Improved Round Robin Algorithm Based on Fuzzy Logic and Genetic Algorithm for Server Load Balancing

The problem of efficient load distribution in modern computer networks and cloud computing systems is becoming increasingly urgent. The traditional Round Robin algorithm distributes requests equally among servers, but does not take into account differences in their power. This leads to increased system delays and underutilization of resources. In this paper, the Round Robin algorithm is integrated with a fuzzy logic control system and optimized using a genetic algorithm. In the proposed model, the appropriate priority level for each server is determined based on parameters such as server load level, computing power, and response speed. A set of fuzzy rules is automatically generated using a genetic algorithm, and various shapes of membership functions (triangular, trapezoidal, Gaussian) are evaluated in terms of efficiency. The software is implemented in a user-friendly graphical interface based on PyQt5, and the flow of requests is simulated in real time. The experimental results show that the improved algorithm increases the speed of processing requests compared to the classic Round Robin method, balances the system load, and improves quality of service indicators.

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