Adaptive service function chaining mappings in 5G using deep Qlearning Abstract   With introduction of SoftwareDefined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide ondemand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multistep Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Qlearning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two lowcomplexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a realworld substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILPbased approach or the proposed heuristic algorithms, our ADAP approach can improve wholesystem resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.

Joint CorrelationAware VNF Selection and Placement in Cloud Data Center Networks Network Function Virtualization (NFV) brings great flexibility and scalability to the deployment of network services by decoupling network functions from dedicated devices, which has attracted more attention from both academia and industry. Network services in NFV are deployed in the form of Service Function Chain (SFC), which consists of multiple ordered Virtual Network Functions (VNFs). However, how to effectively place VNFs remains a problem to be solved. In this paper, we investigate joint correlationaware VNF selection and placement problem. We first formulate the problem as an Integer Linear Programming (ILP) problem and propose a method based on selflearning matrix to partition VNF correlation. Then, we design a Joint Correlationaware VNF Placement (JCVP) algorithm based on Dynamic Programming to transform the problem into several VNF mapping subproblems. Extensive simulation results show that compared with the previous algorithms our approach has better performance in link occupancy, SFC acceptance, and VNF utilization rate.