基于异步优势演员-评论家学习的服务功能链资源分配算法

考虑网络全局信息难以获悉的实际情况，针对接入网切片场景下用户终端(UE)的移动性和数据包到达的动态性导致的资源分配优化问题，该文提出了一种基于异步优势演员-评论家(A3C)学习的服务功能链(SFC)资源分配算法。首先，该算法建立基于区块链的资源管理机制，通过区块链技术实现可信地共享并更新网络全局信息，监督并记录SFC资源分配过程。然后，建立UE移动和数据包到达时变情况下的无线资源、计算资源和带宽资源联合分配的时延最小化模型，并进一步将其转化为马尔科夫决策过程(MDP)。最后，在所建立的MDP中采用A3C学习方法，实现资源分配策略的求解。仿真结果表明，该算法能够更加合理高效地利用资源，优化系统时延并保证UE需求。

Resource allocation Algorithm of Service Function Chain Based on Asynchronous Advantage Actor-Critic Learning

Considering the fact that global network information is hard to obtain, and the slice resource allocation optimization problem caused by mobility of User Equipment (UE) and dynamics of packet arrival in the radio access network slice, a Service Function Chain(SFC)resource allocation algorithm based on Asynchronous Advantage Actor-Critic (A3C) learning is proposed. Firstly, a resource management mechanism based on blockchain technology is established, which can credibly share and update the global network information, also supervise and record SFC resource allocation process. Then, a delay minimization model based on joint allocation of radio resources, computing resources and bandwidth resources is built under the circumstance of UE moving and time-varying packet arrival, and further transformed into an Markov Decision Process(MDP) problem. At last, A3C learning method is adopted to obtain the resource allocation optimization strategy in this MDP. Simulation results show that the proposed algorithm could utilize resources more efficiently to optimize the system delay while guarantee the requirement of each UE.