Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system performance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.

边缘计算作为云计算的补充范式，通过在网络边缘为移动用户提供多样化的服务而受到越来越多的关注。然而，日益复杂的移动应用给边缘网络带来了更重的负载。为并发请求提供高质量的服务处理是一项挑战，尤其是在边缘网络动态变化的情况下。针对上述问题，本文研究了边缘协作网络中的在线并发用户请求调度优化问题。我们将其建模为在线多阶段决策问题，其中请求被分成一组独立且逻辑上相关的子任务。我们提出了一种基于集中训练分布式执行的多智能体深度强化学习技术，以实现边缘节点间的隐式协作调度决策策略学习。在所提出机制的集中训练阶段，采用基于价值分解的策略学习技术来提高系统的长期性能，而在分布式执行阶段，每个边缘节点只需要本地环境状态信息即可请求调度决策。进行了大量实验，仿真结果表明，所提出的机制在降低长期平均系统延迟和能量消耗，同时提高系统吞吐率方面优于其他请求调度机制。在所提出机制的集中训练阶段，采用基于价值分解的策略学习技术来提高系统的长期性能，而在分布式执行阶段，每个边缘节点只需要本地环境状态信息即可请求调度决策。进行了大量实验，仿真结果表明，所提出的机制在降低长期平均系统延迟和能量消耗，同时提高系统吞吐率方面优于其他请求调度机制。在所提出机制的集中训练阶段，采用基于价值分解的策略学习技术来提高系统的长期性能，而在分布式执行阶段，每个边缘节点只需要本地环境状态信息即可请求调度决策。进行了大量实验，仿真结果表明，所提出的机制在降低长期平均系统延迟和能量消耗，同时提高系统吞吐率方面优于其他请求调度机制。每个边缘节点只需要本地环境状态信息来做出请求调度决策。进行了大量实验，仿真结果表明，所提出的机制在降低长期平均系统延迟和能量消耗，同时提高系统吞吐率方面优于其他请求调度机制。每个边缘节点只需要本地环境状态信息来做出请求调度决策。进行了大量实验，仿真结果表明，所提出的机制在降低长期平均系统延迟和能量消耗，同时提高系统吞吐率方面优于其他请求调度机制。