For effective computation offloading with multi-access edge computing (MEC), both communication and computation resources should be properly managed, considering the dynamics of mobile users such as the time-varying demands and user mobility. Most existing works regard the remote cloud server as a special edge server. However, service quality cannot be met when some of the edge servers cannot be connected. Besides, the computation capability of the cloud has not been fully exploited especially when edge servers are congested. We develop an on-line offloading decision and computational resource management algorithm with joint consideration of collaborations between device–cloud, edge–edge and edge–cloud. The objective is to minimize the total energy consumption of the system, subject to computational capability and task buffer stability constraints. Lyapunov optimization technique is used to jointly deal with the delay-energy trade-off optimization and load balancing. The optimal CPU-cycle frequencies, best transmission powers and offloading scheduling policies are jointly handled in the three-layer system. Extensive simulation results demonstrate that, with $V$ varies in $[0.1,5]\times 10^9$, the proposed algorithm can save more than 50% energy and over 120% task processing time than three existing benchmark algorithms averagely.

为了利用多路访问边缘计算（MEC）进行有效的计算分流，应考虑移动用户的动态（例如时变需求和用户移动性），对通信和计算资源进行适当管理。现有的大多数工作都将远程云服务器视为特殊的边缘服务器。但是，当某些边缘服务器无法连接时，将无法满足服务质量。此外，尤其是在边缘服务器出现拥塞时，云的计算能力尚未得到充分利用。我们结合设备-云，边缘-边缘和边缘-云之间的协作，开发了一种在线卸载决策和计算资源管理算法。目的是在受计算能力和任务缓冲区稳定性约束的情况下，将系统的总能耗降至最低。利用李雅普诺夫优化技术来联合处理时延-能量折衷优化和负载均衡。最佳的CPU周期频率，最佳的传输功率和卸载调度策略在三层系统中共同处理。大量的仿真结果表明，$V$ 在 $[0。\mathrm{1个}，5]\times \mathrm{1个}{0}^9$与现有的三种基准测试算法相比，该算法平均可节省50％以上的能源和120％以上的任务处理时间。