计算机与现代化 ›› 2023, Vol. 0 ›› Issue (11): 13-21.doi: 10.3969/j.issn.1006-2475.2023.11.003

• 算法设计与分析 • 上一篇    下一篇

基于设备协同的大规模卸载:融合分治和贪心的双层优化算法

  

  1. (中国石油大学(华东)计算机科学与技术学院,山东 青岛 266580)
  • 出版日期:2023-11-29 发布日期:2023-11-29
  • 作者简介:闫阳(1979—),女,辽宁铁岭人,实验师,硕士,研究方向:移动边缘计算,E-mail: yanyang@upc.edu.cn; 詹子俊(1999—),男,江西乐平人,硕士研究生,研究方向:移动边缘计算,计算卸载和区块链。

Collaborative Device-based Large-scale Offloading: A Bi-level Optimization Algorithm Fusing Divide-and-conquer and Greedy

  1. (College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China)
  • Online:2023-11-29 Published:2023-11-29

摘要: 摘要:随着通信技术的飞速发展,移动设备的数量不断激增,而这也将导致大规模卸载场景频频发生。但是如何在多项式时间内解决大规模卸载问题仍然是个挑战。本文基于协作计算网络架构提出一个融合分而治之和贪心的双层优化算法,称为DCGreedy。该算法可在多项式时间内高效求解所有任务的卸载策略和资源分配方案。在满足所有约束的同时可以有效降低系统的总能耗。在至少400个移动设备的模拟场景下根据任务满足截止日期的总数、系统总能耗和算法运行时间来评估DCGreedy的性能。将DCGreedy与其他4种基准算法进行大量的实验对比,并发现在不同规模的卸载场景中DCGreedy的平均总能耗比排名第二的算法高出2.11%,而算法运行时间却仅为0.0049%, 充分证实了DCGreedy在优化系统能耗的同时有效地减少了算法的运行时间。

关键词: 关键词:大规模卸载, 分而治之, 贪心, 移动边缘计算(MEC)

Abstract: Abstract: With the rapid development of communication technology, the number of mobile devices is constantly increasing, which will also lead to frequent large-scale offloading scenarios. However, solving large-scale offloading problems in polynomial time remains a challenge. In this paper, we propose a bi-level optimization algorithm based on the cooperative computing network architecture, called DCGreedy, which fuses divide-and-conquer and greedy. This algorithm can efficiently solve the offloading strategy and resource allocation scheme of all tasks in polynomial time. It can effectively reduce the total energy consumption of the system while meeting all constraints. We evaluate the performance of DCGreedy based on the total number of tasks meeting deadlines, total system energy consumption, and algorithm runtime in a simulation scenario of at least 400 mobile devices. We conducted extensive experimental comparisons between DCGreedy and four other benchmark algorithms and found that in different scale offloading scenarios, the average total energy consumption of DCGreedy was 2.11% higher than the second ranked algorithm, while the algorithm’s running time was only 0.0049%. This fully confirms that DCGreedy effectively reduces the algorithm’s running time while optimizing system energy consumption.

Key words: Key words: large-scale offloading, divide-and-conquer, greedy, mobile edge computing

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