计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 32-41.doi: 10.11896/jsjkx.220300198
陈祎鹏1,2,3, 杨哲1,2,3, 谷飞1,2, 赵雷1,2,3
CHEN Yipeng1,2,3, YANG Zhe1,2,3, GU Fei1,2, ZHAO Lei1,2,3
摘要： 现有的对移动边缘计算资源分配策略问题的研究,较多的是针对时延和能耗因素进行优化,考虑边缘服务器的收益问题的相对较少,而在考虑边缘服务器收益时,许多研究忽略了对任务完成时延的优化。因此,提出了一种基于博弈论的双向更新策略(TUSGT)。TUSGT在边缘服务器侧将其之间的任务竞争关系转化为一个非合作博弈问题,采用基于势博弈的联合优化策略,允许边缘服务器以最大化其自身收益为目的来确定任务选择偏好。在移动设备侧使用在线学习中的EWA算法进行参数更新,从全局角度影响边缘服务器的任务选择偏好,提高总体任务完成率。仿真实验结果表明,TUSGT与BGTA、MILP、贪婪策略、随机策略、理想策略相比,任务完成率最多提高30%,边缘服务器平均收益最多提高65%。
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