Constructing a DRL Decision Making Scheme for Multi-Path Routing in All-IP Access Network

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Abstract

Despite the massive increase in network capacities and devices capabilities, intra-domain TE problems have remained a challenge due to high traffic demand in dynamic network settings. In this paper, we fundamentally adopt and construct the use of Deep Reinforcement Learning (DRL) algorithm to improve end-to-end delay and maximum link utilisation (MLU) in a wireless All-IP access network. In an experimental pseudo setting, we propose a routing method DRL-MPR (Multi-Plane Routing) within a Dueling Deep Q-Network (DDQN) framework as controller in which simultaneously allocating multiple robust IP sessions, each session will take the near optimal path/plane.We first revisit Multi-Plane Routing Protocol (MPR) to configure a set of routing paths/Routing Planes (RP) from a physical topology as pre-condition ahead of the traffic injection. We compare our decision making scheme results with some traditional routing methodologies such as OSPF and standard Multi-Plane Routing (MPR). Our simulation results show that DRL obtains lower maximum link utilisation (MLU) and end-to-end delay. For example, in the 19 nodes, 41 links topology, small traffic load results in 60% maximum link utilisation for DRL-MPR, whereas those of OSPF and MPR are 100% and 94% respectively. In this simulation environment, the traffic demand in the IP access network is gradually increasing to the point of saturated network link utilisation.
Original languageEnglish
Title of host publicationIEEE Global Communications Conference (GLOBECOM)
Subtitle of host publicationIEEE GLOBECOM 2022
Place of PublicationRio de Janeiro, Brazil
Pages3630-3635
DOIs
Publication statusPublished - 4 Dec 2022

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