This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy traffic load condition respectively. Results from ns-3 network simulator using different experimental scenarios clearly reveals that the proposed RED-QL algorithm yields a substantial reduction in delay performance and indeed a reduced average queue size than other three representative AQM algorithms. RED-QL is robust, easy to implement and deploy in routers (both in software and hardware) as no more than the packet drop probability profile of the classic RED’s algorithm implementation needs modest alteration.

<span>Network congestion is still a problem on the internet. The random early detection (RED) algorithm being the most notable and widely implemented congestion algorithm in routers faces the problems of queue instability and large delay arising from the presence of an ineffectual singular linear packet dropping function. This research article presents a refinement to RED, named quadratic exponential random early detection (QERED) algorithm, which exploits the advantages of two drop functions, namely quadratic and exponential in order to enhance the performance of RED algorithm. ns-3 simulation studies using various traffic load conditions to assess and benchmark the effectiveness of QERED with two improved variants of RED affirmed that QERED offers a better performance in terms of average queue size and delay metrics at various network scenarios. Fortunately, to replace/upgrade the implementation for RED algorithm with QERED’s in routers will require minimal effort due to the fact that nothing more besides the packet dropping probability profile got to be adjusted.</span>

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Random Early Detection (RED), an active queue management (AQM) scheme has been known to address the problem of network congestion in Internet routers. However, RED suffers from large delay which can be partly traced to the single linear packet drop function it deploys. In this paper, we present a new RED-based AQM scheme called Curvilinear Random Early Detection (CLRED) scheme which modified the single linear dropping function of RED with a two-segment (that is, a quadratic and a linear) dropping functions to address the reported drawback. Simulation carried out in network simulator 3 (NS-3) confirms that the proposed CLRED scheme achieved a significant reduction in the average queue size when compared with the classical RED scheme. Therefore, the existing RED implementations can be upgraded/replaced with the proposed CLRED scheme so as to help RED overcome its large delay drawback.

The random early detection (RED) algorithm was developed in 1993. Nearly three decades later, several improved variants have been proposed by scientists. The use of a (pure) linear function for computing packet drop probability has turned out to be a disadvantage, leading to the problem of large delays. Such a problem may be addressed by using linear and non-linear (i.e. as exponential) packet drop probability functions. This paper proposes a revised RED active queue management algorithm named RED-linear exponential (RED-LE). This variant involves an interplay of linear and exponential drop functions, in order to improve the performance of the original RED algorithm. More importantly, at low and moderate network traffic loads, the RED-LE algorithm employs the linear drop action. However, for high traffic loads, RED-LE employs the exponential function for computing the packet drop probability rate. Experimental results have shown that RED-LE effectively controls congestion and offers an improved network performance under different traffic loads.

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In the internet, router plays a strategic role in the transmission of data packets. Active queue management (AQM) aimed at managing congestion by keeping a reduced average buffer occupancy and hence a minimal delay. The novel random early detection (RED) algorithm suffers from large average buffer occupancy and delay shortcomings. This problem is due in part to the existence of a distinctive linear packet drop function it deploys. In this paper, we present a new version of RED, called improved RED (IMRED). An important strategy of IM-RED is to deploy two dropping functions: i) nonlinear (i.e. quadratic) to deal with both light-and moderatenetwork traffic load conditions, and ii) linear to deal with heavy traffic load condition. Simulation experiments conducted using open-source ns-3 software to evaluate and compare the functionality of the proposed IM-RED with other two previous AQM algorithms confirmed that IM-RED reduces the average buffer occupancy and obtained an improved delay performance especially at heavy network traffic load scenario. Very fortunately, since RED algorithm is known to appear as a built-in model in ns-3 and even Linux kernel, its implementation can therefore be leveraged to obtain IMRED while only adjusting the packet dropping probability profile and holding on to its other attributes.