GA-WEO: A Hybrid Meta-Heuristic Algorithm for Heterogeneous Task Scheduling on Cloud Environment

Abstract The growth of cloud computing is astounding in the business and research domains in the last decade. The popularity of cloud could be attributed to the virtualization technology. To achieve balance and due to resource scarcity during peak load period, a few of those tasks are migrated to other datacenters. The potential of cloud computing can be harvested to its fullest by employing efficient task scheduling techniques. In cloud, focus is on makespan and maximum system utilization phenomenon owing to which tasks may be scheduled on different virtual machines. Meta-heuristic scheduling algorithms could be employed to provide solution to such issues since task scheduling is a NP-hard problem. In this proposed research work, a integration of Genetic Water Evaporation Optimization Algorithm (GWEO) had been presented for efficiently scheduling of tasks on a heterogeneous cloud environment. The proposed research work objective is to minimize the time of task execution, cost and energy consumed by the resources during task execution. The proposed hybrid GWEO algorithm is hybridization of Genetic Algorithm (GA) and Water Evaporation Optimization Algorithm (WEO). Different datasets had been employed for evaluating the proposed GWEO algorithm. The proposed hybrid technique performance was evolved for the metrics cost, energy and execution time using CloudSim toolkit. The experimental results obtained through simulation had been compared with other algorithms, which showed that the proposed GWEO technique achieve a significant improvement in the QoS metrics (cost, energy consumption and execution time).