Improved Ant Colony Optimization based Task Scheduling and Load Balancing Algorithm for Cloud Computing Infrastructure
Abstract
Cloud Computing consists of virtual machines which are interconnected and provides distributed and parallel computing capabilities. Nowadays due to the advantages of cloud computing infrastructure, demands are also increasing and the advancement is also taking place to fulfil the demands and requirements. Task scheduling is an elementary issue in the cloud computing environment. Many researchers have attempted to solve this issue in order to this, many meta-heuristics approaches for task scheduling have been proposed. It is known that in the cloud, task scheduling is the NP-hard Optimization problem. Many environmental factors and types of tasks may also affect the performance of the task scheduler. In this paper, a task scheduling algorithm has been proposed which is based on the meta-heuristic optimisation approach called Ant Colony Optimization (ACO). The proposed approach helps the cloud infrastructure to balance the load of the visual machine and physical machine. It balances the load of the cloud infrastructure with improving the makespan of the submitted tasks. The proposed algorithm is implemented using the Cloudsim Simulator. Further, the implementation results of the proposed algorithm are compared with the related work to find the performance of the proposed algorithm.