Performance evaluation of multiple time scale TCP under self-similar traffic conditions

• Authors: Kihong Park;Tsunyi Tuan
• Venue: ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on modeling and simulation of communication networks
• Year: 2000

Measurements of network traffic have shown that self-similarity is a ubiquitous phenomenon spanning across diverse network environments. In previous work, we have explored the feasibility of exploiting long-range correlation structure in self-similar traffic for congestion control. We have advanced the framework of multiple time scale congestion control and shown its effectiveness at enhancing performance for rate-based feedback control. In this article, we extend the multiple time scale control framework to window-based congestion control, in particular, TCP. This is performed by interfacing TCP with a large time scale module that adjusts the aggressiveness of bandwidth consumpton behavior exhibited by TCP as a function of large time scale network state, that is, information that exceeds the time horizon of the feedback loop as determined by RTT. How to effectively utilize such information—due to its probabilistic nature, dispersion over multiple time scales, and realization on top of existing window-based congestion controls—is a nontrivial problem. First, we define a modular extension of TCP (a function call with a simple interface that applies to various flavors of TCP, e.g., Tahoe, Reno, and Vegas) and show that it significantly improves performance. Second, we show that multiple time scale TCP endows the underlying feedback control with proacativity by bridging the uncertainty gap associated with reactive controls which is exacerbated by the high delay-bandwidth product in broadband wide area networks. Third, we investigate the influence of three traffic control dimensions—tracking ability, connection duration, and fairness—on performance. Performance evaluation of multiple time scale TCP is facilitated by a simulation benchmark environment based on physical modeling of self-similar traffic. We explicate our methodology for disc