Abstract
The popularity of network applications has increased the number of packets travelling within the routers in networks. The movement expends most resources in such networks and consequently leads to congestion, which worsens the performance measures of networks, such as delay, packet loss and bandwidth. This study proposes a new method called Fuzzy Logic Approach for Congestion Control (FLACC), which uses fuzzy logic to decrease delay and packet loss. This method also improves network performance. In addition, FLACC employs average queue length (aql) and packet loss (PL) as input linguistic variables to control the congestion at early stages. In this study, the proposed and compared methods were simulated and evaluated. Results reveal that fuzzy logic Gentle Random Early Detection (FLGRED) showed better performance results than Gentle Random Early Detection (GRED) and GRED Fuzzy Logic in delay and packet loss and when the router buffer was in heavy congestion.
Highlights
Computer networks are utilised in organizations, homes and offices
FLACC is an extension of the Gentle Random Early Detection (GRED) Active Queue Management Method (AQM) method
As parameterisation is a limitation of current AQM methods, FLACC aims to detect and avoid such a problem. aql, packet loss (PL), and fuzzy inference process (FIP) are employed as indicators to discover and prevent congestion at early stages or before the router buffer becomes full
Summary
Computer networks are utilised in organizations, homes and offices. This situation motivates the movement of huge data within computer networks around the world [1,2,3]. The main goal of the AQM methods discover and manage the congestion at early before the router buffer reaches to the limit size. The current AQM methods cannot predict the congestion at early stage in effective manner, as a result decrease performance of the network [21, 22]. Current paper a new method proposed, to discover the congestion earlier to detect the problems that appear in trail-drop method and enhance the network performance. The inability of current AQM methods to predict congestion at early stages effectively decreases network performance [21, 22]. This study proposes a new method to discover the congestion early, detect the problems that appear in the trail-drop method and enhance network performance. The proposed method uses fuzzy logic in utilising the router buffer by using (aql) and (PL)
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