A Priority Based Task Scheduling in Cloud Computing Using a Hybrid MCDM Model

Task scheduling is an interesting topic in cloud computing nowadays. The mapping of the cloud resources to process the customer requests is very challenging and a well-known NP-Complete problem. In this paper, we address this problem with the consideration of the priority as one of the critical issues in the task scheduling process. The priority is computed according to the most important parameters that can meet user's requirements and improve the resource utilization. We propose a new Dynamic Priority-Queue (DPQ) approach based on a hybrid multi-criteria decision making (MCDM) namely ELECTRE III and Differential Evolution (DE). Furthermore, to schedule the tasks, we introduce a hybrid meta-heuristic algorithm based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA). The proposed DEELDPQ-SAPSO approach has been validated through the CloudSim simulator. The experimental results show that the proposed approach can achieve good performance, user priority, load balancing and improve the resource utilization.