A Dynamic Resource Allocation Method for Parallel DataProcessing in Cloud Computing

Abstract

Problem statement: One of the Cloud Services, Infrastructure as a Service (IaaS) provides a Compute resourses for demand in various applications like Parallel Data processing. The computer resources offered in the cloud are extremely dynamic and probably heterogeneous. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of processing a job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. However, the current algorithms does not consider the resource overload or underutilization during the job execution. In this study, we have focussed on increasing the efficacy of the scheduling algorithm for the real time Cloud Computing services. Approach: Our Algorithm utilizes the Turnaround time Utility effieciently by differentiating it into a gain function and a loss function for a single task. The algorithm also assigns high priority for task of early completion and less priority for abortions/deadlines issues of real time tasks. Results: The algorithm has been implemented on both preemptive and Non-premptive methods. The experimental results shows that it outperfoms the existing utility based scheduling algorithms and also compare its performance with both preemptive and Non-preemptive scheduling methods. Conculsion: Hence, a novel Turnaround time utility scheduling approach which focuses on both high priority and the low priority tasks that arrives for scheduling is proposed.