Abstract
Random Early Detection (RED) is a widely deployed active queue management algorithm that improves the overall performance of the network in terms of throughput and delay. The effectiveness of RED algorithm, however, highly depends on appropriate setting of its parameters. Moreover, the performance of RED is quite sensitive to abrupt changes in the traffic load. In this paper, we propose a Cautious Adaptive Random Early Detection (CARED) algorithm that dynamically varies maximum drop probability based on the level of traffic load to improve the overall performance of the network. Based on extensive simulations conducted using Network Simulator-2 (ns-2), we show that CARED algorithm reduces the packet drop rate and achieves high throughput as compared to RED, Adaptive RED and Refined Adaptive RED. Unlike other RED based algorithms, CARED algorithm does not introduce new parameters to achieve performance gain and hence can be deployed without any additional complexity.
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