Energy Efficient Task Scheduling Algorithms for Cloud Computing Data

Abstract

Cloud computing refers to Internet based development and utilization of computer technology, and hence, cloud computing can be described as a model of Internet-based computing. Scheduling is a critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud computing system. So scheduling is the major issue in establishing Cloud computing systems. The main goal of scheduling is to maximize the resource utilization and minimize processing time of the tasks. In this thesis, an efficient task-grouping based approach has been proposed for task scheduling in computational cloud. Proposed work is grouping the tasks before resource allocation according to resource capacity to reduce the communication overhead. Cloud Resources are heterogeneous in nature, owned and managed by different organizations with different allocation policies. In our scheduling algorithm tasks are scheduled based on resources computational and communication capabilities. Here tasks are grouped together based on the chosen resources characteristics, to maximize resource utilization and minimize processing time and cost. Task scheduling is a decision process by which tasks are assigned to available resources to optimize various performance metrics. Hence in this thesis, we have specifically focused on improving computational cloud performance in terms of total processing time and total processing cost and reduce communication overhead. A simulation of proposed approach using CloudSim toolkit is conducted. Experimental results show proposed algorithm performs efficiently in computational cloud environment.