Abstract
We present an empirical study of the effects of active queue management (AQM) and explicit congestion notification (ECN) on the distribution of response times experienced by users browsing the Web. Three prominent AQM designs are considered: the proportional integral (PI) controller, the random exponential marking (REM) controller, and adaptive random early detection (ARED). The effects of these AQM designs were studied with and without ECN. Our primary measure of performance is the end-to-end response time for HTTP request-response exchanges. Our major results are as follows. If ECN is not supported, ARED operating in byte-mode was the best performing design, providing better response time performance than drop-tail queueing at offered loads above 90% of link capacity. However, ARED operating in packet-mode (with or without ECN) was the worst performing design, performing worse than drop-tail queueing. ECN support is beneficial to PI and REM. With ECN, PI and REM were the best performing designs, providing significant improvement over ARED operating in byte-mode. In the case of REM, the benefit of ECN was dramatic. Without ECN, response time performance with REM was worse than drop-tail queueing at all loads considered. ECN was not beneficial to ARED. Under current ECN implementation guidelines, ECN had no effect on ARED performance. However, ARED performance with ECN improved significantly after reversing a guideline that was intended to police unresponsive flows. Overall, the best ARED performance was achieved without ECN. Whether or not the improvement in response times with AQM is significant, depends heavily on the range of round-trip times (RTTs) experienced by flows. As the variation in flows' RTT increases, the impact of AQM and ECN on response-time performance is reduced.We conclude that AQM can improve application and network performance for Web or Web-like workloads. In particular, it appears likely that with AQM and ECN, provider links may be operated at near saturation levels without significant degradation in user-perceived performance.
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