A DYNAMIC ALGORITHM FOR TASK SCHEDULING IN CLOUD ENVIRONMENT

Abstract

Cloud computing has emerged as a popular computing model to support on demand services. It is a style of computing where massively scalable resources are delivered as a service to external customers using Internet technologies. The aim is to make effective utilization of distributed resources and put them together in order to achieve _ higher throughput. Scheduling in cloud is the process of selecting resources where the tasks submitted by user are executed. In other words, scheduling is responsible for selection of best suitable resources for task execution, by taking some static and dynamic parameters and restrictions of tasks' into consideration. This resource allocation policy can be random, sequential, based on processing power of resources or may be based upon any other criteria. The users' perspective of efficient scheduling may be based on parameters like task completion time or task execution cost etc. Service providers like to ensure that resources are utilized efficiently and to their best capacity so that resource potential is not left unused. In this dissertation entitled "A Dynamic Algorithm for Task Scheduling in Cloud Environment", a scheduling algorithm is proposed which addresses the major challenges of task scheduling in cloud. It overcomes the problems of high task execution cost, improper resource utilization and improves the task completion time. The incoming tasks are grouped on the basis of task requirement like minimum execution time or minimum cost and prioritized. Resource selection is done on the basis of task constraints using a greedy approach. By meeting the task requirements, the proposed scheduling scheme results in providing better outcome to users. In this dissertation, the proposed algorithm also obtains improved resource utilization. The proposed model is implemented and tested on CloudSim (2.1.1) simulation toolkit. We analyze the performance of proposed algorithm. The analysis results are also compared with existing scheduling approach (First Come First Serve task selection to Sequential resource mapping) used by the simulator. The results validate the correctness of the framework.