Meeting the strict Quality of Service (QoS) requirements of terminals has
imposed a signiffcant challenge on Multiaccess Edge Computing (MEC) systems,
due to the limited multidimensional resources. To address this challenge, we
propose a collaborative MEC framework that facilitates resource sharing between
the edge servers, and with the aim to maximize the long-term QoS and reduce the
cache switching cost through joint optimization of service caching,
collaborative offfoading, and computation and communication resource
allocation. The dual timescale feature and temporal recurrence relationship
between service caching and other resource allocation make solving the problem
even more challenging. To solve it, we propose a deep reinforcement learning
(DRL)-based dual timescale scheme, called DGL-DDPG, which is composed of a
short-term genetic algorithm (GA) and a long short-term memory network-based
deep deterministic policy gradient (LSTM-DDPG). In doing so, we reformulate the
optimization problem as a Markov decision process (MDP) where the
small-timescale resource allocation decisions generated by an improved GA are
taken as the states and input into a centralized LSTM-DDPG agent to generate
the service caching decision for the large-timescale. Simulation results
demonstrate that our proposed algorithm outperforms the baseline algorithms in
terms of the average QoS and cache switching cost.

通过多维资源的资源共享、服务缓存、深度强化学习等方面的联合优化，我们提出了一种协同多接入边缘计算（MEC）框架，旨在最大化长期的服务质量（QoS）并降低缓存切换成本。通过改进的遗传算法（GA）和基于长短期记忆网络的深度确定性策略梯度（LSTM-DDPG）组成的深度强化学习（DRL）的双时间尺度方案（DGL-DDPG），我们将优化问题转化为马尔可夫决策过程（MDP），以实现大时间尺度上的服务缓存决策。仿真结果表明，我们提出的算法在平均 QoS 和缓存切换成本方面优于基准算法。