Analysis of dynamic congestion control protocols

Abstract

We present an approximate analysis of a queue with dynamically changing input rates that are based on implicit or explicit feedback information. This is motivated by proposals for adaptive congestion control algorithms [RaJa 88, Jac 88], where the sender's window size at the transport level is adjusted based on perceived congestion level of a bottleneck node. We develop an analysis methodology for a simplified system; however, it is powerful enough to answer the important questions regarding stability, convergence (or oscillations), fairness and the significant effect that delayed feedback plays on performance. SpecificsJly, we find that, in the absence of feedback delay, the linear increase/exponential decrease algorithm of Jacobson and Ramakrishnan-Jain [Jac 88, RaJa 88] is provably stable and fair. Delayed feedback, on the other hand, introduces oscillations for every individual user as well as unfairness across those competing for the same resource. While the simulation study of Zhang [Zha 89] and the fluid-approximation study of Bolot and Shankar [BoSh 90] have observed the oscillations in cumulative queue length and measurements by Jacobson [Jac 88] have revealed some of the unfairness properties, the reasons for these have not been identified. This study quantitatively identifies the cause of the these effects, vis-a-vis the system parameters and properties of the algorithm used. The model presented here is fairly general and can be applied to evaluate the performance of a wide range of feedback control schemes. It is an extension of the classical Fokker-Planck equation. Because the Fokker-Planck equation models diffusion, it addresses traflic variability that fluid approximation techniques do not.

Multiple time scale congestion control for self-similar network traffic

“…In a nutshell, SAC will try to aggressively soak up bandwidth if it predicts the future network state to be \idle," adjusting the level of aggressiveness as a function of the predicted idleness. Congestion control has been an active area of networking research spanning over two decades with a urry of work carried out in the late '80s and early '90s [5,6,15,18,23,24,26,29,30,33,35,37]. Gerla and Kleinrock [15] laid down much of the early groundwork and Jacobson [23] has been instrumental in inuencing the practical mechanisms that have survived until today.…”

“…Gerla and Kleinrock [15] laid down much of the early groundwork and Jacobson [23] has been instrumental in inuencing the practical mechanisms that have survived until today. A central part of the investigation has been the study of stability and optimality issues [5,12,23,24,29,30,33,37] associated with feedback congestion control. A taxonomy for classifying the various protocols can be found in [42].…”

Rate control algorithms for the ATM ABR service

“…BoIot and Shankar [13, 141 also present an analysis of both packet and fluid versions of the JRI version of the binary feedback scheme where the source increases its transmission rate linearly in time if the congestion bit is set to zero and decreases its rate exponentially fast in time if the congestion bit is set to one. Mukheqee and Strikwerda [42] derive a diffusion model for the joint probability density function of the queue length and arrival rate at the bottleneck node.…”

Analysis of An End-to-End Proportional Bandwidth Allocation Algorithm

“…Analysis in proving the stability of a general feedback congestion mechanism with linear increase and multiplicative decrease has recently been studied in (Altman , 1994), (Keshav, 1991), (Mukherjee, 1994). ARB belongs to this class of algorithm .…”