A new method for analysing feedback-based protocols with applications to engineering Web traffic over the Internet

Abstract

Most of the studies of feedback-based flow and congestion control consider only persistent sources which always have data to send. However, with the rapid growth of Internet applications built on TCP/IP such as the World Wide Web and the standardization of traffic management schemes such as Available Bit Rate (ABR) in Asynchronous Transfer Mode (ATM) networks, it is essential to evaluate the performance of feedback-based protocols using traffic models which are specific to dominant applications. This paper presents a method for analysing feedback-based protocols with a Web-user-like input traffic where the source alternates between "transfer" periods followed by "think" periods. Our key results, which are presented for the TCP protocol, are:(1) The goodputs and the fraction of time that the system has some given number of transferring sources are insensitive to the distributions of transfer (file or page) sizes and think times except through the ratio of their means. Thus, apart from network round-trip times, only the ratio of average transfer sizes and think times of users need be known to size the network for achieving a specific quality of service.(2) The Engset model can be adapted to accurately compute goodputs for TCP and TCP over ATM, with different buffer management schemes. Though only these adaptations are given in the paper, the method based on the Engset model can be applied to analyze other feedback systems, such as ATM ABR, by finding a protocol specific adaptation. Hence, the method we develop is useful not only for analysing TCP using a source model significantly different from the commonly used persistent sources, but also can be useful for analysing other feedback schemes.(3) Comparisons of simulated TCP traffic to measured Ethernet traffic shows qualitatively similar autocorrelation when think times follow a Pareto distribution with infinite variance. Also, the simulated and measured traffic have long range dependence. In this sense our traffic model, which purports to be Web-user-like, also agrees with measured traffic.