The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing and mobile edge computing (MEC) to provide customized networks while reducing the service processing time. Due to the uncertainty of user requests and the environment, reasonable resource allocation is always particularly challenging. A novel dynamic resource allocation scheme for MEC slice systems, which formulates resource allocation and computation offloading issues as an optimization problem subject to the latency and rate, is proposed. Based on the dynamics of the slice requirements, quantity, and service time, the proposed problem is converted to a Markov decision process (MDP), and a state, action, and reward function are proposed. By exploiting the deep deterministic policy gradient (DDPG) algorithm, the wireless resources and computing resources are configured dynamically according to the requirements of different types of slices to maximize the revenue of the network operator. The simulation results demonstrate the influence of the slice arrival rate and total resources on the allocation policy. Compared with other schemes, the proposed scheme can provide a more effective performance when resources are scarce.

中文翻译：

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动态资源分配方案和基于深度确定性策略梯度的移动边缘计算切片系统


多行业兼容的发展以及多种业务、多种功能的通信网络的共存将导致移动通信系统流量的快速增长。用户对服务质量（QoS）的要求将越来越严格，例如高速率、低时延、低能耗等。为了解决这些问题，将网络切片和移动边缘计算（MEC）结合起来，提供定制网络，同时减少业务处理时间。由于用户请求和环境的不确定性，合理的资源分配总是特别具有挑战性。提出了一种针对 MEC 切片系统的新型动态资源分配方案，该方案将资源分配和计算卸载问题表述为受延迟和速率影响的优化问题。基于切片需求、数量和服务时间的动态，将所提出的问题转换为马尔可夫决策过程（MDP），并提出状态、动作和奖励函数。利用深度确定性策略梯度（DDPG）算法，根据不同类型切片的需求动态配置无线资源和计算资源，实现网络运营商收益最大化。仿真结果验证了切片到达率和资源总量对分配策略的影响。与其他方案相比，该方案在资源稀缺时可以提供更有效的性能。