MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration
This work addresses network resource optimization for service providers, but it is incremental as it builds on existing reinforcement learning methods for a specific domain.
The paper tackles the problem of service function chain migration under dynamic traffic by proposing MSDF, a multi-agent deep reinforcement learning framework that reduces total network operation cost while meeting quality of service constraints, and it outperforms typical heuristic algorithms in experiments.
Under dynamic traffic, service function chain (SFC) migration is considered as an effective way to improve resource utilization. However, the lack of future network information leads to non-optimal solutions, which motivates us to study reinforcement learning based SFC migration from a long-term perspective. In this paper, we formulate the SFC migration problem as a minimization problem with the objective of total network operation cost under constraints of users' quality of service. We firstly design a deep Q-network based algorithm to solve single SFC migration problem, which can adjust migration strategy online without knowing future information. Further, a novel multi-agent cooperative framework, called MSDF, is proposed to address the challenge of considering multiple SFC migration on the basis of single SFC migration. MSDF reduces the complexity thus accelerates the convergence speed, especially in large scale networks. Experimental results demonstrate that MSDF outperforms typical heuristic algorithms under various scenarios.