文章基本信息

标题：Resource Allocation Method using Scheduling methods for Parallel Data Processing in
本地全文：下载
作者：Sowmya Koneru ; V N Rajesh Uddandi ; Satheesh Kavuri 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：4
页码：4625-4628
出版社：TechScience Publications
摘要：Infrastructure as a Service (IaaS) clouds have emerged as a promising new platform for massively parallel data processing. By eliminating the need for large upfront capital expenses, operators of IaaS clouds offer their customers the unprecedented possibility to acquire access to a highly scalable pool of computing resources on a short-term basis and enable them to execute data analysis applications at a scale which has been traditionally reserved to large Internet companies and research facilities. However, despite the growing popularity of these kinds of distributed applications, the current parallel data processing frameworks, which support the creation and execution of large-scale data analysis jobs, still stem from the era of dedicated, static compute clusters and have disregarded the particular characteristics of IaaS platforms so far. 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 paper, we have focused on increasing the efficacy of the scheduling algorithm for the real time Cloud Computing services. Our Algorithm utilizes the Turnaround time Utility efficiently 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 issues of real time tasks. The algorithm has been implemented on RR method. The out performs existing utility based scheduling algorithms and also compare its performance.
关键词：Cloud computing; task scheduling; resource utilization; High-;Throughput Computing.