Fuzzy Comprehensive Random Early Detection of Router Congestion

Abstract

The queue length and the load rate should be monitored to overcome the problem of router congestion due to the increase in network utilization and achieve a high-speed transmission. Previous active queue management methods manage the queued packets in the router buffer to maintain high network performance. However, these methods depend on monitoring indicators that do not cover all the congestion signs, leading to packet loss and delay. Accordingly, all the congestion signs should be wrapped into these indicators and managed by an algorithm that randomly drops packets to avoid global synchronization, loss, and delay. In this paper, a fuzzy comprehensive random early detection (FCRED) is proposed to deal with the gap in network monitoring and congestion control at the router buffer. FCRED is built by using three indicators, which monitor the router's arrival, departure, and queue length. Accordingly, a fuzzy inference process is developed to manage these indicators and calculate the dropping probability (Dp). Simulation results show that FCRED improves loss and packet dropping under various network statuses compared with RED, BLUE, and ERED. In terms of loss, FCRED achieves zero loss at high congested status. For dropping, FCRED achieves an optimal rate of 0.47 with an arrival rate of 0.95. For the throughput and delay, FCRED achieves the best results. Accordingly, the proposed FCRED method achieves zero loss and reduces packet dropping from 0.28 to 0.21, a 25% reduction compared with the best performance of these methods. Compared with recent fuzzy-based methods, the proposed FCRED achieves comparable results and outperforms them by dropping more packets to avoid loss, which in such case is necessary dropping.

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

“…The Proportional Integral (PI) controller [13], which is among the most utilized AQM methods, extends RED by incorporating delay as a congestion indicator. The experiments proved that RED and PI outperformed the drop-tail but still suffer from unnecessary packet dropping and packet loss [8].…”

“…However, these methods exhibit performance issues under bursty traffic conditions. The adaptive methods stabilize the AQM performance by enabling self-adaptation based on traffic or queue status [8]. The problem with these methods is using equal increasing and decreasing rates, leading to a slow response in sudden network load changes.…”

“…A low decreasing rate characterizes the slow reduction of Dp in BLUE. While a high reduction rate might seem like a potential solution, it could introduce instability in the algorithm dealing with the bursting nature of the traffic [8].…”

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

“…Fuzzy BLUE (FBLUE) [38] used buffer size and packet loss as input variables, Fuzzy GREEN [39] used Q and dropping rate, Fuzzy-logic Controller-based RED [40] used AQL and Pl, and Fuzzy-logic RED (FLRED) [41] used the delay and AQL as the input variables. Fuzzy Comprehensive RED (FCRED) [42] used three indicators, which monitor the router's arrival, departure, and queue length. Table 2 summarizes the existing work on fuzzy-based AQM methods.…”