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9 February 2024 Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow
Peng Fu, Zhihao Wang, Meng Guo
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Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 1307314 (2024) https://doi.org/10.1117/12.3026325
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China
Abstract
Supercomputing Internet is based on supercomputers and high-speed Internet, achieving the interconnection of data and computing resources between supercomputing centers in various regions, solving the problem of uneven distribution of computing resources, and providing diversified computing services. Workflow tasks consist of a large number of related tasks, and task scheduling of workflows is crucial in the supercomputing Internet. Good algorithms help provide users with higher quality services. Traditional heuristic methods usually focus on shortening the makespan and tend to prioritize the use of resources with stronger computing power. However, this method is prone to falling into local optima and increases cost overhead. In this article, we model the makespan of workflows and the cost of supercomputing resources as multi-objective optimization problems and propose a multi-objective ant colony algorithm (MOACO). The experimental results show that the algorithm effectively reduces makespan and cost, and fully utilizes available resources.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Peng Fu, Zhihao Wang, and Meng Guo "Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 1307314 (9 February 2024); https://doi.org/10.1117/12.3026325
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KEYWORDS
Internet
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Distributed computing
Process modeling
Matrices
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