Deep Reinforcement Learning based Green Resource Allocation Mechanism in Edge Computing driven Power Internet of Things

Smart grid deploys a large number of smart terminals and sensing devices to form an edge network, as well as a virtual network of information space and the power Internet of Things. As a key component of 5G and future network, the latency of end-to-end and the traffic of backhaul link could be reduced by edge network. Nevertheless, the function of storage and computing are moved down to the edge nodes in mobile edge network which increases the complexity of resource management. So it is an important issue to find out a more effectively resources allocation mechanism as well as meeting the requirements of each user. Edge computing refers to the processing of large amounts of edge data in the edge space in the edge network, thereby reducing dependence on the data center, achieving limited self-governance of the edge network, and reducing off-line threats. Although Deep Reinforcement Learning (DRL) has been applied to many of the work related to edge networks, there lacks the applications for green resource allocation. A Deep Reinforcement Learning (DRL) based green resource allocation mechanism is proposed in this paper which aims at efficiently allocating the resources while satisfying the needs of mobile users. The value of energy efficiency can be obtained when the algorithm achieves convergence according to the simulation results. The efficiency of the DRL-based mechanism and its effectiveness in meeting user requirements and implementing green resource allocation are validated.