面向移动边缘计算网络的高能效计算卸载算法

移动边缘计算（mobile edge computing，MEC）是一种高效的技术，通过将计算密集型任务从移动设备卸载到边缘服务器，使终端用户实现高带宽、低时延的目标.移动边缘计算环境下的计算卸载在减轻用户负载和增强终端计算能力等方面发挥着重要作用.考虑了服务缓存，提出一种云-边-端协同的计算卸载框架，在该框架中引入D2D （device-to-device，D2D）通信和机会网络.基于建立的模型，将计算卸载决策问题转化为一个混合整数非线性规划问题，并对无线特性和移动用户之间的非合作博弈交互制定了一个迭代机制来共同确定计算卸载方案.对提出的计算卸载算法从理论上证明了多用户计算卸载博弈模型为严格势力场博弈（exact potential game，EPG），卸载决策可获得全网范围内的最优效益.考虑到服务器的计算资源、卸载任务数据量和任务延迟需求，提出对用户和MEC服务器之间最佳用户关联匹配算法.最后，模拟结果表明，卸载决策算法具有较快的收敛速度，并在能效方面优于其他基准算法.

Energy-efficient Computing Offloading Algorithm for Mobile Edge Computing Network

Mobile edge computing (MEC) is an efficient technology that enables end users to achieve the goal of high bandwidth and low latency by offloading computationally intensive tasks from mobile devices to edge servers. Computing offloading in the mobile edge computing environment plays an important role in reducing user load and enhancing terminal computing capabilities. This study considers service caching, and proposes a cloud-side-end collaborative computing offloading framework, in which D2D communication and opportunistic networks are introduced. Based on the established model, the offloading decision problem is transformed into a mixed integer nonlinear programming problem, and an iterative mechanism is formulated for the non-cooperative game interaction between wireless characteristics and mobile users to jointly determine the computational offloading plan. The proposed computational offloading algorithm theoretically proves that the multi-user computational offloading game under this framework is an exact potential game (EPG), and the offloading decision is to uninstall under the optimal benefit strategy in the entire network. Taking into account the computing resources of the server, the amount of data for offloading tasks, and the delay requirements of tasks, based on the Gale-Shapley matching theory, the best user association matching algorithm is improved and proposed. Finally, the simulation results show that the proposed unloading decision algorithm has a faster convergence rate and is superior to other benchmark algorithms in terms of energy efficiency.