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PSO-DS: a scheduling engine for scientific workflow managers

Lookup NU author(s): Professor Raj Ranjan

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Abstract

© 2017 Springer Science+Business Media New York 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.

Publication metadata

Author(s): Casas I, Taheri J, Ranjan R, Zomaya AY

Publication type: Article

Publication status: Published

Journal: Journal of Supercomputing

Year: 2017

Volume: 73

Issue: 9

Pages: 3924-3947

Print publication date: 01/09/2017

Online publication date: 03/03/2017

Acceptance date: 02/04/2016

ISSN (print): 0920-8542

ISSN (electronic): 1573-0484

Publisher: Springer New York LLC

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

DOI: 10.1007/s11227-017-1992-z

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