This paper proposes a Random early detection algorithm based on fuzzy logic Principles. The main target of using the fuzzy logic is to reduce the number of lost packets which are sent by a sender using RED algorithm in queue-buffer router of the network topology. The function of fuzzy logic is to dynamically tune the maximum drop probability (maxp) parameter of the RED algorithm. To realize this target, a twoinput-single-output fuzzy logic is implemented. The inputs of the fuzzy logic are average queue size, the difference in average queue size. To estimate the performance of the FLRED: simple network topology with FTP is suggested. In this research, the opnet modeler 14.5 has been used. The simulation results show that the FLRED algorithm is better than traditional RED algorithm as far as the number of lost packets is concerned.

/pdf/applying-fuzzy-logic-principles-to-improve-the-performance-1vooad922f.pdf

Applying Fuzzy Logic Principles to Improve the Performance of the Random Early Detection Algorithm

Simulation is a widespread procedure for testing and understanding real systems. Internet has a great impact in our society. Everyone wants fast and reliable links. But there are situations when congestion happens because the network delay and the number of users fluctuate from one moment to the next. There are different approaches for dealing with congestion: classics (such as drop tail or RED) or process control techniques (PID, fuzzy or predictive control among others). Future engineers need to learn about this topic. It seems logical to simulate this problem, but, should all the different techniques be implemented? Or should the students learn how to program some mechanisms? The answer is neither unique nor easy. This paper presents a combined approach of ready and self-programmed methods combined with tutorials using OPNET.

Understanding congestion control algorithms in tcp using opnet

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Understanding congestion control algorithms in tcp using opnet