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PSO-DS: a scheduling engine for scientific workflow managers
University of Sydney, Australia; CSIRO, Data61, Canberra, ACT, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
Newcastle University, England ; CSIRO, Data61, Canberra, ACT, Australia.
University of Sydney, Australia.ORCID iD: 0000-0002-3090-1059
2017 (English)In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, Vol. 73, no 9, p. 3924-3947Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 73, no 9, p. 3924-3947
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-65834DOI: 10.1007/s11227-017-1992-zISI: 000407864100010OAI: oai:DiVA.org:kau-65834DiVA, id: diva2:1177507
Available from: 2018-01-25 Created: 2018-01-25 Last updated: 2018-07-04Bibliographically approved

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Taheri, Javid

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