSA) deployments in the city of Rome, Italy. During this campaign, traces related to radio coverage and service performance (i.e., the i-score and corresponding KPIs needed to compute it) were collected in parallel. Therefore, we use the dataset to characterize the observed i-score performance, and demonstrate that it is possible to successfully predict this KPI with machine learning techniques, using radio layer parameters and power measurements. Mobile operators could take advantage of our findings, minimizing the need for time/resource-consuming QoE tests. Ensemble methods in fact achieve an accuracy spanning from 0.79 to 0.83, with Random Forest as one of the best algorithm to predict the i-score from radio layer parameters.

Conditional entropy is an important concept that naturally arises in fields such as finance, sociology, and intelligent decision making when solving problems involving statistical inferences. Formally speaking, given two random variables X and Y, one is interested in the amount and direction of information flow between X and Y . It helps to draw conclusions about Y while only observing X. Conditional entropy H(Y |X) quantifies the amount of information flow from X to Y . In practice, calculating H(Y |X) exactly is infeasible. Current estimation methods are complex and suffer from estimation bias issues. In this paper, we present a simple Machine Learning based estimation method. Our method can be used to estimate H(Y |X) for discrete X and bi-valued Y. Given X and Y observations, we first construct a natural binary classification training dataset. We then train a supervised learning algorithm on this dataset, and use its prediction accuracy to estimate H(Y |X). We also present a simple condition on the prediction accuracy to determine if there is information flow from X to Y. We support our ideas using formal arguments and through an experiment involving a gender-bias study using a part of the employee database of Karlstad University, Sweden.

The rise of Microservices architectures has introduced a surge in the demand for containerized software provisioning and orchestration. Kubernetes is a leading container orchestration platform widely adopted for its efficient management of containerized applications within cloud datacenters. However, the proliferation of containerized applications, particularly in resource-constrained environments like Internet of Things (IoT) edge devices, has prompted numerous software vendors to release lightweight Kubernetes-derived platforms, tailored for streamlined deployment in such infrastructures. Despite this, selecting the most suitable distribution for edge-based IoT applications remains a challenge due to a lack of comprehensive studies in this domain. This paper presents a comprehensive model for evaluating the performance of lightweight Container Orchestration Distributions (CODs), incorporating resource usage, control-plane performance, and data-plane performance metrics. Also, we conduct a comparative empirical analysis of four prominent CODs (i.e., k0s, k3s, Microk8s, and Microshift) by focusing on their suitability for edge environments. Our findings reveal that k3s is the overall best-performant COD.

While Internet Service Providers (ISPs) have traditionally focused on marketing network throughput, it is becoming increasingly recognized that network latency also plays a significant role for the quality of experience. However, many ISPs lack the means to continuously monitor the latency of their network. In this work, we present a method to continuously monitor and aggregate network latency per subnet directly in the Linux kernel by leveraging eBPF. We deploy this solution on a middlebox in an ISP network and collect an extensive dataset of latency measurements for both the internal and external parts of the network. We find that our monitoring solution can monitor all subscriber traffic while maintaining a low overhead of only around 1% additional CPU utilization. Our analysis of the latency data reveals a wide latency tail in the last-mile access, which grows during busy periods in the evening. Furthermore, we dissect the external network latency and uncover the latency profiles for the most popular autonomous systems.

The Access Traffic Steering, Switching, and Splitting (ATSSS) technology, currently under standardization by the 3rd Generation Partnership Project (3GPP), is designed to enhance wireless access through the bundling of both 3GPP and non-3GPP access. One implementation option involves tunneling data through the Multipath QUIC (MP-QUIC) protocol. The decision of which access network each packet is transmitted on can greatly impact the performance of a network flow, and may also impact the performance of other flows. While Active Queue Management (AQM) in the network could normally manage such interactions, such techniques are limited in a shared multipath-tunnel context, since all traffic in the tunnel share a single connection. This paper introduces a multi-flow, multipath tunneling framework using MP-QUIC. It employs four distinct scheduling policies for packet scheduling decisions. We investigate how such scheduling policies interact with each other, and how they may be combined in order to achieve trade-offs in terms of latency, interactivity, and throughput. Through an extensive investigation in both emulated and real-world environments, we show that Quality of Service (QoS) can be significantly improved by smart combinations of scheduling policies.

Although Starlink has been deployed for several years, a detailed understanding of system internals is still lacking. In this work we employ precise per-packet timestamps obtained from a hardware-timestamp capable NIC connected to a Starlink terminal. We find that Starlink frame timing details are readily observable at the network layer by analyzing the packet timing patterns. Based on a one-week measurement campaign we collect around half a billion of packet size and timing observations. Processing these observations yields 2.3 million transmission bursts. To learn details on the radio resource management we develop a methodology to infer the effective physical layer sending rate. Our findings show that although Starlink throughput can vary widely over multiple time-scales, there are a small number of fundamental physical layer transmission rates. We employ Gaussian Mixture Modeling to determine 14 such fundamental transmission rates, and relate the obtained rates to previous knowledge of the Starlink OFDMA frame structure. Our empirical observations provide an excellent match for a radio resource configuration where a Starlink frame employs 1000 sub carriers and 287 symbols per frame for user traffic transmission, which for uniform 4-QAM modulation yields a base rate of 430.5 Mbps. This physical layer base rate appears to mostly be varied by multiples of 27 Mbps, in several instances likely by modifying the modulation of a subset of the symbols in multiples of 18 symbols.

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions.

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models.

Bundling of multiple access technologies is currently being standardized by 3GPP in the 5G access traffic steering, switching and splitting (ATSSS) framework, with the goal to increase robustness, resiliency and capacity of wireless access. A key part of an ATSSS framework is the packet scheduler, which decides the access network over which each packet is to be transmitted. As wireless channels are highly dynamic, a challenge for any scheduler is to correctly estimate the capacity of each path, and thereby avoid congesting the paths. In this paper, we further develop a recent packet scheduler that exploits cross-layer information from the congestion control state of individual transport layer tunnels when making scheduling decisions. Our aim is to achieve good path utilization while keeping the congestion delay low. Extensive emulations show that our approach reduces the excess delay at the bottleneck to as little as 34%. We furthermore show that our approach improves the performance of end-to-end applications including WebRTC and YouTube compared to state-of-the art.

The promises of multipath transport is to aggregate bandwidth and improve resource utilisation and reliability. We demonstrate in this paper that the way multipath coupled congestion control is defined today RFC6359 leads to a sub-optimal resource utilisation when network paths are mainly disjoint, i.e., they do not share a bottleneck. With growing interest to standardise Multipath QUIC (MPQUIC), we implement the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC, namely MPQUIC-SBD. We evaluate MPQUIC-SBD through extensive emulation experiments in the context of video streaming. We show that MPQUIC-SBD is able to correctly detect shared bottlenecks over 90% of the time as the video segments' size increase depending on the Adaptive Bitrate (ABR) algorithm. In non-shared bottleneck scenarios, MPQUIC-SBD results in video throughput gains of more than 13% compared to MPQUIC, which directly translates into better video quality metrics.

Abstract The Domain Name System (DNS) is a critical Internet infrastructure that translates human-readable domain names to IP addresses. It was originally designed over 35 years ago and multiple enhancements have since then been made, in particular to make DNS lookups more secure and privacy preserving. Query name minimization () was initially introduced in 2016 to limit the exposure of queries sent across DNS and thereby enhance privacy. In this paper, we take a look at the adoption of , building upon and extending measurements made by De Vries et al. in 2018. We analyze adoption on the Internet using active measurements both on resolvers used by RIPE Atlas probes and on open resolvers. Aside from adding more vantage points when measuring adoption on open resolvers, we also increase the number of repetitions, which reveals conflicting resolvers – resolvers that support for some queries but not for others. For the passive measurements at root and Top-Level Domain (TLD) name servers, we extend the analysis over a longer period of time, introduce additional sources, and filter out non-valid queries. Furthermore, our controlled experiments measure performance and result quality of newer versions of the -enabled open source resolvers used in the previous study, with the addition of PowerDNS. Our results, using extended methods from previous work, show that the adoption of has significantly increased since 2018. New controlled experiments also show a trend of higher number of packets used by resolvers and lower error rates in the DNS queries. Since is a balance between performance and privacy, we further discuss the depth limit of minimizing labels and propose the use of a public suffix list for setting this limit.

Abstract Network latency is a critical factor for the perceived quality of experience for many applications. With an increasing focus on interactive and real-time applications, which require reliable and low latency, the ability to continuously and efficiently monitor latency is becoming more important than ever. Always-on passive monitoring of latency can provide continuous latency metrics without injecting any traffic into the network. However, software-based monitoring tools often struggle to keep up with traffic as packet rates increase, especially on contemporary multi-Gbps interfaces. We investigate the feasibility of using eBPF to enable efficient passive network latency monitoring by implementing an evolved Passive Ping (ePPing). Our evaluation shows that ePPing delivers accurate RTT measurements and can handle over 1 Mpps, or correspondingly over 10 Gbps, on a single core, greatly improving on state-of-the-art software based solutions, such as PPing.

5G mobile networks introduce the concept of network slicing, the functionality of creating virtual networks on top of shared physical infrastructure. Such slices can be tailored to various vertical services. A single User Equipment (UE) may be served by multiple network slice instances simultaneously, which opens up the possibility of dynamically steering traffic in response to the specific needs of individual applications – and as a reaction to events inside the network, e.g., network failures.This paper presents the PoLicy-based Architecture for Network Slicing (PLANS). In this policy framework, the network slice management entity in the 5G core and the UE can cooperatively optimize the usage of the available network slices via policy systems installed both inside the network and on the UE. The PLANS architecture has been implemented and evaluated in a 5G testbed. For two different case studies, we show how such a system can be leveraged to provide optimized services and increased robustness against network failures. First, we consider a drone autopilot scenario, and demonstrate how PLANS can reduce network-slice recovery time by more than 90%. Second, we illustrate for a 360°video streaming scenario how PLANS can help prevent video quality degradation when a network slice becomes unavailable.

Tactile Internet defines applications for remotely controlling and manipulating critical devices that require perceived real-time operation with additional demanding requirements like reliability. These use cases with stringent requirements demand adequate transport protocols to take advantage of the underlying possibilities. Traditional transport-layer solutions like TCP and UDP are no longer sufficient, hence novel protocols are being developed to support these applications. In this paper, we present an implementation and evaluation of the Multi-connection Tactile Internet Protocol (MTIP), a transport layer proposal to support these communications. MTIP uses application and network status information to perform an intelligent selection of paths in order to improve reliability and latency. In our evaluations, we study how the different configurations of the MTIP algorithm affect this selection and we see a direct trade-off where, with more restrictive thresholds, MTIP can increase the packets received correctly at the cost of sending extra duplicate packets.

Networked systems have recently aimed to use multiple access networks in parallel to increase resiliency, availability and capacity. However, different paths may have different latency characteristics, which may lead to out-of-order packet delivery. This may severely impact both the end-to-end application performance and the capacity utilisation of multiaccess systems. In this paper, we show that in-network support for packet reordering for multiaccess systems that are based on multiple transport layer tunnels is beneficial for several application types. Our findings are applicable to TCP and QUIC traffic in the 3GPP ATSSS context, where we use the MP-DCCP tunneling framework with a buffer-based packet reordering approach that uses a dynamic timing threshold to cope with variation of path delays over time. We demonstrate achievable performance gains for a wide range of path latency differences and end-to-end round trip times when using different in-network reordering algorithms.

“Roam like Home” is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people can use data services more freely across Europe. However, the implications of roaming solutions on network performance have not been carefully examined yet. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 4G networks in Europe, including mobile network configuration, performance characteristics, and quality of experience. We find that operators adopt a common approach to implement roaming called Home-routed roaming (HR). This results in additional latency penalties of 60 ms or more, depending on geographical distance. This leads to worse browsing performance, with an increase in the metrics related to Quality of Experience (QoE) of users (Page Load time and Speed Index) in the order of 15-20 percent. We further analyze in isolation the impact of latency on QoE metrics and find that the penalty imposed by HR leads to a degradation on QoE metrics up to 150 percent in case of intercontinental roaming.

Cooperative intelligent transport systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100 percent availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

In the fifth generation (5G) mobile networks, the number of user-plane gateways has increased, and, in contrast to previous generations they can be deployed in a decentralized way and auto-scaled independently from their control-plane functions. Moreover, the performance of the user-plane gateways can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user-plane gateways has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user-plane gateways. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load-balancing algorithms for the user plane of a VPP-based, softwarized 5G network. The novel performance indicator is estimated based on the VPP vector rate and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user-plane gateway and their VPP vector rate. We validate and evaluate our proposed solution in a 5G testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user-plane traffic, and that our proposed load-balancing algorithms can give a better distribution of traffic load as compared to traditional static algorithms.

Cellular networks are continuously evolving to allow improved throughput and low latency performance for applications. However, it has been shown that, due to buffer over-provisioning, TCP's standard loss-based congestion control algorithms (CCAs) can cause long delays in cellular networks. The QUIC transport protocol and the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control are both proposed in response to shortcomings observed in TCP and loss-based CCAs. Despite its notable advantages, BBR can experience suboptimal delay performance in cellular networks due to one of its underlying design choices: the maximum bandwidth filter at the sender. In this work, we leverage QUIC's extensibility to enhance BBR. Instead of using the ACK rate observed at the sender side, we apply a more fitting delivery rate calculated at the receiver. Our 5G-trace-based emulation experiments in CloudLab suggest that our modified QUIC could significantly improve latency without any notable effect on the throughput: In particular, in some of our experiments, we observe up to 39% reduction of the round-trip time (RTT) with a worstcase throughput reduction of 2.7%.

Cellular networks have evolved to support high peak bitrates with low loss rates as observed by the higher layers. However, applications and services running over cellular networks are now facing other difficult congestion-related challenges, most notably a highly variable link capacity and bufferbloat. To overcome these issues and improve performance of network traffic in 4G/5G cellular networks, a number of in-network and end-to-end solutions have been proposed. Fairness between interacting congestion control algorithms (CCAs) has played an important role in the type of CCAs considered for research and deployment. The placement of content closer to the user and the allocation of per-user queues in cellular networks has increased the likelihood of a cellular access bottleneck and reduced the extent of flow interaction between multiple users. This has resulted in renewed interest in end-to-end CCAs for cellular networks by opening up room for research and exploration. In this work, we present end-to-end CCAs that target a high throughput and a low latency over highly variable network links, and classify them according to the way they address the congestion control. The work also discusses the deployability of the algorithms. In addition, we provide insights into possible future research directions, such as coping with a higher degree of variability, interaction of CCAs in a shared bottleneck, and avenues for synergized research, such as CCAs assisted by software defined networking and network function virtualization. We hope that this work will serve as a starting point for systematically navigating through the expanding number of cellular CCAs.

The lack of publicly available large-scale measurements has hindered the derivation of empirical path-loss (PL) models for Narrowband Internet of Things (NB-IoT). Therefore, simulation-based investigations currently rely on models conceived for other cellular technologies, which are characterized, however, by different available bandwidth, carrier frequency, and infrastructure deployment, among others. In this article, we take advantage of data from a large-scale measurement campaign in the city of Oslo, Norway, to provide the first empirical characterization of NB-IoT PL in an urban scenario. For the PL average term, we characterize Alpha-Beta-Gamma (ABG) and Close-In (CI) models. By analyzing multiple NB-IoT cells, we propose a statistical PL characterization, i.e., the model parameters are not set to a single constant value across cells, but are randomly extracted from well-known distributions. Similarly, we define the PL shadowing distribution, correlation over distance, and intersite correlation. Finally, we give initial insights on the outdoor-to-indoor propagation, using measurements up to deep indoor scenarios. The proposed models improve the PL estimation accuracy compared to the ones currently adopted in NB-IoT investigations, enabling more realistic simulations of urban scenarios similar to the sites covered by our measurements.

In the context of massive machine-type communications (mMTCs), the narrowband Internet-of-Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored random access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This article presents the first data-driven analysis of NB-IoT RA, exploiting a large-scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators' configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. A comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a machine learning (ML) approach and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in an RA-enhanced scheme and show that optimized configurations enable power consumption reduction of at least 50%. We also make our data set available for further exploration, toward the discovery of new insights and research perspectives.

Multipath transport protocols utilize multiple network paths (e.g., WiFi and cellular) to achieve improved performance and reliability, compared with their single-path counterparts. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths. However, state-of-the-art multipath schedulers face the challenge when dealing with heterogeneous paths with dynamic path characteristics (i.e., packet loss, fluctuation of delay). In this paper, we propose Peekaboo, a novel learning-based multipath scheduler that is aware of the dynamic characteristics of the heterogeneous paths. Peekaboo is able to learn scheduling decisions to adopt over time based on the current path characteristics and dynamicity levels - from both deterministic and stochastic perspectives. We implement Peekaboo in Multipath QUIC (MPQUIC) and compare it with state-of-the-art multipath schedulers for a wide range of dynamic heterogeneous environments, upon both emulated and real networks. Our results show that Peekaboo outperforms the other schedulers by up to 31.2% in emulated networks and up to 36.3% in real network scenarios.

Internet traffic is comprised of data flows from various applications with unique traffic characteristics. For many cloud applications, end-to-end latency is a primary factor affecting the perceived user experience. As packet losses cause delays in the communication they impact user experience, making efficient handling of packet losses an important function of transport layer protocols. Multipath TCP (MPTCP) is a modification to TCP that enables simultaneous use of several paths for a TCP flow. MPTCP is known to improve throughput. However, the performance of MPTCP is not optimal when handling certain loss scenarios. Efficient packet loss recovery is thus important to achieve desirable flow completion times for interactive cloud-based applications. In this paper we evaluate the performance of MPTCP in handling tail losses using traffic traces from various cloud-based applications. Tail losses, losses that occur at the end of a flow or traffic burst, are particularly challenging from a latency perspective as they are difficult to detect and recover in a timely manner. Tail losses in TCP are handled by using a tail loss probe (TLP) mechanism which was adapted to MPTCP from TCP. We investigate the performance of TLP in MPTCP, comparing the standard implementation to a recently proposed, less conservative approach. Our experimental results show that a less conservative implementation of TLP performs significantly better than the standard implementation in handling tail losses, reducing the average burst completion time of cloud based applications when tail loss occurs by up to 50% in certain cases.

Current LTE networks provide a large fraction of the mobile communication needs. One recent application area that have attained additional interest is the provision of mobile communication services to train passengers. To allow more efficient use of network resources and better onboard communication experience, onboard traffic aggregation can be performed. In this work we examine a large-scale operational data set from a router-based LTE traffic aggregation system mounted onboard more than 100 trains belonging to a major Swedish train operator. We use both deep learning (DL) with Deep Neural Networks and traditional machine learning (ML) with Random Forests to examine an observed association between train velocity and achieved throughput, which curiously varies over different radio conditions. More than 37000 train journeys are analyzed to explore for structure and learn potential explanatory features. The results indicate that the association has a limited presence on a per cell basis, and that there is only a limited amount of learnable structure per cell. A resampling evaluation shows that the association becomes apparent when cell measurements are aggregated at an order of tens to a hundred cells.

Mobile nodes are typically equipped with multiple radios and can connect to multiple radio access networks (e.g. WiFi, LTE and 5G). Consequently, it is important to design mechanisms that efficiently manage multiple network interfaces for aggregating the capacity, steering of traffic flows or switching flows among multiple interfaces. While such multi-access solutions have the potential to increase the overall traffic throughput and communication reliability, the variable latencies on different access links introduce packet delay variation which has negative effect on the application quality of service and user quality of experience. In this paper, we present a new IP-compatible multipath framework for heterogeneous access networks. The framework uses Multipath Datagram Congestion Control Protocol (MP-DCCP) - a set of extensions to regular DCCP - to enable a transport connection to operate across multiple access networks, simultaneously. We present the design of the new protocol framework and show simulation and experimental testbed results that (1) demonstrate the operation of the new framework, and (2) demonstrate the ability of our solution to manage significant packet delay variation caused by the asymmetry of network paths, by applying pluggable packet scheduling or reordering algorithms.

Providing reliable internet connectivity to train passengers can be handled with onboard aggregation routers that use multiple external antennas to simultaneously convey user traffic over multiple links. This work studies the operational characteristics of a large-scale deployment of such a system. The examination focuses on how train velocity is associated with achieved link throughput, and how various interaction effects influence the relationship. A large data set collected over three years is analyzed, indicating that there is a systematic association between train velocity and link throughput that varies over the radio conditions and which is also linked to differences between operators.

As WiFi grows ever more popular, airtime contention becomes an increasing problem. One way to alleviate this is through network policy enforcement. Unfortunately, WiFi lacks protocol support for configuring policies for its usage, and since network-wide coordination cannot generally be ensured, enforcing policy is challenging. However, as we have shown in previous work, an access point can influence the behaviour of connected devices by changing its scheduling of transmission opportunities, which can be used to achieve airtime fairness. In this work, we show that this mechanism can be extended to successfully enforce airtime usage policies in WiFi networks. We implement this as an extension our previous airtime fairness work, and present PoliFi, the resulting policy enforcement system. Our evaluation shows that PoliFi makes it possible to express a range of useful policies. These include prioritisation of specific devices; balancing groups of devices for sharing between different logical networks or network slices; and limiting groups of devices to implement guest networks or other low-priority services. We also show how these can be used to improve the performance of a real-world DASH video streaming application.

Seamless wireless connectivity and low latency communication in mobility scenarios are two fundamental requirements in the fifth generation networks to effectively cover a broader range of use cases such as massive machine type communications and ultra-reliable low-latency communications for new industrial/potential users, as well as enhanced mobile broadband for conventional users. In this paper, we aim at measuring certain key performance indicators in mobility scenarios, highlighting some practical inefficiencies that require more careful consideration when designing and enhancing mobility features of 5G networks. We investigate the round trip time, uplink throughput and the impact of handover interruption time on these metrics. Our results are based on three months experiment campaign in a mix of rural, suburban, and urban environment using the geographically distributed MONROE platform. Based on the observed discrepancies between measured and standardized LTE performance, we conclude the paper with a discussion of techniques that may need more attention for mobility scenarios in fifth generation mobile networks.

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.

The demand for mobile communication is continuously increasing, and mobile devices are now the communication device of choice for many people. To guarantee connectivity and performance, mobile devices are typically equipped with multiple interfaces. To this end, exploiting multiple available interfaces is also a crucial aspect of the upcoming 5G standard for reducing costs, easing network management, and providing a good user experience. Multi-path protocols, such as multi-path TCP (MPTCP), can be used to provide performance optimization through load-balancing and resilience to coverage drops and link failures, however, they do not automatically guarantee better performance. For instance, low-latency communication has been proven hard to achieve when a device has network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN). For multi-path communication, the data scheduler is vital to provide low latency, since it decides over which network interface to send individual data segments. In this paper, we focus on the MPTCP scheduler with the goal of providing a good user experience for latency-sensitive applications when interface quality is asymmetric. After an initial assessment of existing scheduling algorithms, we present two novel scheduling techniques: the block estimation (BLEST) scheduler and the shortest transmission time first (STTF) scheduler. BLEST and STTF are compared with existing schedulers in both emulated and real-world environments and are shown to reduce web object transmission times with up to 51% and provide 45% faster communication for interactive applications, compared with MPTCP's default scheduler.

"Roam like Home" is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people are able to use data services more freely across Europe. However, the implications roaming solutions have on performance have not been carefully examined. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 3G and 4G networks, including mobile network configuration, performance characteristics, and content discrimination. We find that operators adopt common approaches to implementing roaming, resulting in additional latency penalties of ∼60 ms or more, depending on geographical distance. Considering content accessibility, roaming poses additional constraints that leads to only minimal deviations when accessing content in the original country. However, geographical restrictions in the visited country make the picture more complicated and less intuitive.

In this paper, we aim at tackling the scalability problem of the Mobility Management Entity (MME), which plays a crucial role of handling control plane traffic in the current 4G Evolved Packet Core as well as the next generation mobile core, 5G. One of the solutions to this problem is to virtualize the MME by applying Network Function Virtualization principles and then deploy it as a cluster of multiple virtual MME instances (vMMEs) with a front-end load balancer. Although several designs have been proposed, most of them assume the use of simple algorithms such as random and round-robin to balance the incoming traffic without any performance assessment. To this end, we implemented a weighted round robin algorithm which takes into account the heterogeneity of resources such as the capacity of vMMEs. We compare this algorithm with a random and a round-robin algorithm under two different system settings. Experimental results suggest that carefully selected load balancing algorithms can significantly reduce the control plane latency as compared to simple random or round-robin schemes.

Information-centric networking (ICN) with its design around named-based forwarding and in-network caching holds great promises to become a key architecture for the future Internet. Many proposed ICN hop-by-hop congestion control schemes assume a fixed and known link capacity, which rarely - if ever - holds true for wireless links. Firstly, we demonstrate that although these congestion control schemes are able to fairly well utilise the available wireless link capacity, they greatly fail to keep the delay low. In fact, they essentially offer the same delay as in the case with no hop-by-hop, only end-to-end, congestion control. Secondly, we show that by complementing these schemes with an easy-to-implement, packet-train capacity estimator, we reduce the delay to a level significantly lower than what is obtained with only end-to-end congestion control, while still being able to keep the link utilisation at a high level.

Identifying VoIP and video traffic is often useful in the context of managing a cellular network, and to perform such traffic classification deep packet inspection (DPI) approaches are often used. Commercial DPI classifiers do not necessarily differentiate between, for example, YouTube traffic that arises from browsing inside the YouTube app, and traffic arising from the actual viewing of a YouTube video. Here we apply unsupervised clustering methods on such cellular DPI-labeled VoIP and video traffic to identify the characteristic behavior of the two sub-groups of media-transfer and non media-transfer flows. The analysis is based on a measurement campaign performed inside the core network of a commercial cellular operator, collecting data for more than two billion packets in 40+ million flows. A specially instrumented commercial DPI appliance allows the simultaneous collection of per packet information in addition to the DPI classification output. We show that the majority of flows falls into clusters that are easily identifiable as belonging to one of the traffic sub-groups, and that a surprising majority of DPIlabeled VoIP and video traffic is non-media related.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.

Seamless wireless connectivity in high mobility scenarios (> 300 km/h), is one of the fundamental key requirements for the future 5G networks. High speed train (HST) is one of the preferred mid-range transportation systems, and highlights the challenges of providing wireless connectivity in high mobility scenarios for the 5G networks. Advanced version of Long Term Evolution (LTE-A) from the Third Generation Partnership Project (3GPP) with peak data rate up to 100 Mbps in high mobility scenarios paved the road toward high quality and cost effective onboard Internet in HSTs. However, frequent handovers (HO) of large number of onboard users increase the service interruptions that in turn inevitably decrease the experienced quality of service (QoS). In this paper, according to the two-tier architecture of the HST wireless connectivity, we propose a novel and practically viable onboard traffic offloading mechanism among the HST carriages that effectively mitigates the service interruptions caused by frequent HOs of massive number of onboard users. The proposed architecture does not imply any change on the LTE network standardization. Conclusions are supported by numerical results for realistic LTE parameters and current HST settings.

Train-mounted aggregation routers that provide WiFi access to train passengers and bundle external communication over multiple cellular modems/links is an efficient way of providing communication services on trains. However, the characteristics of such systems have received limited attention in the literature. In this paper we address this gap by examining the communication characteristics of such systems based on a large data set gathered over six months from an operational Swedish railway system. We focus our examination on the relationship between per link throughput and train velocity. Using Levenberg- Marquardt non-linear regression a noticeable critical point is observed for an RS-SINR of around 12 dB. At this point the impact of increased train velocity on per link throughput changes from being negative to becoming positive. Using a machine learning approach we also explore the relative importance of several observed metrics in relation to per link throughput.

We analyse two complementary datasets to quantify the latency variation experienced by internet end-users: (i) a large-scale active measurement dataset (from the Measurement Lab Network Diagnostic Tool) which shed light on long-term trends and regional differences; and (ii) passive measurement data from an access aggregation link which is used to analyse the edge links closest to the user.

Virtualization is central to cloud computing systems. It abstracts computing resources to be shared among multiple virtual machines (VMs) that can be easily managed to run multiple applications and services. To benefit from the advantages of cloud computing, and to cope with increasing traffic demands, telecom operators have adopted cloud computing. Telecom services and applications are, however, characterized by real- time responsiveness, strict end-to-end latency, and high reliability. Due to the inherent overhead of virtualization, the network performance of applications and services can be degraded. To improve the performance of emerging applications and services that demand stringent end-to-end latency, and to understand the network performance bottleneck of virtualization, a comprehensive performance measurement and analysis is required. To this end, we conducted controlled and detailed experiments to understand the impact of virtualization on end-to-end latency and the performance of transport protocols in a virtualized environment. We also provide a packet delay breakdown in the virtualization layer which helps in the optimization of hypervisor components. Our experimental results indicate that the end-to-end latency and packet delay in the virtualization layer are increased with co-located VMs.

This paper aims at increasing our understanding of the behaviour of some of the major TCP variants in LTE networks. A simulation study on the behaviour of seven TCP variants is performed. The evolution of the throughput, congestion window and queuing delay are studied for four scenarios with different network loads and flow types. Our measurements show that, in average radio conditions, most variants are able to quickly reach full link utilisation. However, to achieve the same throughput, they create different amounts of queuing delay. On the one hand, loss-based algorithms tend to completely fill the queue, creating huge queuing delays and inducing packet losses. On the other hand, delay-based variants manage to limit the queue size and decrease the amount of packets dropped by the eNodeB, but struggle to reach the maximum throughput in some circumstances.

There is a growing concern that the Internet transport layer has become less adaptive to the requirements of new applications, and that further evolution has become very difficult. This is because a fundamental assumption no longer holds: it can no longer be assumed that the transport layer is only in the scope of end-hosts. The success of TCP and UDP and the ubiquity of middleboxes have led to ossification of both the network infrastructure and the API presented to applications. This has led to the development of workarounds and point solutions that fail to cover many facets of the problem. To address this issue, this paper identifies requirements for a new transport layer and then proposes a conceptual architecture that we argue is both flexible and evolvable. This new architecture requires that applications interface to the transport at a higher abstraction level, where an application can express communication preferences via a new richer API. Protocol machinery can use this information to decide which of the available transport protocols is used. By placing the protocol machinery in the transport layer, the new architecture can allow for new protocols to be deployed and enable evolution of the transport layer.

To mitigate delay during transmission of bursty signaling traffic, concurrent multipath transmission (CMT) over several paths in parallel could be an option. Still, unordered delivery is a well known problem when concurrently transmitting data over asymmetric network paths, leading to extra delay due to Head-of-Line Blocking (HoLB). The Stream Control Transmission Protocol (SCTP), designed as a carrier for signaling traffic over IP, is currently being extended with support for CMT (CMT-SCTP). To reduce the impact of HoLB, SCTP has support for transmission of separate data flows, called SCTP streams. In this paper, we address sender scheduling to optimize latency for signaling traffic using CMT-SCTP. We present dynamic stream-aware (DS) scheduling, which utilizes the SCTP stream concept, and continuously considers the current network status as well as the data load to make scheduling decisions. We implement a DS scheduler and compare it against some existing schedulers. Our investigation suggests that DS scheduling could significantly reduce latency compared to dynamic path scheduling that does not consider streams. Moreover, we show that naive round-robin scheduling may provide low latency over symmetric network paths, but may transmit data on non-beneficial asymmetric network paths leading to increased latency. Finally, our results show that a static stream based approach, found beneficial for bulk traffic, is not appropriate for bursty signaling traffic.

Communication performed with mobile devices will experience varying levels of connectivity as the communication device moves in and out of coverage. A subset of mobile communication devices operate under conditions where the connectivity is characterized by relatively short contact periods occurring intermittently. In this paper we propose a model to predict the amount of data that can be transferred during such short contact periods. The model includes aspects of the transport layer slow-start behavior and is validated using data from a long-running measurement campaign in the networks of four Swedish cellular networks. Further validation of the modeling assumptions is performed by employing a numerical optimization technique based on non-linear least squares regression using the iterative Levenberg-Marquardt approach. The model is then used to explore the relevant parameter space.

Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.

Currently, Multipath TCP (MPTCP) - a modification to standard TCP that enables the concurrent use of several network paths in a single TCP connection - is being standardized by IETF. This paper provides a comprehensive evaluation of the use of MPTCP to reduce latency and thus improve the quality of experience or QoE for cloud-based applications. In particular, the paper considers the possible reductions in latency that could be obtained by using MPTCP and multiple network paths between a cloud service and a mobile end user. To obtain an appreciation of the expected latency performance for different types of cloud traffic, three applications are studied, Netflix, Google Maps, and Google Docs, representing typical applications generating high-, mid-, and low-intensity traffic. The results suggest that MPTCP could provide significant latency reductions for cloud applications, especially for applications such as Netflix and Google Maps. Moreover, the results suggest that MPTCP offers a reduced latency despite a few percent packet loss, and in spite of limited differences in the round-trip times of the network paths in an MPTCP connection. Still, larger differences in the round-trip times seem to significantly increase the application latency, especially for Netflix, Google Maps, and similar applications. Thus, to become an even better alternative to these applications, this paper suggests that the MPTCP packet scheduling policy should be changed: Apart from the round-trip times of the network paths in a connection, it should also consider the difference in round-trip time between the network paths.

New innovations that can transform societies and improve life for people are increasingly being asked for. Unfortunately, some avenues of research indicate that users of these new innovations may be inaccurate when they predict or remember the value of such new offerings. For example, the rapid development of new ICT services in areas such as health care may imply opportunities for better life conditions and well-being, but may also involve complicated predictions for users about the value they will create. New innovations may face adoption difficulties if users make inaccurate predictions or remember falsely the value that such innovations might have. In this study, 48 nurses predicted, experienced, and remembered the value of a new ICT service they used to diagnose an external skin lesion on a patient. Results showed significant differences between predicted and experienced value as well as between a service with high technical quality and the same service with lower technical quality; the value was underestimated at the time of prediction, as compared to actual experience, and the value of a high-quality ICT service was substantially more underestimated than the value of a low-quality ICT service. The results provide a novel and comprehensive understanding of how employees predict and experience the value of ICT service innovations.

Several new active queue management (AQM) and hybrid AQM/fairness queueing algorithms have been proposed recently. They seek to ensure low queueing delay and high network goodput without requiring parameter tuning of the algorithms themselves. However, extensive experimental evaluations of these algorithms are still lacking. This paper evaluates a selection of bottleneck queue management schemes in a test-bed representative of residential Internet connections of both symmetrical and asymmetrical bandwidths as well as WiFi. Latency under load and the performance of VoIP and web traffic patterns are evaluated under steady state conditions. Furthermore, the impact of the algorithms on fairness between TCP flows with different RTTs, and also the transient behaviour of the algorithms at flow startup is examined. The results show that while the AQM algorithms can significantly improve steady state performance, they exacerbate TCP flow unfairness. In addition, the evaluated AQMs severely struggle to quickly control queueing latency at flow startup, which can lead to large latency spikes that hurt the perceived performance. The fairness queueing algorithms almost completely alleviate the algorithm performance problems, providing the best balance of low latency and high throughput in the tested scenarios. However, on WiFi the performance of all the tested algorithms is hampered by large amounts of queueing in lower layers of the network stack inducing significant latency outside of the algorithms’ control.

Wireless mesh network (WMN), radio nodes that form a mesh topology, is an interesting architectural candidate for the future wireless Internet. A dense access network can rapidly be deployed at a reasonable cost because there is no need to wire the mesh access points. However, WMNs typically do not perform well with latency-sensitive data traffic, such as Voice over IP (VoIP), due to restricted bandwidth. In this paper, we present the design, implementation and evaluation of LA-AP-OLSR for hybrid WMNs, where mesh nodes have two radios; a fixed radio for receiving and a switchable radio which rapidly changes channels to send to neighbor nodes. The key novelty of our approach is to use any path routing for latency-sensitive traffic while normal traffic is treated using single path routing. Also, we develop a novel queuing strategy which prioritizes latency-sensitive traffic. A detailed evaluation using KAUMesh, an in-house multi-radio wireless mesh testbed, shows a reduction in latency and packet loss for VoIP traffic without negatively impacting competing TCP background traffic.

This paper examines the efficiency of resource utilization with respect to short-lived TCP flows in various cellular networks. The examination is done from the vantage point of an end-user who would like to use as much as possible of the cellular transmission resources that are available at any given time, thus minimizing the delays associated with communication. Based on a comprehensive measurement campaign we first derive network characteristics with regards to base RTT, RTT under load, and average throughput. A protocol efficiency metric is introduced to capture how efficiently short TCP flows are in fact able to use the instantaneously available transmission resources in a cellular network. The measurements show that short TCP connections have low efficiency in 3.5G (HSPA+) and 4G (LTE) mobile broadband networks, and that the improved latency and throughput characteristics of 4G in relation to 3.5G nevertheless results in lower short-flow efficiency for 4G.

Today commodity mobile devices are frequently equipped with two wireless access technologies, WiFi and 3G/4G. To enable continuous connectivity it is vital that these terminals provide for vertical handover between different technologies. Particularly, they should provide a vertical handover that complies with the timeliness requirements of soft real-time applications. Considering aspects such as cost- and ease-of-deployment, application neutrality, and, not least, the emergence of transport protocols that support multi-homing such as mobile SCTP and multi-path TCP, we think it would be beneficial to handle vertical handover in the transport layer of the mobile terminal. This paper demonstrates through several real-world experiments, the feasibility of using a lightweight vertical handover scheme in smart mobile terminals for live video streaming. The vertical handover criteria is based on the received signal strength. Our experiments suggest that the scheme indeed provides for seamless vertical handover at walking speed - our target scenario. However, the experiments also suggest that the scheme gives significant reductions in handover time, as compared to mobile SCTP without improvements, at higher speeds.

Wireless mesh networks (WMNs) consist of several small routers relaying packets wirelessly toward the destination or the Internet. A dense deployment enables the rapid creation of wireless access networks at a reasonable cost. The capacity of a wireless mesh network can be increased by using a diverse set of channels to allow simultaneous transmissions without causing interference. An interesting approach is to use one fixed wireless network interface for receiving and at least one switchable interface for sending. However, such a channel assignment suffers from switching overhead.

Coordinated multipoint (CoMP) transmission and reception techniques have been proposed to combat inter-cell interference in cellular systems and, hence, to increase the data rates. Due to the overhead introduced, not all users may be served with CoMP. In this paper, we focus on how to select users for joint processing CoMP under limited backhaul capacity. The evaluated user selection schemes take user experience into account, quantified by the utility of Internet application types. We propose a heuristic algorithm utility based user selection that has low computational complexity. Utility based user selection and maximize utility, which maximizes the sum utilities, are compared to maximize rate, which maximizes the sum data rates. A range of traffic mixes and user locations are evaluated. The simulation results indicate that resources required for CoMP are more efficiently used if the user selection is based on utility rather than on maximizing the total data rate. If utility is taken into account a higher total utility can in many cases be achieved for the same limited backhaul capacity.

The existence of excessively large and too filled network buffers, known as bufferbloat, has recently gained attention as a major performance problem for delay-sensitive applications. One important network scenario where bufferbloat may occur is cellular networks. This paper investigates the interaction between TCP congestion control and buffering in cellular networks. Extensive measurements have been performed in commercial 3G, 3.5G and 4G cellular networks, with a mix of long and short TCP flows using the CUBIC, NewReno and Westwood+ congestion control algorithms. The results show that the completion times of short flows increase significantly when concurrent long flow traffic is introduced. This is caused by increased buffer occupancy from the long flows. In addition, for 3G and 3.5G the completion times are shown to depend significantly on the congestion control algorithms used for the background flows, with CUBIC leading to significantly larger completion times.

Internet-based applications that require low latency are becoming more common. Such applications typically generate traffic consisting of short, or bursty, TCP flows. As TCP, instead, is designed to optimize the throughput of long bulk flows there is an apparent mismatch. To overcome this, a lot of research has recently focused on optimizing TCP for short flows as well. In this paper, we identify a performance problem for short flows caused by the metric caching conducted by the TCP control block interdependence mechanisms. Using this metric caching, a single packet loss can potentially ruin the performance for all future flows to the same destination by making them start in congestion avoidance instead of slow-start. To solve this, we propose an enhanced selective caching mechanism for short flows. To illustrate the usefulness of our approach, we implement it in both Linux and FreeBSD and experimentally evaluate it in a real test-bed. The experiments show that the selective caching approach is able to reduce the average transmission time of short flows by up to 40%.

Routing packets over multiple disjoint paths towards a destination can increase network utilization by load-balancing the traffic over the network. In wireless mesh networks, multi-radio multi-channel nodes are often used to create a larger set of interference-free paths thus increasing the chance of load-balancing. The drawback of load-balancing is that different paths might have different delay properties, causing packets to be reordered. This can reduce TCP performance significantly, as reordering is interpreted as a sign of congestion. Packet reordering can be avoided by letting the network layer forward traffic strictly on flow-level. This would avoid the negative drawbacks of packet reordering, but will also limit the ability to achieve optimal network throughput. On the other hand, there are several proposals that try to mitigate the effects of reordering at the transport layer. In this paper, we perform an in-depth evaluation of such TCP reordering mitigations in multi-radio multi-channel wireless mesh networks when using multi-path forwarding. We evaluate two TCP reordering mitigation techniques implemented in the Linux kernel. The transport layer mitigations are compared using different multi-path forwarding strategies. Our findings show that, in general, flow-level forwarding gives the best TCP performance and that transport layer reordering mitigations only marginally can improve performance.

Routing packets over multiple disjoint paths towards a destination can increase network utilization by load-balancing the traffic over the network. The drawback of load-balancing is that different paths might have different delay properties, causing packets to be reordered. This can reduce TCP performance significantly, as reordering is interpreted as a sign of congestion. Packet reordering can be avoided by letting the network layer route strictly on flow-level. This will, however, also limit the ability to achieve optimal network throughput. There are also several proposals that try to mitigate the effects of reordering at the transport layer. In this paper, we perform an initial evaluation of such TCP reordering mitigations in multi-radio multi-channel wireless mesh networks when using multi-path routing. We evaluate two TCP reordering mitigation techniques implemented in the Linux kernel. The transport layer mitigations are compared using different multi-path routing strategies. Our findings show that, in general, flow-level routing gives the best TCP performance and that transport layer reordering mitigations only marginally can improve performance.

The rapidly growing interest in untethered Internet connections, especially in terms of WLAN and 3G/4G mobile connections, calls for intelligent session management: a mobile device should be able to provide a reasonable end-user experience despite location changes, disconnection periods and, not least, handovers. As part of an effort to develop a SCTP-based session management framework that meets these criteria, we are studying ways of improving the SCTP handover delay for real-time traffic; especially the startup delay on the connection between a mobile device and the target access point. To obtain an appreciation of the theoretically feasible gains of optimizing the startup delay on the handover-target path, we have developed a model that predicts the transfer times of SCTP messages during slow start. This paper experimentally validates our model and demonstrates that it could be used to predict the message transfer times in a variable bitrate flow by approximating the variable flow with a constant dito. It also employs our model to obtain an appreciation of the startup delay penalties incured by slow start during handovers in typical mobile, real-time traffic scenarios.

PR-SCTP is an extension to SCTP for partial reliability that enables a content sensitive transport service where the reliability of messages can be individually controlled. In previous work, PR-SCTP has been applied for prioritization of messages and trading of reliability against timeliness for applications such as real time video streaming, IPTV transmission, SIP signaling, and syslog. As compared to TCP, it has been shown to provide significant reductions in message transfer delay and gains in application performance. Much of the previous work has, however, considered artificial loss scenarios without competing traffic. Experiments presented in this paper show that although PR-SCTP clearly outperforms TCP in artificial loss scenarios, the performance gain is not as evident in scenarios where PR-SCTP shares a bottleneck link with competing traffic. Our analysis shows that inefficiencies in the forward_tsn mechanism of PR-SCTP, overhead due to small messages, and a higher loss rate per byte encountered by PR-SCTP are contributing factors to the reduced performance when network resources are shared. Finally, a proposal for a more efficient forward_tsn mechanism is introduced.

From being regarded as a pathological event, packet reordering is now considered to be naturally prevalent within the Internet. When packets are reordered, the performance of transport protocols like TCP can be severely hurt. To overcome performance problems a number of mitigations have been proposed. Common for most proposals is, however, the lack of evaluations using real protocol implementations and good models of packet reordering. In this paper we highlight the need for detailed reordering models, and implement support for such models in the KauNet network emulator. To demonstrate the importance of using detailed models we present an experimental example.

Abstract Secure communications have a key role in future networks and applications. Information security provisions such as authorization, authentication, and encryption must be added to current communications protocols. To accomplish this, each protocol must be reexamined to determine the impact on performance of adding such security services. This paper presents an experimental evaluation of the performance costs of a wide variety of authentication methods over IKEv2 in real and partly emulated scenarios of next generation wireless networks. The studied methods are pre‐shared keys (PSKs), extensible authentication protocol (EAP) using MD5, SIM, TTLS‐MD5, TLS, and PEAP‐MSCHAPv2. For the EAP‐based methods, RADIUS is used as the authentication, authorization, and accounting (AAA) server. Different lengths of certificate chains are studied in case of the TLS‐based methods, i.e., TTLS‐MD5, TLS, and PEAP‐MSCHAPv2. The paper first presents a brief overview of the considered authentication methods. Then, a comparison of the costs for message transfers and computations associated with the authentication methods is provided. The measurement results are verified through a simple analysis, and interpreted by discussing the main contributing factors of the costs. The measurement results illustrate the practical costs involved for IKEv2 authentication, and the implications of the use of different methods are discussed. Copyright © 2009 John Wiley & Sons, Ltd.

Recently, wireless mesh networks (WMNs) have attracted attention as a way to provide alternative Internet connectivity to rural areas or communities. In WMNs, wireless access points communicate with each other wirelessly, forming a true wireless mesh based access network of mesh relay nodes (MRNs). A major problem is, however, scalability of WMNs as well as MAC and PHY layer overhead for packet transmission. Capacity of WMNs can be increased significantly by aggregating (combining) several smaller packets into larger ones. This has previously been demonstrated for voice flows where many packets are of small sizes. TCP could also benefit, as by aggregating several packets together there is a reduced collision risk between TCP DATA and TCP ACKs in addition to the reduced MAC layer contention. However, the traffic pattern when TCP is used is completely different from when voice and UDP flows are used, which sometimes requires different aggregation designs. In this paper, we investigate the impact of packet aggregation on TCP in Wireless Mesh Networks. Using several different scenarios we demonstrate that packet aggregation can not only increase capacity for TCP in such networks but also improve fairness and reduce end-to-end delay.

The stream control transmission protocol (SCTP) is a fairly new transport protocol that was initially designed for carrying signaling traffic in IP networks. SCTP offers a reliable end-to-end (E2E) transport. Compared to TCP, SCTP provides a much richer set of transport features such as message oriented transfer, multistreaming to handle head- of-line blocking, and multihoming for enhanced failover. These are all very attractive features, but at the same time proven hard and complex to secure for E2E transports. All existing security solutions have limitations. In this paper, a survey of existing solutions is first given. Then, an alternative solution is proposed. The proposed solution uses the new authenticated chunks for SCTP for integrity protection, TLS for key exchange and authentication, and symmetric encryption implemented at the socket layer for confidentiality protection. A qualitative comparison of the described E2E security solutions is also given.

The stream control transmission protocol (SCTP) is an important component in the ongoing evolution towards IP in the fixed and mobile telephone networks. It is the transport protocol being used in the ongoing deployment of IETFpsilas signaling transport (SIGTRAN) architecture for tunneling of traditional telephony signaling traffic over IP. Further SCTP represents an alternative for future SIP signaling traffic. Key to the success of SCTP is its ability to recover from network failures, in particular failed network paths. SCTP includes multihoming and a failover mechanism which should swiftly shift from a failed or unavailable network path to a backup path. However, several studies have shown that SCTPpsilas failover performance is dependent on factors both related to protocol parameters and network conditions. This paper complements these studies by providing a comprehensive evaluation of the impact of SACK delay under various traffic distributions. The results show a clear relation between the traffic distribution and the impact of the SACK delay on SCTP failover performance. Severe negative effects are observed for low intensity traffic composed of individual signaling messages. On the other hand, our results show limited impact of SACK delay for high intensity and bursty traffic. Furthermore, the results show a limited increase in network traffic by reducing the SACK delay at low traffic intensities and even less impact on network traffic at high traffic intensities. Based on these results we recommend a decrease of the SCTP SACK timer to a small value in signaling scenarios.

The Stream Control Transmission Protocol (SCTP) was designed by the IETF as a viable solution for transportation of signaling traffic within IP-based networks. Signaling traffic is different from ordinary TCP bulk traffic in many ways. One example is that the requirement of timely delivery usually is much stricter. However, the management of the SCTP retransmission timer is not optimally designed considering this requirement. Basically, the management algorithm, unnecessarily, extends the time needed for loss detection. This paper presents a new management algorithm that is able to maintain a correct state of the retransmission timer, which eliminates this particular problem. In addition, the paper also compares the performance of the two management algorithms in an emulated signaling environment, using the lksctp implementation of SCTP. The results show that the proposed algorithm is able to provide significant reductions in loss recovery time. In some cases, the time needed to recover from packet loss is reduced with as much as 43%.

The motivation behind this paper is a need to have a more accurate estimation of the failover time in SCTP. The traditional one, commonly used in the literature, is based on the sum of the consecutive retransmission timeouts. This is not always appropriate, especially when using the SCTP multihoming feature as a basis for achieving transport layer mobility in wireless networking scenarios, where the transition time between available paths becomes a key aspect for the optimisation. Two new factors are introduced into the proposed estimation formula to reflect the influence of the network parameters and the behaviour of the most common protocol implementations. For the proposed model, we perform a best-worst case analysis, and then illustrate it with an example of a detailed estimation. Finally, we perform simulations comparing our proposal with the traditional estimation in a typical transport layer mobility scenario including long thin networks.

High data rates, high spectral efficiency, flexibility, and low delays over the air interface will be important features in next-generation wireless systems. The overall challenge will be packet scheduling and adaptive radio transmission for multiple users, via multiple antennas and over frequency-selective wideband channels. This problem needs to be structured to obtain feasible solutions. The basic simplifying assumptions used here are clustering of antennas into cells, orthogonal transmission by use of cyclic-prefix orthogonal frequency-division multiplexing (OFDM) and a time-scale separation view of the total link adaptation, scheduling and intercell coordination problem. Based on these assumptions, we survey techniques that adapt the transmission to the temporal, frequency, and spatial channel properties. We provide a systematic overview of the design problems, such as the dimensioning of the allocated time-frequency resources, the influence of duplexing schemes, adaptation control issues for downlinks and uplinks, timing issues, and their relation to the required performance of channel predictors. Specific design choices are illustrated by recent research within the Swedish Wireless IP program and the EU IST-WINNER project. The presented results indicate that high-performance adaptive OFDM transmission systems are indeed feasible, also for challenging scenarios that involve vehicular velocities, high carrier frequencies, and high bandwidths.

This paper presents two new layer 4 fairness measures, the worst case TCP fairness index and the TCP fairness index. The purpose of the two indices is to measure the performance of algorithms scheduling elastic traffic in wireless networks. Numerical evaluation of round-robin, proportional-fair and max-rate scheduling shows that the new measures can accurately classify the fairness properties of the studied scheduling algorithms. This means that the new fairness measures are appropriate for measuring TCP fairness. The worst case TCP fairness index is the primary measure. The TCP fairness index can be used as a complement in certain cases. The new fairness measures are also compared with two layer 2 fairness measures. Numerical evaluation shows that the new layer 4 measures and the layer 2 measures in some aspects show similar results. However, when evaluating transport layer fairness, it is better to use the new layer 4 measures as they actually measure fairness on layer 4 and the results are easier to interpret

This paper proposes that fairness in wireless networks should be measured using one of the following new measures: the deterministic unfairness bound called the wireless absolute fairness bound (WAFB) or the statistical unfairness bound called the 99-percentile wireless absolute fairness bound (WAFB/sub 99/). Compared with previous fairness definitions, the new fairness measures are better suited for measuring fairness of scheduling disciplines that exploit multiuser diversity. A new scheduling discipline called opportunistic proportional fair scheduling is defined. Numerical results show that the new scheduling discipline has slightly higher throughput and slightly better fairness than proportional fair scheduling.

This paper explores the utilization of soft information in image decoding. Error free transmission is not always possible in today's nor future's communication networks without retransmissions. Retransmissions, however, introduce delay. To delay sensitive applications the concept of soft information, i.e. lower layer knowledge about the channel conditions, could reduce the number of retransmissions. It is not sufficient to treat the soft information and the image decoder separately. By combining soft information and the knowledge of the image structure in the decoding process image quality can be improved. A modified JPEG2000 image decoder that utilizes soft information has been developed. Experimental results with images transmitted over a simulated wireless channel show that iterative decoding with soft information can give high gains in image quality.

Traditionally, allocation of data in distributed database management systems has been determined by off-line anidysis and optimization.This technique works well for static database access patterns, but is often inadequate for frequently changing workloads.This paper addresses the problem of dynamically reallocating data in a partionable distributed database with changing access patterns.Rather than complicated and expensive optimization algorithms, a simple heuristic is presented and shown, via an implementation study, to improve system throughput by 3070 in a local area net work based system.For a wide area network the performance gain is expected to be even larger.It is also shown that individual site load must be taken into consideration when reallocating data.A a simple policy that incorporates load in the reallocation decision is provided.1