On Control Parameters Tuning for Active Queue Management Mechanisms using Multivariate Analysis
نویسندگان
چکیده
In recent years, AQM (Active Queue Management) mechanisms, which support the end-to-end congestion control mechanism of TCP by performing congestion control at a router, have been actively studied by many researchers. AQM mechanisms usually have several control parameters, and their effectiveness depends on a setting of those control parameters. Therefore, issues on parameter tuning of several AQM mechanisms have been extensively studied using simulation experiments. However, in most of those studies, only a small number of simulation experiments are performed for investigating the effect of control parameters on the performance of AQM mechanisms. In this paper, we therefore statistically analyze a large number of simulation experiments using multivariate analysis, and quantitatively show how the performance of AQM mechanisms is affected by a setting of control parameters. In particular, we analyze the performance of three AQM mechanisms: GRED (Gentle RED), DRED (Dynamic-RED), and SRED (Stabilized RED), all of which are variants of RED (Random Early Detection). Through several numerical examples, we clarify how control parameters of GRED, DRED, and SRED have impact on their steady state performance measures such as the average queue length and the packet loss probability. We present a few guidelines for configuring control parameters of those AQM mechanisms.
منابع مشابه
Multivariate Analysis for Performance Evaluation of Active Queue Management Mechanisms in the Internet
AQM (Active Queue Management) mechanism, which performs congestion control at a router for assisting the end-to-end congestion control mechanism of TCP, has been actively studied by many researchers. For instance, RED (Random Early Detection) is a representative AQM mechanism, which drops arriving packets with a probability being proportional to its average queue length. The RED router has four...
متن کاملAn Improvement over Random Early Detection Algorithm: A Self-Tuning Approach
Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed ...
متن کاملA New Self-tuning Active Queue Management Algorithm Based on Adaptive Control
Most Active Queue Management (AQM) algorithms based on control theory have difficulty in obtaining desirable performance once the network conditions or the traffic patterns change out of the presumed ones they are designed for. To address these problems, a new self-tuning AQM algorithm called STR is proposed in this paper. STR has the ability of keeping minimum variance between the instantaneou...
متن کاملPURPLE: Predictive Active Queue Management Utilizing Congestion Information
Active Queue Management (AQM) tries to find a delicate balance between two antagonistic Internet queuing requirements: First, buffer space should be maximized to accommodate the possibly huge transient bursts; second, buffer occupation should be minimum so as not to introduce unnecessary endto-end delays. Traditional AQM mechanisms have been built on heuristics to achieve this balance, and have...
متن کاملOn Tuning of Red Parameters
Random Early Detection (RED) [1] is a widely deployed active queue management scheme in packetswitched networks. Although RED can improve packet loss rates, its performance depends severely on the tuning of its operating parameters. To alleviate the problem of parameter dependence, we propose an algorithm to adaptively vary the queue weight wq in conjunction with the maximum packet dropping pro...
متن کامل