Abstract
Task management has always been a key issue in collective computing, including task decomposition, distribution, execution and results integration, but there is little research on task decomposition. In order to improve multi-tasks execution efficiency and promote the full utilization of collective resources, a task decomposition model based on extended task-tree is proposed in this paper. Meanwhile, a series of pruning and reorganization algorithms are proposed, and the performance of the algorithms is analyzed and evaluated. Experiments verify that the proposed algorithms outperform traditional methods, and prove that the practicality and efficiency of the strategy.
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Acknowledgement
This research was supported by Defense Industrial Technology Development Program under Grant No. JCKY2016605B006, Six talent peaks project in Jiangsu Province under Grant No. XYDXXJS-031.
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Zhang, Z., Zhao, Y., Li, Y., Zhu, K., Wang, R. (2019). A Multi-task Decomposition and Reorganization Scheme for Collective Computing Using Extended Task-Tree. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_16
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DOI: https://doi.org/10.1007/978-3-030-15093-8_16
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