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2024 | OriginalPaper | Chapter

Fine Time Granularity Allocation Optimization of Multiple Networks Industrial Chains in Task Processing Systems

Authors : Pan Li, Kai Di, Xinlei Bai, Yuanshuang Jiang, Fulin Chen

Published in: Parallel and Distributed Computing, Applications and Technologies

Publisher: Springer Nature Singapore

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Abstract

As the industrial division of labor becomes increasingly specialized, various collaborative relationships between industrial chains develop, forming complex multi-networks. In the task processing system of multiple network industrial chains, there are dynamic online tasks. The arrival and deadline of these tasks cannot be accurately predicted. Therefore, it is necessary to divide the scheduling into finer time granularity to improve the response speed, efficiency, and timeliness, while ensuring the task completion rate and minimizing the task cost. In this paper, we study the characteristics of online tasks in multiple networks industrial chains and design a corresponding online scheduling framework. We analyze the arrival of online tasks in real-world scenarios and propose a passive scheduling algorithm based on the characteristics of different scenarios. The algorithm is tested on several sets of simulated data. Compared with previous heuristic algorithms, our algorithm can achieve better results in terms of task completion time, energy cost, and task completion rate in scenarios with fine time granularity.

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Metadata
Title
Fine Time Granularity Allocation Optimization of Multiple Networks Industrial Chains in Task Processing Systems
Authors
Pan Li
Kai Di
Xinlei Bai
Yuanshuang Jiang
Fulin Chen
Copyright Year
2024
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-8211-0_16

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