A Multi-Agent Reinforcement Learning Scheme for SFC Placement in Edge Computing Networks

TL;DR

This paper introduces a multi-agent reinforcement learning approach for placing Service Function Chains in edge computing networks, optimizing resource use and increasing provider profits.

Contribution

It proposes a novel multi-agent RL scheme for SFC placement in edge networks, addressing resource allocation and profit maximization.

Findings

Improves edge service provider profit by 12% over heuristics.

Demonstrates effective collaborative decision-making among RL agents.

Enhances resource allocation efficiency in edge environments.

Abstract

In the 5G era and beyond, it is favorable to deploy latency-sensitive and reliability-aware services on edge computing networks in which the computing and network resources are more limited compared to cloud and core networks but can respond more promptly. These services can be composed as Service Function Chains (SFCs) which consist of a sequence of ordered Virtual Network Functions (VNFs). To achieve efficient edge resources allocation for SFC requests and optimal profit for edge service providers, we formulate the SFC placement problem in an edge environment and propose a multi-agent Reinforcement Learning (RL) scheme to address the problem. The proposed scheme employs a set of RL agents to collaboratively make SFC placement decisions, such as path selection, VNF configuration, and VNF deployment. Simulation results show our model can improve the profit of edge service providers by 12\% compared with a heuristic solution.