Time scale analysis and scalability issues for explicit rate allocation in ATM networks

Abstract

End-to-end rate based congestion management has been extensively studied for control of computer communication networks. We examine some of the properties of end-to-end rate based algorithms for congestion management using explicit rate feedback, to achieve maxmin fair rate allocation in the network. We formulate a requirement on a switch's local rate allocation computation sufficient to ensure convergence of the distributed algorithm to the global maxmin fair rate allocation. We provide an upper bound on the convergence time when the switch algorithm satisfies this requirement. We propose a requirement of feasibility of the transmission rates, maintaining which results in oscillation-free operation of the algorithm while maintaining small queues at the switches. Feasibility implies that the capacity of any of the resources is not exceeded at any time. We suggest a source policy to ensure feasibility of transmission rates, the essence of which is to delay a rate increase by a certain time, while decreasing the transmission rate immediately upon receiving the appropriate feedback. Finally, we seek ways of improving scalability of the computation of maxmin fair rates in the presence of a large number of flows. We suggest using a discrete set of rates, which as we show, reduces the computational complexity and the state requirement at the switches.