随着无人驾驶、在线游戏、虚拟现实等低延迟应用的大量涌现, 传统集中式的移动云计算范式越来越难以满足此类用户服务质量的需求. 为弥补云计算的不足, 移动边缘计算应运而生. 移动边缘计算通过计算卸载, 将计算任务迁移到网络边缘服务器来为用户提供计算和存储资源. 然而, 现有大部分工作仅考虑了延迟或能耗的单目标性能优化, 未考虑延迟和能耗的均衡优化. 为减少任务延迟和设备能耗, 提出了一种面向多用户的联合计算卸载和资源分配策略. 该策略首先利用拉格朗日乘子法获得给定卸载决策的最佳计算资源分配; 然后, 提出一个基于贪心算法的计算卸载算法获得最佳卸载决策; 最后, 通过不断迭代得到最终解. 实验结果表明, 与基准算法相比, 所提算法最高可以降低40%的系统成本.

With the emergence of low-latency applications such as driverless cars, online gaming, and virtual reality, it is becoming increasingly difficult to meet users’ demands for service quality using the traditional centralized mobile cloud computing model. In order to make up for the shortages of cloud computing, mobile edge computing came into being, which provides users with computing and storage resources by migrating computing tasks to network edge servers through computation offloading. However, most of the existing work processes only consider single-objective performance optimization of delay or energy consumption, and do not consider the balanced optimization of delay and energy consumption. Therefore, in order to reduce task delay and equipment energy consumption, a multi-user joint computation offloading and resource allocation strategy is proposed. In this strategy, the Lagrange multiplier method is used to obtain the optimal allocation of computing resources for a given offloading decision. Then, a computation offloading algorithm based on a greedy algorithm is proposed to obtain the optimal offloading decision; the final solution is obtained through continuous iteration. Experimental results show that, compared with the benchmark algorithm, the proposed algorithm can reduce system costs by up to 40%.