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 paper, 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 control module which adjusts the aggressiveness of bandwidth consumption behavior exhibited by TCP as a function of "large time scale " network state, i.e., information that exceeds the 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 affection on top of existing window-based congestion controls---is a nontrivial problem. Our contribution is threefold. First, we define a modular extension of TCP---a function call with a simple interface---that applies to various flavours of TCP---e.g., Tahoe, Reno, Vegas---
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