Cesar D. Guerrero

On the applicability of available bandwidth estimation techniques and tools

Available Bandwidth Estimation Techniques and Tools (ABETTs) have recently been envisioned as a supporting mechanism in areas such as compliance of service level agreements, network management, traffic engineering and real-time resource provisioning, flow and congestion control, construction of overlay networks, fast detection of failures and network attacks, and admission control. However, it is unknown whether current ABETTs can run efficiently in any type of network, under different network conditions, and whether they can provide available bandwidth estimates at the timescales needed by these applications. This article includes a performance evaluation of Pathload, Pathchirp, Spruce, IGI, and Abing in a low cost and flexible test bed. The evaluation includes scenarios and conditions not evaluated before, such as varying the packet loss rate and the propagation delays of the links, the amount of cross-traffic, the capacity of the links, and the cross-traffic packet size. The results demonstrate that ABETTs are far from being ready to be applied in all these applications and scenarios. In addition, the article clearly indicates those aspects that need further research and which ABETTs are the best candidates for specific applications and environments.

Traceband: A fast, low overhead and accurate tool for available bandwidth estimation and monitoring

Available bandwidth estimation techniques are being used in network monitoring and management tools to provide information about the utilization of the network and verify the compliance of service level agreements. However, the use of these techniques in other applications and network environments is limited by the long convergence times, accuracy errors, and the amount of overhead that they introduce. In this paper, we introduce Traceband, a hidden Markov model-based technique for end-to-end available bandwidth estimation and monitoring that improves these performance metrics and therefore promises to expand the use of these techniques in other scenarios. Traceband is evaluated and compared with Spruce and Pathload using Poisson and self-similar cross-traffic. Experimental results in a controlled environment with Poisson cross-traffic demonstrate that Traceband is as accurate as Spruce and Pathload but considerably faster, and introduces less overhead. Tracebands convergence time is demonstrated using bursty cross-traffic, as it is the only tool that accurately reacts to zero-traffic periods, which may be particularly useful for those applications that need to make decisions in real time. Using self-similar traffic, Tracebands mean accuracy and variability degrade with the Hurst parameter but it still performs within reasonable limits. A general and optional moving average algorithm is also introduced to solve these issues.

Experimental and Analytical Evaluation of Available Bandwidth Estimation Tools

In this paper, we present a low cost and flexible testbed to evaluate the performance of available bandwidth estimation tools in a common and controlled environment. In addition, we include a model based on a network of M/M/I queues to have an analytical reference to compare the experiments with. We then utilize the proposed testbed and model to evaluate the performance of Pathload, IGI, and Spruce, three well-known bandwidth estimation tools. Our main results indicate that Pathload is the most accurate tool but the slowest to converge. IGI, on the other hand, is the fastest tool but the least accurate. Spruce is the least intrusive tool with intermediate accuracy and convergence time

A Hidden Markov Model approach to available bandwidth estimation and monitoring

Available bandwidth estimation techniques are being used in network monitoring and management tools to provide information about the utilization of the network and verify the compliance of service level agreements. However, the use of these techniques in other applications and network environments is limited by the convergence time, accuracy, and amount of overhead that they introduce. In this paper, we propose a Hidden Markov Model-based technique to end-to-end available bandwidth estimation and monitoring that improves these performance metrics and therefore promises to expand the use of these techniques in other scenarios. The estimator, which has been implemented in a new tool called Traceband, is as accurate as Spruce and Pathload but considerably faster, and introduce far less overhead. In addition, when compared using bursty cross-traffic, Traceband is the only tool that accurately reacts to zero-traffic periods, which may be particularly useful for those applications that need to make decisions in real time.

On the implementation of a capacity estimator for wireless ad hoc networks

End-to-end capacity is a useful metric for network applications such as traffic engineering, QoS verification, peer-to-peer file distribution, video/audio streaming, admission control, etc. Although several tools have been developed to estimate end-to-end capacity and available bandwidth in wired networks, estimators for wireless ad-hoc networks are still to be developed. The paper briefly describes the model of a new capacity estimation tool for wireless ad-hoc networks. Then, it discusses two critical aspects in the implementation of any estimation tool: time stamping packets and clock synchronization. The paper details how to time stamp packets under the Linux and Windows operating systems, and a describes a regression-based mechanism that eliminates measurement errors due to clock drifts. The time stamping methods and the regression algorithm are implemented in the capacity estimator, which shows accurate capacity estimations.