Dynamic Network Slicing for Fog Radio Access Networks Fog radio access network (FRAN) has been recently proposed to satisfy the qualityofservice (QoS) requirements of the ultrareliablelowlatencycommunication (URLLC) IoT applications, hence fog nodes are empowered with computing and storage resources to independently deliver network functionalities at the edge of network without referring the users to the cloud. However, due to their limited resources, fog nodes should utilize their resources intelligently for low latency IoT applications to leverage the complementarity with cloud computing. We consider the problem of sequentially allocating fog nodexe2x80x99s limited resources to various IoT applications with heterogeneous latency needs. We formulate the problem as a finitehorizon Markov Decision Process (MDP), and present the optimal solution, known as the optimal policy, through dynamic programming. The fog node learns the optimal policy through interaction with the IoT environment, which enables adaptive resource allocation in different IoT environments. Comprehensive simulation results for various IoT environments corroborate the theoretical basis of the proposed MDP method.

RealTime Resource Slicing for 5G RAN via Deep Reinforcement Learning With the rapid growth of Internet of Things (IoT), network slicing is regarded as an important technology to support the multiusersu0027 needs for 5G mobile network. Network slicing allows network operators to provide services to different users, which can improve the rational utilization of network and hardware resources. In order to ensure the quality of service and build lowcost network infrastructure services, it is a challenging problem to find an appropriate resource allocation mechanism. In this paper, we discuss resource allocation in 5G radio access network (RAN). Considering the realtime resource request of the slice user, we propose a semiMarkov decision system model, which enables the virtual network provider to effectively satisfy the different user demands in real time. Then, we propose a resource slicing algorithm based on deep reinforcement learning (RSDRL), which aims to improve the longterm benefits of virtual network providers and the utilization of slicing resources. We evaluate the performance of the RSDRL through evaluations and comparisons. The results show that the proposed RSDRL algorithm can effectively improve the performance and achieve the longterm benefits quickly.