Sanjeewa Athuraliya

REM: active queue management

We describe a new active queue management scheme, random exponential marking (REM), that aims to achieve both high utilization and negligible loss and delay in a simple and scalable manner. The key idea is to decouple the congestion measure from the performance measure such as loss, queue length, or delay. While the congestion measure indicates excess demand for bandwidth and must track the number of users, the performance measure should be stabilized around their targets independent of the number of users. We explain the design rationale behind REM and present simulation results of its performance in wireline and wireless networks.

An enhanced random early marking algorithm for Internet flow control

We propose an optimization based flow control for the Internet called random early marking (REM). In this paper we propose and evaluate an enhancement that attempts to speed up the convergence of REM in the face of large feedback delays. REM can be regarded as an implementation of an optimization algorithm in a distributed network. The basic idea is to treat the optimization algorithm as a discrete time system and apply linear control techniques to stabilize its transient. We show that the modified algorithm is stable globally and converges exponentially locally. This algorithm translates into an enhanced REM scheme and we illustrate the performance improvement through simulation.

Optimization flow control with Newton-like algorithm

We proposed earlier an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. The control mechanism is derived as a gradient projection algorithm to solve the dual problem. In this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix approximates the diagonal terms of the Hessian and can be computed at individual links using the same information required by the unscaled algorithm. We prove the convergence of the scaled algorithm and present simulation results that illustrate its superiority to the unscaled algorithm.

Random Early Marking

Random Early Marking (REM) consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Marking allows a source to estimate its path congestion measure and adjusts its rate in a way that aligns individual optimality with social optimality. We describe the REM algorithm, summarize its key properties, and present some simulation results that demonstrate its stability, fairness and robustness.

An empirical validation of a duality model of TCP and queue management algorithms

In this paper we validate through simulations a duality model of TCP and active queue management (AQM) proposed earlier. In this model, TCP and AQM are modeled as carrying out a distributed primal-dual algorithm over the Internet to maximize aggregate source utility. TCP congestion avoidance algorithms, such as Reno and Vegas, iterate on source rates, the primal variable. AQM algorithms, such as RED and REM, iterate on marking probability, the dual variable.

Simulation comparison of RED and REM

We proposed earlier (Athuraliya et al. 2000) an optimization based flow control for the Internet called random exponential marking (REM). REM consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Because of the finer measure of congestion provided by REM, sources do not constantly probe the network for spare capacity, but settle around a globally optimal equilibrium, thus avoiding the perpetual cycle of sinking into and recovering from congestion. In this paper we compare the performance of REM with Reno over RED (random early detection) through simulation.

Price computation in random early marking (REM)

We proposed earlier a flow control algorithm derived from solving the dual of a welfare maximization problem. The algorithm however requires communication between network links and sources that is not achievable on the current Internet. We then extended the basic algorithm to a random early marking (REM) scheme which can be implemented using only binary feedback. We proposed a new price computation algorithm for REM and present simulation results to illustrate its superior performance over the previous versions.

Faster parameter estimation using risk-sensitive filters

In this paper, we propose a risk-sensitive approach to parameter estimation for hidden Markov models (HMM). The parameter estimation approach considered exploits estimation of various functions of the state, based on model estimates. We propose certain practical suboptimal risk-sensitive filters to estimate the various functions of the state during transients, rather than optimal risk-neutral filters as in earlier studies. The estimates are asymptotically optimal, if asymptotically risk neutral, and can give significantly improved transient performance, which is a very desirable objective for certain engineering applications. To demonstrate the improvement in estimation simulation studies are presented that compare parameter estimation based on risk-sensitive filters with estimation based on risk-neutral filters.