A Particle Swarm and Ant Colony Optimization based Load Balancing and Virtual Machine Scheduling Algorithm for Cloud Computing Environment

Abstract

In cloud computing balancing the load of the Virtual Machine (VM) is very much essential. Load balancing efficient utilization of resource and fairly balance the resource usage. In the real time scenario the request for the Virtual Machine (VM)s and tasks submission could be dynamic, whereas system creates the Virtual Machine (VM) according to the customer demand and map it to suitable Physical Machine (PM). These Virtual Machine (VM) could be created without knowing the detailed information about the task.Hence, the scheduling of these taskscould not be optimised using traditional task scheduling algorithms. In this paper a hybrid meta heuristic approach for scheduling these tasks is proposed. Two different optimization techniques for Virtual Machine (VM) scheduling has been used in this paper. We combine Particle Swarm optimization and Ant Colony Optimization approaches called (PSACO). The PSACO uses the historical information regarding the Virtual Machine (VM)s and task submission to predict The workload of new task submission and resource request in dynamic environment without extra information. The proposed approach also rejects the computing request which does not satisfied the current resource constraints. It reduces the computation time for scheduling. the experiment results shows that the proposed metaheuristic algorithm balance the load with the dynamic environment and outperformed the existing algorithms.