EXPONENTIAL-SELF-ADAPTIVE RANDOM EARLY DETECTION SCHEME FOR QUEUE MANAGEMENT IN NEXT GENERATION ROUTERS
Abstract
ensuring optimal performance in next-generation routers. Active Queue Management (AQM) scheme, has been advocated by the Internet research community for the next generation routers. Random Early Detection (RED) is the most well-known AQM scheme. However, RED lacks self-adaptation mechanism and it is susceptible to parametrization problem. Several variants of RED were developed, however all of them possess a static drop pattern; as such they are severely affected when a traffic load changes. To address the self-adaptation shortcoming of the RED and its variant schemes, Self-Adaptive Random Early Detection (SARED) scheme was developed. However, to avoid congestion, SARED aggressively drops packets once the queue length reached a certain maximum threshold limit, subsequently, this will increase the average queuing delay for networks with high traffic load conditions, therefore, to eliminate the aggressiveness of SARED in such situations, an Exponential version of SARED was proposed in this paper. Results of the simulation experiments carried out have indicated that in high traffic load situations, Exponential-SARED (ESARED) has significantly reduced average queuing delay by 4% and maximized average throughput by 3% compared to SARED and QERED.
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