Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (8): 2293-2298.DOI: 10.11772/j.issn.1001-9081.2019122200

• Advanced computing • Previous Articles     Next Articles

Computation offloading strategy based on particle swarm optimization in mobile edge computing

LUO Bin1,2, YU Bo1,2   

  1. 1. University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang Liaoning 110168, China
  • Received:2019-12-31 Revised:2020-03-29 Online:2020-08-10 Published:2020-05-14


罗斌1,2, 于波1,2   

  1. 1. 中国科学院大学, 北京 100049;
    2. 中国科学院 沈阳计算技术研究所, 沈阳 110168
  • 通讯作者: 罗斌(1995-),男,甘肃兰州人,硕士研究生,主要研究方向:计算机网络、机器学习,
  • 作者简介:于波(1980-),男,辽宁沈阳人,博士,主要研究方向:计算机网络、IP通信。

Abstract: Computation offloading is one of the means to reduce delay and save energy in Mobile Edge Computing (MEC). Through reasonable offloading decisions, industrial costs can be greatly reduced. Aiming at the problems of long delay and high energy consumption after the deployment of MEC servers in the industrial production line, a computation offloading strategy based on Particle Swarm Optimization (PSO) was proposed, namely PSAO. First, the actual problem was modeled to a delay model and an energy consumption model. Since it was targeted at delay-sensitive applications, the model was transformed into a delay minimization problem under the constraints of energy consumption, and a penalty function was used to balance delay and energy consumption. Second, according to the PSO, the computation offloading decision vector was obtained, and each computation task was reasonably allocated to the corresponding MEC server through the centralized control method. Finally, through simulation experiments, the delay data of local offloading strategy, MEC baseline offloading strategy, Artificial Fish Swarm Algorithm (AFSA) based offloading strategy and PSAO were compared and analyzed. The average total delay of PSAO was much lower than those of the other three offloading strategies, and PSAO reduces the total cost of the original system by 20%. Experimental results show that the proposed strategy can effectively reduce the delay in MEC and balance the loads of MEC servers.

Key words: Mobile Edge Computing (MEC), computation offloading, Augmented Reality (AR), Particle Swarm Optimization (PSO) algorithm, industrial production line

摘要: 计算卸载作为移动边缘计算(MEC)中降低时延与能耗的手段之一,通过合理的卸载决策能够降低工业成本。针对工业生产线中部署MEC服务器后时延变长和能耗增高的问题,提出了一种基于粒子群优化(PSO)算法的计算卸载策略PSAO。首先,将实际问题建模为时延模型与能耗模型。由于是针对时延敏感型的应用,因此将模型转化为在能耗约束条件下的最小化时延问题,使用惩罚函数来平衡时延与能耗。其次,根据PSO算法优化后得到计算卸载决策向量,通过集中控制的方式使每一个计算任务合理分配到对应的MEC服务器。最后,通过仿真实验,对比分析了本地卸载策略、MEC基准卸载策略、基于人工鱼群算法(AFSA)的卸载策略以及PSAO的时延数据,PSAO的平均总时延远远低于其他三种卸载策略,PSAO比原来系统总代价降低了20%。实验结果表明,PSAO策略能够降低MEC中的时延,均衡MEC服务器的负载。

关键词: 移动边缘计算, 计算卸载, 增强现实, 粒子群优化算法, 工业生产线

CLC Number: