Attention Model-Driven Maddpg Algorithm for Delay and Cost-Aware Placement of Service Function Chains in 5g

Abstract

The rapidly expanding applications of 5G networks necessitate strategic placement of Virtual Network Functions (VNFs) within Service Function Chains (SFCs) to minimize placement costs while delivering real-time services to users. The dual objectives of this efficient placement strategy are to simultaneously reduce resource usage costs and application service delays in the 5G network. Previous studies have limitations, typically constrained by fixed resource costs or by adopting a greedy approach for resource selection from nearby nodes. In this paper, we introduce a multi-objective linear programming (MOLP) based optimization framework designed for the placement of VNFs in SFC requests, considering a real-time pricing scheme of the resources and the demands of user applications. This framework allows for the analysis of the boundary performances regarding cost and delay, facilitating a balanced trade-off between the two. Given that this problem is proven to be NP-hard in large networks, we have also developed a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which leverages an attention model-based approach for the placement of SFC VNFs. This method focuses on neighboring nodes to help agents reduce the complexity of the solution and effectively capture the dynamic nature of the network environment. Simulation experiments demonstrate that our proposed system model surpasses existingstate-of-the-art approaches in terms of resource placement cost and service latency.

Keywords: Network Function Virtualization, Service Function Chain, VNF Placement, Attention Model, Multi-Agent deep reinforcement learning