Hybrid Meta-heuristics based scheduling technique for Cloud Computing Environment
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Abstract
Cloud computing provides an environment where the distinct resources are delivered as a service to the customers/tenants over the internet. Herein, the core idea is to map the tasks to appropriate resources in order to optimize one or more objectives. As the resource allocation is categorized to be NP-Hard problem, there are no such algorithms that may find the optimal solution within genuine polynomial time. Hence, it is preferable to utilize meta-heuristic algorithms to find sub-optimal solutions in short duration of time. This paper has designed a hybrid technique for parallel scheduling in a cloud computing environment. The proposed technique has utilized mutation and crossover operators to improve the hybridization of Simulated Annealing (SA) with Particle Swarm Optimization (PSO). Thus, proposed technique can efficiently reduce the schedule length and flow time. Experimental results indicate that the proposed algorithm is more efficient than existing techniques.
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