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We analyze the performance of an Additive Increase Multiplicative Decrease (AIMD)-like flow control mechanism. The transmission rate is considered to increase linearly in time until the receipt of a congestion notification, when the transmission rate is multiplicatively decreased. AIMD captures the steady state behavior of TCP in the absence of timeouts and in the absence of maximum window size limitation. We introduce a general fluid model based on a multi-state Markov chain for the moments at which the congestion is detected. With this model, we are able to account for correlation and burstiness in congestion moments. Furthermore, we specify several simple versions of our general model and then we identify their parameters from real TCP traces.

Proposals to improve the performance of TCP in high speed networks have been recently put forward. Examples of such proposals include High Speed TCP, Scalable TCP, and FAST. In contrast to the additive increase multiplicative decrease algorithm used in the standard TCP, Scalable TCP uses a multiplicative increase multiplicative decrease (MIMD) algorithm for the window size evolution. In this paper, we present a mathematical analysis of the MIMD congestion control algorithm in the presence of random losses. Random losses are typical to wireless networks but can also be used to model losses in wireline networks with a high bandwidth-delay product. Our approach is based on showing that the logarithm of the window size evolution has the same behaviour as the workload process in a standard G/G/1 queue. The Laplace–Stieltjes transform of the equivalent queue is then shown to directly provide the throughput of the congestion control algorithm and the higher moments of the window size. Using ns-2 simulations, we validate our findings using Scalable TCP.