基于算力-能量全分布式在线共享的5G网络负荷管理策略

5G与边缘计算等信息基础设施海量部署造成运营商用电成本上升，需推动边缘网络与电网的能量互动以节能降本。现有研究重点关注边缘网络参与日前经济调度，未考虑可再生能源和网络流量双重随机性造成的网络能量供需不平衡问题。针对强随机环境下的网络负荷管理问题，提出面向虚拟化边缘网络的能量实时管理策略。首先，以网络用能成本最小化为目标，构建联合网络资源管理、储能充放电与能量共享模型。其次，针对未来网络信息未知无法直接求解的问题，提出基于随机对偶次梯度法的在线管理策略。然后，针对资源共享涉及运营商隐私问题，提出全分布式的计算资源与能量协同共享算法。最后，仿真验证表明，所提在线算法在无需先验知识的前提下有效减少了5G边缘网络的用能成本。

Energy optimization strategy of a 5G edge network based on load-energy online full-distributed sharing

The massive deployment of information infrastructure including 5G base station and edge computing server has increased the electricity purchasing cost for network operators. Thus it is necessary to promote the interaction of energy between the edge network and power grid to achieve energy saving and cost reduction. Current studies mainly focus on the edge network participating in a day-ahead economic dispatch strategy without considering that the double randomness of renewable energy and network traffic may cause the mismatch of energy supply and demand in the network. To cope with the network load management problem in a strongly random environment, a real-time energy management strategy faced to a virtualized network is proposed. First, to minimize the sum of time-average energy cost, a joint resource allocation, energy storage and energy sharing model is proposed. Second, to solve the proposed multi-slot problem with future network information being unknown, this paper proposes an online resource allocation algorithm based on a stochastic dual-subgradient method. Also, a full distributed energy-computing resource sharing strategy is investigated considering the protection of privacy of operators. Finally, the simulations show that proposed online algorithm effectively reduces the total energy purchasing cost of a 5G edge network without a-priori knowledge.