Reinforcement Learning of Graph Neural Networks for Service Function Chaining

11/17/2020
by   DongNyeong Heo, et al.
0

In the management of computer network systems, the service function chaining (SFC) modules play an important role by generating efficient paths for network traffic through physical servers with virtualized network functions (VNF). To provide the highest quality of services, the SFC module should generate a valid path quickly even in various network topology situations including dynamic VNF resources, various requests, and changes of topologies. The previous supervised learning method demonstrated that the network features can be represented by graph neural networks (GNNs) for the SFC task. However, the performance was limited to only the fixed topology with labeled data. In this paper, we apply reinforcement learning methods for training models on various network topologies with unlabeled data. In the experiments, compared to the previous supervised learning method, the proposed methods demonstrated remarkable flexibility in new topologies without re-designing and re-training, while preserving a similar level of performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/03/2020

Sampling and Recovery of Graph Signals based on Graph Neural Networks

We propose interpretable graph neural networks for sampling and recovery...
research
09/11/2020

Graph Neural Network based Service Function Chaining for Automatic Network Control

Software-defined networking (SDN) and the network function virtualizatio...
research
06/04/2021

Nara: Learning Network-Aware Resource Allocation Algorithms for Cloud Data Centres

Data centres (DCs) underline many prominent future technological trends ...
research
01/06/2019

Path Computation for Provisioning in Multi-Technology Multi-Layer Transport Networks

Service providers employ different transport technologies like PDH, SDH/...
research
04/20/2021

GDDR: GNN-based Data-Driven Routing

We explore the feasibility of combining Graph Neural Network-based polic...
research
01/28/2022

RiskNet: Neural Risk Assessment in Networks of Unreliable Resources

We propose a graph neural network (GNN)-based method to predict the dist...

Please sign up or login with your details

Forgot password? Click here to reset