Performance Analysis of Diverse Active Queue Management Algorithms

Congestion is a problem which can affect a computer’s network performance, typically occurring when the available resources of the network cannot deal with arriving packets, thus deteriorating performance. Many research studies have acknowledged various Active Queue Management (AQM) methods to alleviate congested networks in an attempt to improve resource management and performance. However, the performance of AQM methods differs significantly and, more importantly, can be influenced by the level of congestion (light, moderate, or heavy). This paper empirically compares three different AQM methods, Curvilinear Random Early Detection (CLRED), Three-section Random Early Detection (TRED) based on nonlinear RED and Enhanced Adaptive Gentle Random Early Detection (Enhanced AGRED), to identify which method most effectively manages congestion in a single queue node system and also at a router buffer in a queueing network system. This router buffer may have more arriving packets than the departing packet. These methods are compared based on distinctive criteria including packet arrival probability, maximum packet-dropping probability and packet arrival probability (Alpha 1 or $$\alpha1$$ ), among others. The comparisons suggest that Enhanced AGRED provides better performance results than CLRED and TRED, with respect to mean queue length, average queueing delay, and overflow packet loss probability, especially when there is high congestion, and for these performance measures, the most satisfactory results are obtained for Enhanced AGRED when the value of maximum packet-dropping probability is set to the smallest given value. In addition, this paper presents the Post-quantum cryptography (PQC) in Networks to protect data traffic against conventional and quantum attacks.