Generation and Validation of Synthetic Internet Traffic

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This research project focuses on a critical problem in network simulations, i.e. the gener-ating of application-dependent, network-independent synthetic traffic that corresponds to a valid, contemporary model of application or user behavior. Specifically, an abstract rep-resentation of network connections will be investigated that captures the dynamics of both end-user interactions and application-level protocols. The representation, called an a-b-t trace, models connections as a series of request/response exchanges separated by inter-exchange think times. Network packet traces are "reverse compiled" into a collec-tion of a-b-t traces that serve as inputs to a synthetic traffic generation engine. The engine will, through a variety of techniques, sample from a collection of a-b-t traces to generate network-independent synthetic traffic that is statistically similar to the original packet trace. This project will investigate a variety of traffic generation techniques and will em-pirically and mathematically validate each. Furthermore, the use of statistical cluster analysis to identify subsets of a-b-t traces will be investigated that correspond to applica-tion connections that are generating statistically homogeneous traffic. The premise of the proposed cluster analysis work is that while literally tens of thousands of port pairs are in use at any one time, the number of distinct types of applications that are in use is far smaller. Beyond enabling better-controlled simulations, the proposed cluster analysis techniques will likely allow providers to better understand the fundamental make-up and structure of traffic currently seen on their networks. For example, instead of seeing 20 thousand active connections on seemingly random port pairs they can identify the 5-10 fundamental traffic classes present. The results of this research will contribute to more accurate and realistic simulations and hence a deeper understanding of the merits of pro-posed network technologies. Finally, the abstract network-independent characterization of network connections can be used to understand the fundamental makeup and evolution of Internet traffic.