Hyper Heuristic MapReduce Workflow Scheduling in Cloud

Q3 Medicine
Arunkumar Panneerselvam, B. Subbaraman
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引用次数: 1

Abstract

The Advancement in the field of computing requires new technologies and algorithms for efficient processing of large scale data such as Big Data. Distributed environments such as Cloud are prominent in storing and processing Big Data. Hadoop is a framework for processing Big Data. Hadoop follows MapReduce technique to process data in parallel. Today MapReduce workflows are extensively used in large scale scientific applications which are executed in cloud. Cloud offers rented resources for scheduling MapReduce workflows. Hyper Heuristic technique can be efficiently used for efficient scheduling of MapReduce task to the cloud resources. This paper explores the basis of MapReduce workflow execution in IaaS cloud and application of Hyper Heuristic technique in resource provisioning.
云中的超启发式MapReduce工作流调度
计算领域的发展需要新的技术和算法来高效地处理大数据等大规模数据。云等分布式环境在大数据的存储和处理中占有突出地位。Hadoop是一个处理大数据的框架。Hadoop采用MapReduce技术并行处理数据。今天,MapReduce工作流被广泛应用于在云端执行的大规模科学应用程序中。云提供租用资源来调度MapReduce工作流。hyperheuristic技术可以有效地用于MapReduce任务对云资源的高效调度。探讨了MapReduce工作流在IaaS云中执行的基础,以及超启发式技术在资源配置中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Koomesh
Koomesh Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
0
审稿时长
24 weeks
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