On the Scalable Fairness and Efficient Active Queue Management of RED

Abstract

Internet routers generally see packets from a fast flow more often than a slow flow. This suggests that network fairness may be improved without per-flow information. In this paper, we propose a scheme using Most Recently Used List (MRUL)-a list storing statistics of limited active flows that sorted in most recently seen first mode-to improve the fairness of RED. Based on the list, our proposed scheme jointly considers the identification and punish of the fast and unresponsive fast flows, and the protection of slow flows. Its performance improvements are demonstrated with extensive simulations. Different from the previous proposals, the complexity of our proposed scheme is proportional to the size of the MRUL list but not coupled with the queue buffer size or the number of active flows, so it is scalable and suitable for various routers. In addition, another issue we address in this paper is queue management in RED. Specifically, we replace the linear packet dropping function in RED by a judicially designed nonlinear quadratic function, while original RED remains unchanged. We call this new scheme Nonlinear RED, or NLRED. The underlying idea is that, with the proposed nonlinear packet dropping function, packet dropping becomes gentler than RED at light traffic load but more aggressive at heavy load. As a result, at light traffic load, NLRED encourages the router to operate in a range of average queue sizes rather than a fixed one. When the load is heavy and the average queue size approaches the pre-determined maximum threshold (i.e. the queue size may soon get out of control), NLRED allows more aggressive packet dropping to back off from it. Simulations demonstrate that NLRED achieves a higher and more stable throughput than RED and REM. Since NLRED is fully compatible with RED, we can easily upgrade/replace the existing RED implementations by NLRED

Comparative Analysis of Drop Tail, Red and NLRED Congestion Control Algorithms Using NS2 Simulator

“…[7] submitted his dissertation in which the three mechanisms Drop Tail (DT), Early Random Drop (ERD) and Multithreshold Adaptive Early Random Drop (MTAERD) are compared using a Java framework and outcomes are showing the total improvement in the quality of service that can be obtained by the mechanisms over their non-adaptive supplements. One approach in this area was given by [4], in which they replaced RED based on linear packet dropping function by NLRED which is based on nonlinear quadratic function. NLRED is gentler than RED which improves the performance.…”