End-to-end rate-based congestion control: convergence properties and scalability analysis
In this paper, we study several properties of binary-feedback congestion control in rate-based applications. We first derive necessary conditions for generic binary-feedback congestion control to converge to fairness monotonically (which guarantees asymptotic stability of the fairness point) and show that AIMD is the only TCP-friendly binomial control with monotonic convergence to fairness. We then study steady-state behavior of binomial controls with n competing flows on a single bottleneck. Our main result here shows that combined probing for new bandwidth by all flows results in significant overshoot of the available bandwidth and rapid (often super-linear as a function of n) increase in packet loss. We also show that AIMD has the best scalability and lowest packet-loss increase among all TCP-friendly binomial schemes. We conclude the paper by deriving the conditions necessary to achieve constant packet loss regardless of the number of competing flows n and examine one new scheme with such constant packet loss called ideally scalable congestion control in both simulation and streaming experiments.