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

Congzhou Li, Zhouxiang Wu, Divya Khanure, Jason P. Jue

TL;DR

The proposed scheme employs a set of RL agents to collaboratively make SFC placement decisions, such as path selection, VNF configuration, and VNF deployment, and can improve the profit of edge service providers by 12% compared with a heuristic solution.

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.

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

TL;DR

The proposed scheme employs a set of RL agents to collaboratively make SFC placement decisions, such as path selection, VNF configuration, and VNF deployment, and can improve the profit of edge service providers by 12% compared with a heuristic solution.

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.
Paper Structure (18 sections, 16 equations, 6 figures, 1 table)

This paper contains 18 sections, 16 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model and Problem Formulation
  4. Network Model
  5. SFC request model
  6. Problem Formulation
  7. Proposed Scheme
  8. Scheme Overview
  9. Environment Design
  10. Agent Design
  11. Training Algorithm
  12. Experiment and Evaluation
  13. Experiment Settings
  14. Baseline Algorithms
  15. Reinforcement Learning Settings
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Example Deployment Patterns of Three SFCs
  • Figure 2: Workflow of Multi-Agent System
  • Figure 3: Experiment Topology
  • Figure 4: Performance Comparison
  • Figure 5: Influence of Arrival Intensity
  • ...and 1 more figures