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PSO-DS: a scheduling engine for scientific workflow managers

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Abstract

Cloud computing, an important source of computing power for the scientific community, requires enhanced tools for an efficient use of resources. Current solutions for workflows execution lack frameworks to deeply analyze applications and consider realistic execution times as well as computation costs. In this study, we propose cloud user–provider affiliation (CUPA) to guide workflow’s owners in identifying the required tools to have his/her application running. Additionally, we develop PSO-DS, a specialized scheduling algorithm based on particle swarm optimization. CUPA encompasses the interaction of cloud resources, workflow manager system and scheduling algorithm. Its featured scheduler PSO-DS is capable of converging strategic tasks distribution among resources to efficiently optimize makespan and monetary cost. We compared PSO-DS performance against four well-known scientific workflow schedulers. In a test bed based on VMware vSphere, schedulers mapped five up-to-date benchmarks representing different scientific areas. PSO-DS proved its efficiency by reducing makespan and monetary cost of tested workflows by 75 and 78%, respectively, when compared with other algorithms. CUPA, with the featured PSO-DS, opens the path to develop a full system in which scientific cloud users can run their computationally expensive experiments.

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Acknowledgements

The authors would like to thank the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Consejo Nacional de Ciencia Tecnología (Conacyt) for supporting this work.

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Authors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, Australia

    Israel Casas & Albert Y. Zomaya

  2. Department of Mathematics and Computer Science, Karlstad University, Karlstad, Sweden

    Javid Taheri

  3. Newcastle University, Newcastle upon Tyne, UK

    Rajiv Ranjan

  4. Data61, CSIRO, Canberra, Australia

    Israel Casas & Rajiv Ranjan

Authors
  1. Israel Casas
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  2. Javid Taheri
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  3. Rajiv Ranjan
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  4. Albert Y. Zomaya
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Correspondence to Israel Casas.

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Casas, I., Taheri, J., Ranjan, R. et al. PSO-DS: a scheduling engine for scientific workflow managers. J Supercomput 73, 3924–3947 (2017). https://doi.org/10.1007/s11227-017-1992-z

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  • DOI: https://doi.org/10.1007/s11227-017-1992-z

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