Fuzzy based Particle Swarm Optimization Scheduling in Cloud Computing

Cloud computing technology is advancing at a rapid pace. These days, the majority of industries use this technology to improve service quality, one of the biggest issues. In order to execute, the system must be able to operate efficiently with little downtime and without lowering customer expectations. Study over here elaborates the use of Particle Swarm Optimization (PSO) algorithm with Fuzzy Logic (FL) to replace the conventional Genetic Algorithm (GA) for task scheduling in cloud computing environment. The work collects a bunch of scheduled tasks and evaluating each task quality based on user assumptions. This work ilterates the scheduling order genetic operations for generating optimal task schedule. When every fitness function parameters are same, it takes a long time for general GA to determine the correct scheduling order. Because it considers all possible aspects of the issue, it is a built-in technique for solving problems. When used in combination with FL and PSO. It solves problems from existing database memory in the same way that the human brain does. The present scheme gives a comparison of GA, GA with FL, and GA with FL, PSO. In comparison to GA and GAFL(Genetic Algorithm with Fuzzy Logic), the suggested system demonstrated better task progress using FL and PSO.