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卸载决策是数据处理的重要一环，然而大多数研究在确定卸载决策时忽略了边缘服务器之间协作处理时产生的时延与能耗，因此提出一种最小化任务处理成本算法，通过最小化任务迁移时产生的时延与能耗，并确定感知数据在云端与边缘端的最优卸载比例达到优化成本的目标。首先，将优化目标转化为一个产销平衡的运输问题并利用Vogel法求解任务在边缘端处理的最小时延与能耗。其次，结合任务在云端与边缘端处理所产生的时延与能耗，求解最优的卸载比例使得任务处理的成本达到最小。最后，Matlab仿真实验结果表明，所提算法与其他算法相比减少了18%～25%的任务处理成本。
Abstract：Offload decision-making was an important part of data processing. However, most studies ignored the delay and energy consumption generated by collaboration between edge servers when determining offload decisions. An algorithm to minimize task processing costs was proposed, to achieve the goals of optimizing costs by minimizing the delay and energy consumption generated during task migration and determining the optimal offload ratio of sensed data between the cloud and the edge. Firstly, the optimization objective was transformed into a transportation problem with balanced production and sales, and the Vogel algorithm was used to solve the minimum delay and energy consumption of task processing at the edge. Secondly, combined with the delay and energy consumption of task processing in the cloud and the edge, the optimal offloading ratio was solved to minimize the costs of task processing. Finally, Matlab simulation experiments showed that the proposed algorithm reduces the task processing cost by about 18% to 25% compared with other algorithms.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023262
中图分类号:TN929.5
引用信息:
[1]武小丰,袁培燕.一种资源受限的时延与能耗优化卸载策略[J].郑州大学学报(理学版),2025,57(05):62-68.DOI:10.13705/j.issn.1671-6841.2023262.
基金信息:
国家自然科学基金项目(62072159,U1804164)
2023-11-17
2023
2025-08-24
2025
3
2024-05-21
2024-05-21
2024-05-21