分层学习的边缘计算资源调度粒子群优化算法

doi:10.3778/j.issn.1002-8331.2110-0060

摘要/Abstract

摘要： 由于存在诸如CPU运算速度慢，电池容量低等问题，智能移动设备本身无法执行计算需求大的应用程序，需要借助边缘计算技术来降低程序对移动设备硬件的要求。然而将部分计算任务从移动设备传输给边缘服务器，会带来额外的传输能耗和服务器计算能耗。综合考虑影响移动设备和服务器，以及数据传输能耗值的四个因素，即移动设备的计算速度，下载数据功耗，数据卸载百分比和剩余网络带宽占，提出一种基于分层学习的粒子群算法，优化每台移动设备对于这四个参数的取值，更合理分配计算资源使得总能耗最小。对计算资源建模时，还考虑了最大能耗、计算周期、存储、带宽和延迟约束条件。与其他算法进行对比实验发现，通过分层学习优化的粒子群算法，能更快速地获得满足约束条件具有更低能耗的资源调度最优解。

关键词: 计算卸载, 资源调度, 分层学习, 粒子群算法

Abstract: Due to problems such as slow CPU operation speed and low battery capacity, smart mobile devices themselves cannot execute applications with large computing requirements. It needs the help of edge computing technology to reduce the requirements of programs on mobile device hardware. However, by transferring the computing burden from mobile devices to edge computing servers, additional transmission energy and server computing power consumption are needed. By considering the four factors influencing the energy consumptions of mobile devices, servers, and data transmission, i.e. the computing speed, data downloading power consumption, the proportion of data offloading and the remain network bandwidth, this paper proposes a level-based learning particle swarm optimization algorithm to optimize the values of these four parameters for each mobile device and more reasonably allocate of computing resources for minimizing the total energy consumption. While modeling of computing resources, the constraints of maximum energy consumption, computing cycle, storage, bandwidth and delay are also considered. Compared with other algorithms, experiments show that the level-based learning particle swarm optimization algorithm can obtain the optimal resource scheduling solution which meet the constrains and with lower energy consumption more quickly.

Key words: computing offloading, resource scheduling, level-based learning, particle swarm optimization algorithm

胡晓敏, 陈镇填, 李敏. 分层学习的边缘计算资源调度粒子群优化算法[J]. 计算机工程与应用, 2022, 58(24): 107-115.

HU Xiaomin, CHEN Zhentian, LI Min. Level-Based Learning Swarm Optimization Algorithm for Resource Scheduling in Edge Computing[J]. Computer Engineering and Applications, 2022, 58(24): 107-115.

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