TCP Symbiosis

In this chapter, we introduce a robust, self-adaptive and scalable congestion control mechanism for TCP. We change the window size of a TCP connection according to the information of the physical and available bandwidths of the end-to-end network path. The bandwidth information is obtained by an inline network measurement technique. We also borrowed algorithms from biophysics to update the window size: the logistic growth model and the Lotka-Volterra competition model. The greatest advantage of using these models is that we can refer previous discussions and results for various characteristics of the mathematical models, including scalability, convergence, fairness and stability in these models. Through mathematical analysis and simulation experiments, we compare the proposed mechanism with traditional TCP Reno, HighSpeed TCP, Scalable TCP and FAST TCP, and exhibit its effectiveness in terms of scalability to the network bandwidth and delay, convergence time, fairness among competing connections, and stability.

STOCHASTICALLY SCALABLE FLOW CONTROL

Recent advances in the mathematical analysis of flow control have prompted the creation of the Scalable TCP (STCP) and Exponential RED (E-RED) algorithms. These are designed to be scalable under the popular deterministic delay stability modeling framework. In this article, we analyze stochastic models of STCP and STCP combined with E-RED link behavior. We find that under certain plausible network conditions, these probabilistic models also exhibit scalable behavior. In particular, we derive parameter choice schemes for which the equilibrium coefficients of variation of flow rates are bounded, however large, fast, or complex the network. Our model is shown to exhibit behavior similar to the mean field convergence that has recently been observed in TCP.