基于DQN的电力物联网5G边缘切片资源管理研究

随着5G通信技术以及移动边缘计算(Mobile Edge Computing,MEC)的发展,各式各样的电力物联网新需求层出不穷.一方面,部分新型电力物联网业务需要高服务质量保障;另一方面,移动边缘计算需要为新型电力物联网的业务提供差异化的计算服务.为解决上述问题,文章定义了一种面向电力物联网业务的可靠性衡量指标.基于...

Research on DQN-based 5G Edge Slicing Resource Management of Power Internet of Things

With the development of 5G communications and Mobile Edge Computing (MEC), a variety of new demands for power Internet of Things (PIoT) have emerged. On the one hand, these new PIoT applications usually request for high quality of service (QoS) guarantee. On the other hand, service providers are desired to have elastic framework for diverse service level agreements (SLA). The feature technologies of network slicing and MEC have constituted a practical viable framework for solving these challenges. This article first defined a metric to measure the reliability of PIoT services. After that, a deep reinforcement learning based network slicing approach is proposed to jointly optimize the computing and communication resources. The proposed approach ensures the latency and reliability of the PIoT services while minimizing their energy consumption. Through simulation experiments, the slice management approach is demonstrated to outperform the traditional baseline approach.