Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

TL;DR

This paper proposes a deep reinforcement learning-based algorithm for dynamic service function chain scheduling in space-air-ground integrated networks, addressing resource allocation challenges caused by mobility and heterogeneity.

Contribution

It introduces a reconfigurable time extension graph and formulates a novel scheduling model optimized via deep reinforcement learning.

Findings

The algorithm outperforms benchmark methods in convergence.

It effectively maximizes successful SFC deployments.

Simulation results validate improved scheduling performance.

Abstract

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehicles in multi-layer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms.