A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Abstract

This paper presents a new hybrid approach, called ACOSA, for cloudlets scheduling to enhance the scheduler behavior in Cloud computing (CC) environment and to overcome the results oscillation problem of the existing meta-heuristic scheduling algorithms. The proposed approach combines both the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithm to improve both quality of solutions and time complexity of the scheduling algorithm. The proposed approach is evaluated by using the well-known CloudSim, and the results are compared with the ant colony and simulated annealing separately in terms of schedule length, load balancing, and time complexity. It decreases the schedule length by 29.75% with SA and 12.25% with ACO. The ACOSA provides higher load balancing degree. It improves the balancing degree ratio by 36.36% than SA and 12.13% than ACO algorithms.