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Data-Intensive Workflow Scheduling in Cloud on Budget and Deadline Constraints

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Collaborate Computing: Networking, Applications and Worksharing (CollaborateCom 2016)

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Abstract

With the development of Cloud Computing, large-scale applications expressed as scientific workflows are often executed in cloud. The problems of workflow scheduling are vital for achieving high efficient and meeting the needs of users in clouds. In order to obtain more cost reduction as well as maintain the quality of service by meeting the deadlines, this paper proposed a novel heuristic, PWHEFT (Path-task Weight Heterogeneous Earliest Finish Time), based on Heterogeneous Earliest Finish Time (HEFT). The criticality of tasks in a workflow and data transmission between resources are considered in PWHEFT while ignored in some other algorithms. The heuristic is evaluated using simulation with five different real world workflow applications. The simulation results show that our proposed scheduling heuristic can significantly improve planning success rate.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Zhang Xin, Changze Wu & Kaigui Wu

Authors
  1. Zhang Xin
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  2. Changze Wu
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  3. Kaigui Wu
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Corresponding author

Correspondence to Changze Wu .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Ao Zhou

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xin, Z., Wu, C., Wu, K. (2017). Data-Intensive Workflow Scheduling in Cloud on Budget and Deadline Constraints. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_24

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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