Cloud computing gives a large quantity of processing possibilities and heterogeneous resources, meeting the prerequisites of numerous applications at diverse levels. Therefore, resource allocation is vital in cloud computing. Resource allocation is a technique that resources such as CPU, RAM, and disk in cloud data centers are divided among cloud users. The resource utilization, cloud service provider profit, and user satisfaction are the common objectives of the proposed algorithms. In this study, the algorithm is based on fuzzy logic and tries to achieve better results than other resource allocation algorithms by using meta-heuristic methods. After designing the preliminary fuzzy inference system (FIS), the use of meta-heuristic algorithms is ended up tuning the FIS parameters. By adjusting the parameters, membership functions are improved and finally a trained FIS is delivered. PSO has been used to train the fuzzy system. The fuzzy resource allocation algorithm responded 95% of the requests and achieved 61.61% of the resource efficiency, while the fuzzy-PSO algorithm answered 96% of the requests and improved the resource utilization by 0.5%. The results have shown that the application of PSO improves fuzzy resource allocation efficiency. More requests are answered and it increases resource utilization.
 

Keywords: Cloud Computing, Resource Allocation, Fuzzy Logic, Particle Swarm Optimization (PSO), Resource Utilization.

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