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A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources

A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources

Yin Li, Yuyin Ma, Ziyang Zeng
Copyright: © 2020 |Volume: 17 |Issue: 3 |Pages: 13
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781799804918|DOI: 10.4018/IJWSR.2020070104
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MLA

Li, Yin, et al. "A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources." IJWSR vol.17, no.3 2020: pp.56-68. http://doi.org/10.4018/IJWSR.2020070104

APA

Li, Y., Ma, Y., & Zeng, Z. (2020). A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources. International Journal of Web Services Research (IJWSR), 17(3), 56-68. http://doi.org/10.4018/IJWSR.2020070104

Chicago

Li, Yin, Yuyin Ma, and Ziyang Zeng. "A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources," International Journal of Web Services Research (IJWSR) 17, no.3: 56-68. http://doi.org/10.4018/IJWSR.2020070104

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Abstract

Edge computing is pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. A major technological challenge for workflow scheduling in the edge computing environment is cost reduction with service-level-agreement (SLA) constraints in terms of performance and quality-of-service requirements because real-world workflow applications are constantly subject to negative impacts (e.g., network congestions, unexpected long message delays, shrinking coverage, range of edge servers due to battery depletion. To address the above concern, we propose a novel approach to location-aware and proximity-constrained multi-workflow scheduling with edge computing resources). The proposed approach is capable of minimizing monetary costs with user-required workflow completion deadlines. It employs an evolutionary algorithm (i.e., the discrete firefly algorithm) for the generation of near-optimal scheduling decisions. For the validation purpose, the authors show that our proposed approach outperforms traditional peers in terms multiple metrics based on a real-world dataset of edge resource locations and multiple well-known scientific workflow templates.

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