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Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach

Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach

Lei Wu, Yuandou Wang
Copyright: © 2018 |Volume: 15 |Issue: 4 |Pages: 15
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522542476|DOI: 10.4018/IJWSR.2018100105
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MLA

Wu, Lei, and Yuandou Wang. "Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach." IJWSR vol.15, no.4 2018: pp.82-96. http://doi.org/10.4018/IJWSR.2018100105

APA

Wu, L. & Wang, Y. (2018). Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach. International Journal of Web Services Research (IJWSR), 15(4), 82-96. http://doi.org/10.4018/IJWSR.2018100105

Chicago

Wu, Lei, and Yuandou Wang. "Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach," International Journal of Web Services Research (IJWSR) 15, no.4: 82-96. http://doi.org/10.4018/IJWSR.2018100105

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Abstract

Cloud computing, with dependable, consistent, pervasive, and inexpensive access to geographically distributed computational capabilities, is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. Scheduling multiple workflows over cloud infrastructures and resources is well recognized to be NP-hard and thus critical to meeting various types of Quality-of-Service (QoS) requirements. In this work, the authors consider a multi-objective scientific workflow scheduling framework based on the dynamic game-theoretic model. It aims at reducing make-spans, cloud cost, while maximizing system fairness in terms of workload distribution among heterogeneous cloud virtual machines (VMs). The authors consider randomly-generated scientific workflow templates as test cases and carry out extensive real-world tests based on third-party commercial clouds. Experimental results show that their proposed framework outperforms traditional ones by achieving lower make-spans, lower cost, and better system fairness.

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