A Service Function Chain Deployment Method Based on Transfer A-C Learning

Abstract

The invention relates to a service function chain deployment method based on migration A-C learning, which belongs to the technical field of mobile communication. In this method, the end-to-end delay minimization model of the system based on the joint allocation of virtual network function placement, computing resources, link bandwidth resources and fronthaul network bandwidth resources is first established, and transformed into a continuous state and action space Discrete-time Markov decision process; in the MDP, the A‑C learning algorithm is used to continuously interact with the environment to dynamically adjust the SFC deployment strategy and optimize the end-to-end delay; further, in order to realize and accelerate the A‑C algorithm in other Similar to the convergence process in the target task, the idea of ​​transfer learning is introduced, and the transfer A-C learning algorithm is used to realize the deployment strategy of quickly finding the target task by using the SFC deployment knowledge learned in the source task. The method proposed by the invention can reduce and stabilize the queue backlog of SFC data packets, optimize system end-to-end time delay, and improve resource utilization.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and relates to a service function chain (Service Function Chain, SFC) deployment method based on transfer actor-critic (Actor-Critic) learning. Background technique [0002] In order to cope with the explosive growth of the number, types and business volume of access terminals, the 5G network needs to be able to support a large number of diversified business application scenarios from vertical industries at the same time, so as to meet the requirements of differentiated services on network throughput, delay, number of connections and Different requirements for indicators such as reliability. If a single physical network in the traditional communication network is still used to provide services for multiple application scenarios at the same time, there will be problems such as unsatisfactory network management efficiency, low resource utilization, and extremely complex network framewo...

AI Technical Summary

Problems solved by technology

[0008] S1: Aiming at the problem of high system latency caused by unreasonable resource allocation due to the randomness and unknownness of service requests in the 5G network slicing environment, establish a network based on virtual network function (Virtual Network Function, VNF) placement, computing resources, physical The network model of the system end-to-end delay minimization Service Function Chain (SFC) deployment of link bandwidth resources and fronthaul network bandwidth resources joint allocation;

Images