A Quantization Model of Membership Function Sensitivity in Fuzzy Logic–Based Request Distribution

Efficient request distribution is essential for ensuring stability and performance in modern distributed and cloud based systems. Traditional load balancing methods rely on deterministic rules and are often unable to handle uncertainty and dynamic changes in server conditions. Fuzzy logic based request distribution improves adaptability by using linguistic representations of system states. However, the effectiveness of such systems depends on the sensitivity of membership functions used during fuzzification. This paper presents a quantization model for evaluating and optimizing membership function sensitivity in fuzzy logic based request distribution. The proposed approach selects suitable membership functions according to the semantic characteristics of server load states. Experimental results show improved decision accuracy, better load balance, and reduced uncertainty compared to conventional fuzzy models.

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