Cloudlet Scheduling Based Load Balancing on Virtual Machines in Cloud Computing Environment

Abstract

Management of cloud computing resources is critical, especially when several cloudlets are submitted simultaneously to cloud computing. Therefore, it is very important to use high efficient cloudlet scheduling techniques to guarantee efficient utilization of computing resources. This paper presents a two-phase approach, called SAAC , for scheduling cloudlets onto Virtual Machines ( VMs ) of cloud computing environment to balance workload on the available VMs and minimize makespan (i.e., the completion time at the maximum loaded VM ). In the first phase, the SAAC approach applies the Simulated Annealing ( SA ) to find a near optimal scheduling of the cloudlets. While, in the second phase, the SAAC approach improves the cloudlets distribution by applying the Ant Colony Optimization ( ACO ) considering the solution obtained by the SA as the initial solution. The SAAC approach overcomes the computational time complexity of the ACO algorithm and low solutions quality of the SA . The proposed approach is evaluated by using the CloudSim, and the results are compared with that obtained by the most recent algorithms in terms of schedule length, load balancing, and time complexity.