MULTI-OBJECTIVE TASK SCHEDULING USING K-MEANS ALGORITHM IN CLOUD COMPUTING

Abstract

The growth of information technology has led to the increasing need of computing and storage. Cloud services are the technologies with high demand, consisting of a set of virtualized resources that serve the users as per demand and on a pay-per-use basis via the internet. So, it must have the ability to meet all the users’ requests with high performance and efficiency. Task scheduling is a very important aspect to improve the overall performance of the cloud computing. There are various types of scheduling algorithms for resource utilization. The poor task scheduling resources are not utilized efficiently. In order to address these weaknesses, this work proposes a novel approach to map groups of tasks to customized virtual machine types. Mapping of the tasks is based on task usage patterns like length, file size and bandwidth. The proposed system uses K-means clustering algorithm to generate the task clusters .The tasks are grouped base on their “Tasks Length and Deadline”. After clustering, the individual task in each cluster is scheduled to appropriate VMs. The proposed system performs multi-objective task scheduling with an aim in minimizing the makespan and execution time. The experimental results show that the proposed method produces better results in terms of execution time, makespan than the traditional algorithm (FCFS). In this system, CloudSim simulator version 3.0.3, one of the open source frameworks is used .It is written in Java programming language.