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A Survey of Scheduling and Management Techniques for Data-Intensive Application Workflows

A Survey of Scheduling and Management Techniques for Data-Intensive Application Workflows

Suraj Pandey, Rajkumar Buyya
Copyright: © 2013 |Pages: 21
ISBN13: 9781466641532|ISBN10: 1466641533|EISBN13: 9781466641549
DOI: 10.4018/978-1-4666-4153-2.ch066
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MLA

Pandey, Suraj, and Rajkumar Buyya. "A Survey of Scheduling and Management Techniques for Data-Intensive Application Workflows." Enterprise Resource Planning: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 1170-1190. https://doi.org/10.4018/978-1-4666-4153-2.ch066

APA

Pandey, S. & Buyya, R. (2013). A Survey of Scheduling and Management Techniques for Data-Intensive Application Workflows. In I. Management Association (Ed.), Enterprise Resource Planning: Concepts, Methodologies, Tools, and Applications (pp. 1170-1190). IGI Global. https://doi.org/10.4018/978-1-4666-4153-2.ch066

Chicago

Pandey, Suraj, and Rajkumar Buyya. "A Survey of Scheduling and Management Techniques for Data-Intensive Application Workflows." In Enterprise Resource Planning: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1170-1190. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-4153-2.ch066

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Abstract

This chapter presents a comprehensive survey of algorithms, techniques, and frameworks used for scheduling and management of data-intensive application workflows. Many complex scientific experiments are expressed in the form of workflows for structured, repeatable, controlled, scalable, and automated executions. This chapter focuses on the type of workflows that have tasks processing huge amount of data, usually in the range from hundreds of mega-bytes to petabytes. Scientists are already using Grid systems that schedule these workflows onto globally distributed resources for optimizing various objectives: minimize total makespan of the workflow, minimize cost and usage of network bandwidth, minimize cost of computation and storage, meet the deadline of the application, and so forth. This chapter lists and describes techniques used in each of these systems for processing huge amount of data. A survey of workflow management techniques is useful for understanding the working of the Grid systems providing insights on performance optimization of scientific applications dealing with data-intensive workloads.

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