Abstract
Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.
Highlights
With the fast development in computer hardware and data communications, Quality of Service (QoS) in computer networks has become an important concern for end users [1, 2]
random early detection (RED), Nonlinear RED (NLRED), and RED_E algorithms are compared with reference to the following performance measures: mql, T, D, PL, and DP. e abbreviation mql denotes mean queue length; T is the throughput that represents the number of packets that have been successfully passed through a queue node for every time unit; D is the average queuing delay for packets; PL is the probability of packet loss due to buffer overflow; and DP is the packet dropping probability before a router buffer becomes full. is comparison aims to evaluate RED_E performance in different congestion situations and without utilizing the Dmax parameter
Further simulation tests between RED, NLRED, and RED_E based on different values for min threshold to evaluate their effect on performance results were conducted. α was set to 0.78 since this value can produce heavy congestion and we needed to evaluate the effectiveness of the min threshold parameter on the compared techniques in the presence of heavy congestion. e min threshold was set to different values, ranging from 3 to max threshold − 1 to observe the effectiveness of each min threshold value on the performance measure results. e performance measure results of RED, NLRED, and RED_E versus min threshold values are given in Figures 10–14. e chart type used in this subsection is scatter because the results of the compared methods can be shown clearly
Summary
With the fast development in computer hardware and data communications, Quality of Service (QoS) in computer networks has become an important concern for end users [1, 2]. Many researchers have proposed congestion control techniques [4–9, 10--12] that are implemented via simulation to enhance network performance with different QoS requirements [1, 13, 14]. We would like to minimize this problem by proposing a new method that relaxes the dependency on the Dmax and instead employs a new exponential measure called Dinit (see equation (5)) that is based on average queue length (aql). When aql is between the minimum and the maximum thresholds, the proposed congestion control method drops arriving packets exponentially. (ii) Enhanced performance measures of mql and D when compared to RED and one of the RED-based AQM methods such as NLRED especially in cases where the value of packet arrival probability is very high.
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