缓存辅助移动边缘计算的任务卸载与资源分配联合优化策略

为了减少资源受限的移动边缘计算场景下任务卸载和资源分配过程中的能量消耗，提出缓存辅助的动态卸载决策和计算、通信、缓存多维资源分配的联合优化策略。该策略根据任务流行度制定缓存服务，通过控制用户设备的发射功率优化通信资源分配，并结合计算卸载合理利用服务器的计算资源。提出最小化时延和能耗的均衡优化目标，设计基于深度强化学习的优化求解算法。最后，通过仿真实验验证所提策略的有效性，结果表明该策略在计算资源和缓存容量约束条件下能展现较优性能。

Joint Optimization Strategy of Task Offloading and Resource Allocation for Cache-assisted Mobile Edge Computing

In order to reduce energy consumption when tasks offloading and resource allocation in resource-constrained mobile edge computing scenarios, a cache-assisted dynamic offload decision-making and resource allocation joint optimization strategy for multidimensional resources including computing, communication, and caching resources is proposed. This strategy formulates caching services according to task prevalence, and optimizes allocation of communication resources by controlling the transmission power of the user device. It also rationally utilizes computing server resources in combination with computation offloading. This study aims to optimize equilibrium to minimize delay and reduce energy consumption. Therefore, we designed a new algorithm based on deep reinforcement learning. Finally, simulation results demonstrated the effectiveness and improved performance that the strategy achieved under the constraints of computing resources and cache capacity.