Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed that considering the quality of service (QoS) of users, server resources, and channel interference. This framework consists of three levels. (1) In the offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of the local computing of mobile devices in comparison with the edge-side server. (2) In the edge server selection stage, the candidate is comprehensively evaluated and selected by a multiobjective decision based on the Analytic Hierarchy Process based on Covariance (Cov-AHP) for computation offloading. (3) In the channel selection stage, a multiuser and multichannel distributed computation offloading strategy based on the potential game is proposed by considering the influence of channel interference on the user’s overall overhead. The corresponding multiuser and multichannel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game. Amounts of experimental results show that the proposed framework can greatly increase the number of beneficial computation offloading users and effectively reduce the energy consumption and time delay.

中文翻译：

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移动边缘计算中计算分流的多层优化框架

移动边缘计算是一种新的计算范例，可以将云计算功能扩展到边缘网络，从而支持诸如面部识别，自然语言处理和增强现实之类的计算密集型应用程序。值得注意的是，计算卸载是移动边缘计算的一项关键技术，该技术通过将本地任务卸载到边缘服务器来提高移动设备的性能和用户体验。本文研究了在多用户，多服务器和多通道情况下的计算分流问题，提出了一种考虑用户的服务质量，服务器资源和信道干扰的计算分流框架。该框架包括三个层次。（1）在卸载决策阶段，卸载决策是基于计算卸载的有益程度做出的，计算卸载的有利程度是通过与边缘服务器相比，移动设备本地计算的总成本来衡量的。（2）在边缘服务器选择阶段，基于基于协方差的层次分析法（Cov-AHP）的多目标决策对候选人进行综合评估和选择，以进行计算分流。（3）在信道选择阶段，考虑了信道干扰对用户总体开销的影响，提出了一种基于潜在博弈的多用户，多信道分布式计算分流策略。设计相应的多用户和多通道任务调度算法，以通过找到潜在游戏的Nash平衡点来最大化总体收益。