A control theoretic analysis of RED

Abstract

In this paper we use a previously developed nonlinear dynamic model of TCP to analyze and design Active Queue Management (AQM) control systems using RED. First, we linearize the interconnection of TCP and a bottlenecked queue and discuss its feedback properties in terms of network parameters such as link capacity, load and round-trip time. Using this model, we next design an AQM control system using the random early detection (RED) scheme by relating its free parameters such as the low-pass filter break point and loss probability profile to the network parameters. We present guidelines for designing linearly stable systems subject to network parameters like propogation delay and load level. Robustness to variations in system loads is a prime objective. We present ns simulations to support our analysis.

Understanding the performance of many TCP flows

“…There has been a vast volume of research on studying RED dropping policy, ranging from analytical modeling [5,31] to simulations and experimental evaluation [45,10,8,48], from recommendations for parameters and architecture configuration [18,19,13,22] to proposals of alternatives, which include BLUE [12], SRED (Stabilized RED) [34], Adaptive RED [11], FRED (Fair Random Early Drop) [28], and BRED (Balanced RED) [4]. Our simulation study of RED performance complements the existing body of research by comparing the aggregate throughput, goodput, loss rate, and roundtrip bias of TCP connections under RED gateways with those under drop-tail gateways.…”

Emulating AQM from end hosts

“…The simulation results demonstrate that stability of the system is guaranteed under condition (11) and becomes unstable when R is increased to 175 ms (which exceeds the stability boundary 171 ms). Lack of necessity of the stability condition is also pointed out in the context of TCP/RED in [15]. However, Theorem 1 still serves as a general guideline for choosing PERT parameters.…”

The use of a non-integer order PI controller with an active queue management mechanism

“…In this case we include a more detailed Internet traffic model reflecting self-similarity, a feature frequently observed in traffic and having decisive impact on the quality of service. Section 4 provides simulation Misra et al (2000), as well as Hollot et al (2001a), which uses fluid flow and stochastic differential equation analysis. The model ignores TCP timeout mechanisms and allows obtaining the average value of key network variables.…”