Passive available bandwidth: Applying self -induced congestion analysis of application-generated traffic

Monitoring end-to-end available bandwidth is critical in helping applications and users efficiently use network resources. Because the performance of distributed systems is intrinsically linked to the performance of the network, applications that have knowledge of the available bandwidth can adapt to changing network conditions and optimize their performance. A well-designed available bandwidth tool should be easily deployable and non-intrusive. While several tools have been created to actively measure the end-to-end available bandwidth of a network path, they require instrumentation at both ends of the path, and the traffic injected by these tools may affect the performance of other applications on the path.;We propose a new passive monitoring system that accurately measures available bandwidth by applying self-induced congestion analysis to traces of application-generated traffic. The Watching Resources from the Edge of the Network (Wren) system transparently provides available bandwidth information to applications without having to modify the applications to make the measurements and with negligible impact on the performance of applications. Wren produces a series of real-time available bandwidth measurements that can be used by applications to adapt their runtime behavior to optimize performance or that can be sent to a central monitoring system for use by other or future applications.;Most active bandwidth tools rely on adjustments to the sending rate of packets to infer the available bandwidth. The major obstacle with using passive kernel-level traces of TCP traffic is that we have no control over the traffic pattern. We demonstrate that there is enough natural variability in the sending rates of TCP traffic that techniques used by active tools can be applied to traces of application-generated traffic to yield accurate available bandwidth measurements.;Wren uses kernel-level instrumentation to collect traces of application traffic and analyzes the traces in the user-level to achieve the necessary accuracy and avoid intrusiveness. We introduce new passive bandwidth algorithms based on the principles of the active tools to measure available bandwidth, investigate the effectiveness of these new algorithms, implement a real-time system capable of efficiently monitoring available bandwidth, and demonstrate that applications can use Wren measurements to adapt their runtime decisions

Available Bandwidth Estimation Tools Metrics, Approaches and Performance

The estimation of the available bandwidth (av bw) between two end nodes through the Internet, is an area that has motivated researchers around the world in the last twenty years, to have faster and more accurate tools; Due to the utility it has in various network applications; Such as routing management, intrusion detection systems and the performance of transport protocols. Different tools use different estimation techniques but generally only analyze the three most used metrics as av bw, relative error and estimation time. This work expands the information regarding the evaluation literature of the current Available Bandwidth Estimation Tools (ABET’s), where they analyze the estimation techniques, metrics, different generation tools of cross-traf?c and evaluation testbed; Concentrating on the techniques and estimation methodologies used, as well as the challenges faced by open-source tools in high-performance networks of 10Gbps or higher

Available Bandwidth Estimation Tools Metrics, Approaches and Performance

The estimation of the available bandwidth (av_bw) between two end nodes through the Internet, is an area that has motivated researchers around the world in the last twenty years, to have faster and more accurate tools; Due to the utility it has in various network applications; Such as routing management, intrusion detection systems and the performance of transport protocols. Different tools use different estimation techniques but generally only analyze the three most used metrics as av_bw, relative error and estimation time. This work expands the information regarding the evaluation literature of the current Available Bandwidth Estimation Tools (ABET's), where they analyze the estimation techniques, metrics, different generation tools of cross-traffic and evaluation testbed; Concentrating on the techniques and estimation methodologies used, as well as the challenges faced by open-source tools in high-performance networks of 10 Gbps or higher

Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario