Tuning the Temporal Characteristics of a Kalman-Filter Method for End-to-End Bandwidth Estimation

Abstract

resolution may be different for various applications.Abstract-In this paper we present a way of tuning the The time scale of bandwidth tracking offered by BART is temporal characteristics of a new available-bandwidth related to two adjustable properties. The obvious one is the estimation method, BART. The estimation engine in this inter-probing time. In the example above, when we probe method is Kalman-filter based. A current estimate of the once per second, we cannot hope to accurately track the available bandwidth is maintained, and for each new sequence bandwidth fluctuations at any time resolution better than of probe packet pairs an updated estimate is produced. The main input parameters needed by the Kalman filter are the approximately two seconds (cf. the sampling theorem in variance of the measurement noise and the covariance of the information theory). By decreasing the inter-probing time, process noise. The former is measured by the method, whereas an improved tracking performance is expected; however, the the latter is not in general attainable by analytical or empirical trade-off is the larger amount of probe traffic affecting the investigation. Instead, it is reasonable to treat this as a tunable network. The more subtle adjustable property is the process parameter. We discuss how the temporal characteristics of the noise covariance, Q, one of the crucial filter parameters.tracking of end-to-end available bandwidth may be tuned. This is a 2x2 matrix in the BART formalism.In the present paper, we explore how the elements of Q could be chosen so as to optimize the tracking performance I. INTRODUCTION of BART for desired bandwidth variability. We introduce A. Overview and apply a specific variability measure, which captures the The capability of estimating end-to-end available effect of both time resolution and traffic aggregation. bandwidth is useful in several contexts, including service B. Related Work level agreement verification, network monitoring and server Several other bandwidth estimation methods have been selection. Estimation of bandwidth in real-time, with per-proposed [3, 4, 5, 6, 7, 8, 9, 10]. These methods have been sample update, opens up for many applications, including compared for performance [11, 12]. adaptation based on available bandwidth directly (rather In a seminal paper on packet-pair techniques [13], than measures such as loss or delay) in e.g. congestion Keshav discussed using a Kalman filter for the estimation of control and streaming of audio and video. "bottleneck service rate" for end-point flow control Estimation methods based on a filtering approach possess purposes, and concluded that this would not be practical. this compelling feature of producing an updated estimate for However, his analysis rested on the assumption that queuing each new sampling of the system properties. For instance, service in network nodes is based on stateful flow-based by probing the network with probe packets once every round-robin instead of stateless first-come first-served second, one ...

Change detection and estimation for network-measurement applications

“…That is, the magnitude of the residuals becomes smaller for every filter iteration, which increases the impact of process noise and measurement noise on the residual credibility. Hence, less information about the change is available as k increases and after an initial period we only expect minor improvements of the η estimate; this is described by the change-information matrix C (19). From Figure 7 (b) we can initially observe a rather high error covariance at around 750 seconds, although it quickly decreases and after approximately 50 seconds the change estimate will not improve significantly.…”

New filter techniques to improve the accuracy of available bandwidth measurement

“…applied the Kalman filter [15] to non-linear models of the problem of current bandwidth measurement, called the Bandwidth Available in Real-Time (BART) [10], and have shown positive results, regarding both the reliability and the speed of estimation. The authors also published a paper on using temporal characteristics of the Kalman filter to estimate current bandwidth [16]. Along this line, these authors also published a work that applies the Kalman filter in the tool: Cusum [17].…”

Enhanced Network-State Estimation using Change Detection

“…BART uses active probing and Kalman filtering in order to maintain and update an estimate of the current available bandwidth. It may be configured for optimum performance with regard to the expected variability of the system state [2].…”