MEC中资源分配与卸载决策联合优化策略

针对移动边缘计算(MEC)中用户任务处理时延与能耗过高的问题,提出了“云-边-端”三层MEC计算卸载结构下的资源分配与卸载决策联合优化策略。首先,考虑系统时延与能耗,将优化问题规划为系统总增益(任务处理时延与能耗相对减少的加权和)最大化问题;其次,为用户任务设置优先级,并根据任务数据量初始化卸载决策方案;然后,采用均衡传输性能的信道分配算法为卸载任务分配信道资源,对于卸载至同一边缘服务器上的任务以最大化资源收益为目标进行资源竞争,实现计算资源最优配置;最后,基于博弈论证明优化问题为关于卸载决策的势函数,即存在纳什均衡,并利用迭代增益值比较法得到了纳什均衡下的卸载决策方案。仿真结果表明,所提联合优化策略在满足用户处理时延要求的情况下最大化系统总增益,有效地提高了计算卸载的性能。

Joint Optimization Scheme of Resource Allocation and Offloading Decision in Mobile Edge Computing

Considering the problem of users'high processing delay and energy consumption in mobile edge computing(MEC),a joint optimization scheme of resource allocation and offloading decision based on the“cloud-edge-end”three-tier MEC computation offloading structure is proposed.Firstly,considering the system delay and energy consumption,the optimization problem is studied in order to maximize the users'task offloading gain,which is measured by a weighted sum of reductions in tasks'relative processing delay and energy consumption.Secondly,the priority is set for users'tasks and the offloading decision is initialized according to the data size of tasks.Then,the channel allocation algorithm that balances transmission performance is proposed to allocate channel resources for offloading tasks.For the tasks that are offloaded to the same edge server,the optimal allocation of computing resources can be achieved by computing for resources with the goal of maximizing resources'profit.Finally,the optimization problem is proven to be a potential function about the offloading decision based on game theory,that is,there exists a Nash equilibrium,and the iterative method by comparing the gain value is used to achieve the offloading decision under Nash equilibrium.The simulation results show that the proposed joint optimization scheme achieves the maximum total system gain under meeting the processing delay requirements of users,and effectively improves the performance of computation offloading.