An investigation on task scheduling based on nature inspired Algorithms in cloud computing

Abstract

Cloud computing is a flexible commercial platform where a number of applications are hosted and the users are allowed to access them from different parts of the world. Every second, it handles a number of requests from the users and provides appropriate services. In recent years, nature inspired algorithms are proposed by observing the activities of the insects, animals and birds like food gathering habits, reproduction habits, relocation process and so on. Such nature inspired algorithms are applied to different optimization problems in the fields of engineering, computer science, science and so on and it is proven that they provide optimal solutions for these problems. In this research, computation-intensive tasks are considered as tasks. A Virtual Machine (VM) is a computing element, and most of the tasks would require more time for execution in it. So, the task scheduler in the cloud needs to identify an appropriate VM for the task. For each task, the completion time is calculated, while for each VM, the completion time of the last task is noted. At the end of the scheduling process, the completion time of the last task is considered as the makespan of the schedule. The current research involves the investigation of nature inspired algorithms for task scheduling in cloud computing with the objective of finding the optimal schedule with minimum makespan. Ant Colony Optimization (ACO) algorithm is based on finding the shortest route from the food source to its nest. It is applied in task scheduling in the present study to find a VM for each unscheduled task. At the end of the ACO process, the newline