Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:TS6
Session:
Number:TS6-03
Title : Reinforcement Learning of Graph Neural Networks for Service Function Chaining in Computer Network Management
DongNyeong Heo, Doyoung Lee, Hee-Gon Kim, Suhyun Park, Heeyoul Choi,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
Abstract : In management of computer network systems, a service function chaining (SFC) module plays a vital role in generating an efficient network traffic path that connects virtualized network functions (VNF) on network topology to serve a user request. The SFC module needs to generate a complete path quickly even in various network situations, including dynamic VNF resources, various types of requests, and various network topologies to provide the best quality of service. The previous supervised learning method demonstrated that graph neural networks (GNN) could represent network features for the SFC task. However, the supervised learning method works properly only in the network situation in which the model was trained with labels. Due to the limitation, it showed poor performance on new unseen network situations. In this paper, we apply a reinforcement learning algorithm to train GNN based models in various network situations even without label information.