A passive available bandwidth estimation methodology

The Available Bandwidth (AB) of an end-to-end path is its remaining capacity and it is an important metric for several applications such as overlay routing and P2P networking. That is why many AB estimation tools have been published recently. Most of these tools use the Probe Rate Model, which requires sending packet trains at a rate matching the AB. Its main issue is that it congests the path under measurement. We present a different approach: a novel passive methodology to estimate the AB that does not introduce probe traffic. Our methodology, intended to be applied between two separate nodes, estimates the path’s AB by analyzing specific parameters of the traffic exchanged. The main challenge is that we cannot rely on any given rate of this traffic. Therefore we rely on a different model, the Utilization Model. In this paper we present our passive methodology and a tool (PKBest) based on it. We evaluate its applicability and accuracy using public NLANR data traces. Our results -more than 300Gb- show that our tool is more accurate than pathChirp, a state-of-the-art active PRM-based tool. At the best of the authors’ knowledge this is the first passive AB estimation methodology.Preprin

Design of interface selection protocols for multi-homed wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University on 10 December 2010.The IEEE 802.11/802.16 standards conformant wireless communication stations have multi-homing transmission capability. To achieve greater communication efficiency, multi-homing capable stations use handover mechanism to select appropriate transmission channel according to variations in the channel quality. This thesis presents three internal-linked handover schemes, (1) Interface Selection Protocol (ISP), belonging to Wireless Local Area Network (WLAN)- Worldwide Interoperability for Microwave Access (WiMAX) environment (2) Fast Channel Scanning (FCS) and (3) Traffic Manager (TM), (2) and (3) belonging to WiMAX Environment. The proposed schemes in this thesis use a novel mechanism of providing a reliable communication route. This solution is based on a cross-layer communication framework, where the interface selection module uses various network related parameters from Medium Access Control (MAC) sub-layer/Physical Layer (PHY) across the protocol suite for decision making at the Network layer. The proposed solutions are highly responsive when compared with existing multi-homed schemes; responsiveness is one of the key factors in the design of such protocols. Selected route under these schemes is based on the most up to date link-layer information. Therefore, such a route is not only reliable in terms of route optimization but it also fulfils the application demands in terms of throughput and delay. Design of ISP protocol use probing frames during the route discovery process. The 802.11 mandates the use of different rates for data transmission frames. The ISP-metric can be incorporated into various routing aspects and its applicability is determined by the possibility of provision of MAC dependent parameters that are used to determine the best path metric values. In many cases, higher device density, interference and mobility cause variable medium access delays. It causes creation of ‘unreachable zones’, where destination is marked as unreachable. However, by use of the best path metric, the destination has been made reachable, anytime and anywhere, because of the intelligent use of the probing frames and interface selection algorithm implemented. The IEEE 802.16e introduces several MAC level queues for different access categories, maintaining service requirement within these queues; which imply that frames from a higher priority queue, i.e. video frames, are serviced more frequently than those belonging to lower priority queues. Such an enhancement at the MAC sub-layer introduces uneven queuing delays. Conventional routing protocols are unaware of such MAC specific constraints and as a result, these factors are not considered which result in channel performance degradation. To meet such challenges, the thesis presents FCS and TM schemes for WiMAX. For FCS, Its solution is to improve the mobile WiMAX handover and address the scanning latency. Since minimum scanning time is the most important issue in the handover process. This handover scheme aims to utilize the channel efficiently and apply such a procedure to reduce the time it takes to scan the neighboring access stations. TM uses MAC and physical layer (PHY) specific information in the interface metric and maintains a separate path to destination by applying an alternative interface operation. Simulation tests and comparisons with existing multi-homed protocols and handover schemes demonstrate the effectiveness of incorporating the medium dependent parameters. Moreover, show that suggested schemes, have shown better performance in terms of end-to-end delay and throughput, with efficiency up to 40% in specific test scenarios

ActiveSTB: an efficient wireless resource manager in home networks

The rapid growth of new wireless and mobile devices accessing the internet has led to an increase in the demand for multimedia streaming services. These home-based wireless connections require efficient distribution of shared network resources which is a major concern for the transport of stored video. In our study, a set-top box is the access point between the internet and a home network. Our main goal is to design a set-top box capable of performing network flow control in a home network and capable of quality adaptation of the delivered stream quality to the available bandwidth. To achieve our main goal, estimating the available bandwidth quickly and precisely is the first task in the decision of streaming rates of layered and scalable multimedia services. We present a novel bandwidth estimation method called IdleGap that uses the NAV (Network Allocation Vector) information in the wireless LAN. We will design a new set-top box that will implement IdleGap and perform buffering and quality adaptation to a wireless network based on the IdleGap’s bandwidth estimate. We use a network simulation tool called NS-2 to evaluate IdleGap and our ActiveSTB compared to traditional STBs. We performed several tests simulating network conditions over various ranges of cross traffic with different error rates and observation times. Our simulation results reveal how IdleGap accurately estimates the available bandwidth for all ranges of cross traffic (100Kbps ~ 1Mbps) with a very short observation time (10 seconds). Test results also reveal how our novel ActiveSTB outperforms traditional STBs and provides good QoS to the end-user by reducing latency and excess bandwidth consumption

Explicit congestion control algorithms for time-varying capacity media

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Quality of service routing for real-time traffic

Imperial Users onl

Machine learning-based available bandwidth estimation

Today’s Internet Protocol (IP), the Internet’s network-layer protocol, provides a best-effort service to all users without any guaranteed bandwidth. However, for certain applications that have stringent network performance requirements in terms of bandwidth, it is significantly important to provide Quality of Ser- vice (QoS) guarantees in IP networks. The end-to-end available bandwidth of a network path, i.e., the residual capacity that is left over by other traffic, is deter- mined by its tight link, that is the link that has the minimal available bandwidth. The tight link may differ from the bottleneck link, i.e., the link with the minimal capacity. Passive and active measurements are the two fundamental approaches used to estimate the available bandwidth in IP networks. Unlike passive measurement tools that are based on the non-intrusive monitoring of traffic, active tools are based on the concept of self-induced congestion. The dispersion, which arises when packets traverse a network, carries information that can reveal relevant network characteristics. Using a fluid-flow probe gap model of a tight link with First-in, First-out (FIFO) multiplexing, accepted probing tools measure the packet dispersion to estimate the available bandwidth. Difficulties arise, how- ever, if the dispersion is distorted compared to the model, e.g., by non-fluid traffic, multiple tight links, clustering of packets due to interrupt coalescing and inaccurate time-stamping in general. It is recognized that modeling these effects is cumbersome if not intractable. To alleviate the variability of noise-afflicted packet gaps, the state-of-the-art bandwidth estimation techniques use post-processing of the measurement results, e.g., averaging over several packet pairs or packet trains, linear regression, or a Kalman filter. These techniques, however, do not overcome the basic as- sumptions of the deterministic fluid model. While packet trains and statistical post-processing help to reduce the variability of available bandwidth estimates, these cannot resolve systematic deviations such as the underestimation bias in case of random cross traffic and multiple tight links. The limitations of the state-of-the-art methods motivate us to explore the use of machine learning in end-to-end active and passive available bandwidth estimation. We investigate how to benefit from machine learning while using standard packet train probes for active available bandwidth estimation. To reduce the amount of required training data, we propose a regression-based scale- invariant method that is applicable without prior calibration to networks of arbitrary capacity. To reduce the amount of probe traffic further, we implement a neural network that acts as a recommender and can effectively select the probe rates that reduce the estimation error most quickly. We also evaluate our method with other regression-based supervised machine learning techniques. Furthermore, we propose two different multi-class classification-based meth- ods for available bandwidth estimation. The first method employs reinforcement learning that learns through the network path’s observations without having a training phase. We formulate the available bandwidth estimation as a single-state Markov Decision Process (MDP) multi-armed bandit problem and implement the ε-greedy algorithm to find the available bandwidth, where ε is a parameter that controls the exploration vs. exploitation trade-off. We propose another supervised learning-based classification method to ob- tain reliable available bandwidth estimates with a reduced amount of network overhead in networks, where available bandwidth changes very frequently. In such networks, reinforcement learning-based method may take longer to con- verge as it has no training phase and learns in an online manner. We also evaluate our method with different classification-based supervised machine learning techniques. Furthermore, considering the correlated changes in a network’s traffic through time, we apply filtering techniques on the estimation results in order to track the available bandwidth changes. Active probing techniques provide flexibility in designing the input struc- ture. In contrast, the vast majority of Internet traffic is Transmission Control Protocol (TCP) flows that exhibit a rather chaotic traffic pattern. We investigate how the theory of active probing can be used to extract relevant information from passive TCP measurements. We extend our method to perform the estima- tion using only sender-side measurements of TCP data and acknowledgment packets. However, non-fluid cross traffic, multiple tight links, and packet loss in the reverse path may alter the spacing of acknowledgments and hence in- crease the measurement noise. To obtain reliable available bandwidth estimates from noise-afflicted acknowledgment gaps we propose a neural network-based method. We conduct a comprehensive measurement study in a controlled network testbed at Leibniz University Hannover. We evaluate our proposed methods under a variety of notoriously difficult network conditions that have not been included in the training such as randomly generated networks with multiple tight links, heavy cross traffic burstiness, delays, and packet loss. Our testing results reveal that our proposed machine learning-based techniques are able to identify the available bandwidth with high precision from active and passive measurements. Furthermore, our reinforcement learning-based method without any training phase shows accurate and fast convergence to available band- width estimates

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Measuring the State of ECN Readiness in Servers, Clients, and Routers

Proceedings of the Eleventh ACM SIGCOMM/USENIX Internet Measurement Conference (IMC 2011), Berlin, DE, November 2011.Better exposing congestion can improve traffic management in the wide-area, at peering points, among residential broadband connections, and in the data center. TCP's network utilization and efficiency depends on congestion information, while recent research proposes economic and policy models based on congestion. Such motivations have driven widespread support of Explicit Congestion Notification (ECN) in modern operating systems. We reappraise the Internet's ECN readiness, updating and extending previous measurements. Across large and diverse server populations, we find a three-fold increase in ECN support over prior studies. Using new methods, we characterize ECN within mobile infrastructure and at the client-side, populations previously unmeasured. Via large-scale path measurements, we find the ECN feedback loop failing in the core of the network 40\% of the time, typically at AS boundaries. Finally, we discover new examples of infrastructure violating ECN Internet standards, and discuss remaining impediments to running ECN while suggesting mechanisms to aid adoption