Multi-Objective Scheduling for Scientific Workflows on Cloud with Peer-To-Peer Clustering
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Publication Details
Author list: Wangsom P., Lavangnananda K., Bouvry P.
Publisher: Hindawi
Publication year: 2019
Start page: 175
End page: 180
Number of pages: 6
ISBN: 9781538675120
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
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Abstract
Running scientific workflows, often involves large-scale data-intensive applications, on cloud. The reduction of data movement is necessary because of the major impact to network utilization and energy consumption in network equipment in cloud data center. In order to reduce the Data Movement, this paper proposes Peer-To-peer clustering technique which can be applied to Directed Acyclic Graph (DAG) workflows. This research also includes Cost and Makespan which are probably the most common objectives for workflow scheduling. Due to dealing with the multi-objective optimization problem, Nondominated Sorting Genetic Algorithm-III (NSGA-III) is selected for finding solutions. Three well-known scientific workflows including Epigenomics, LIGO, and Montage are chosen as testbeds. Pareto front is adopted as the mean for visualization of the results while Hypervolume is selected as the performance metric. DAGs with Peer-To-peer clustering is evaluated by comparing its performance against the original DAG versions. The comparison reveals that DAGs with Peer-To-peer clustering enable NSGA-III to generate solutions having better Hypervolume and Pareto front in all three scientific workflows. ฉ 2019 IEEE.
Keywords
Cloud, Scientific Workflow
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