A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

Shengyao Sun, Ying Du, Jiajun Chen, Xuan Zhang, Jiwei Zhang, Yiyi Xu
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Abstract

In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks, it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for offloading. Based on fully considering the competition between multiple UEs for communication resources and MEC service resources, CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’ resources. It achieves its goal by selecting reasonable modules to offload and using the cooperation of UE, MEC, and Cloud Center to determine the execution location of the modules. Extensive experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6% on average, task completion delay reducing around 5% on average, and better task execution success rate than other similar methods.
多约束下多ue协作模式下的部分任务卸载新方法
在多接入边缘计算(MEC)中,针对多约束条件下多用户设备(UE)并行关系的任务卸载问题,提出了一种合作部分任务卸载方法(CPMM),旨在降低UE的能量和计算消耗,同时尽可能满足任务完成延迟。CPMM首先研究单用户任务卸载,然后在单用户任务卸载的基础上考虑多用户任务卸载。CPMM使用关键路径算法将模块划分为关键模块和非关键模块。根据UE-self在卸载任务时的一些约束条件,优先考虑非关键模块进行卸载,并采用评价决策方法选择合适的关键模块进行卸载。CPMM在充分考虑多个终端对通信资源和MEC服务资源竞争的基础上,采用加权排队法缓解通信资源竞争,并根据MEC服务器资源情况,采用分支决策算法确定BS卸载模块的位置。通过选择合理的模块进行卸载,并利用UE、MEC和Cloud Center的协同来确定模块的执行位置,从而达到了目标。大量实验表明,CPMM在任务计算消耗平均减少6%左右,任务完成延迟平均减少5%左右,任务执行成功率优于其他同类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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