Departmental Papers (ESE)

Document Type

Conference Paper

Date of this Version

March 2004

Comments

Copyright 2004 IEEE. Reprinted from Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies 2004 (INFOCOM 2004), Volume 3, pages 1774-1785.
Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=29752&isYear=2004&count=63&page=1&ResultStart=25

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Abstract

Estimating end-to-end packet loss on Internet paths is important not only to monitor network performance, but also to assist adaptive applications make the best possible use of available network resources. There has been significant prior work on measuring and modeling packet loss in the Internet, but most of those techniques do not focus on providing, real-time information and on assessing path performance from an application standpoint. In this paper, we present an on-line probing-based approach to estimate the loss performance of a netework path, and extend this estimate to infer the performance that an application using the path would see. The approach relies on a hidden Markov model constructed from performance estimates generated from probes, which is then used to predict path performance as an application would experience. The accuracy of the model is evaluated using a number of different metrics, including loss rate and loss burstiness. The sensitivity of the results to measurement and computational overhead is also investigated, and an extension of the base approach using a layered model is explored as a possible solution to capturing time-varying channel behavior while keeping computational complexity reasonably low. The results we present show that the approach is capable of generating accurate, real-time estimates of path performance, and of predicting the performance that applications would experience if routed on the path.

Share

COinS
 

Date Posted: 29 April 2005