Service Function Chain Routing in LEO Networks Using Shortest-Path Delay Statistical Stability

TL;DR

This paper introduces a stability-aware routing method for LEO satellite networks that uses statistical delay properties to improve reliability and reduce latency in service function chaining.

Contribution

It proposes the SA-MSGR algorithm that leverages delay stability analysis to enhance routing efficiency in dynamic LEO satellite networks.

Findings

SA-MSGR achieves lower end-to-end delays.

The approach improves delay predictability.

Statistical delay stability is validated in simulations.

Abstract

Low Earth orbit (LEO) satellite constellations have become a critical enabler for global coverage, utilizing numerous satellites orbiting Earth at high speeds. By decomposing complex network services into lightweight service functions, network function virtualization (NFV) transforms global network services into diverse service function chains (SFCs), coordinated by resource-constrained LEOs. However, the dynamic topology of satellite networks, marked by highly variable inter-satellite link delays, poses significant challenges for designing efficient routing strategies that ensure reliable and low-latency communication. Many existing routing methods suffer from poor scalability and degraded performance, limiting their practical implementation. To address these challenges, this paper proposes a novel SFC routing approach that leverages the statistical properties of network link states to mitigate instability caused by instantaneous modeling in dynamic satellite networks. Through comprehensive simulations on end-to-end shortest-path propagation delays in LEO networks, we identify and validate the statistical stability of multi-hop routes. Building on this insight, we introduce the Stability-Aware Multi-Stage Graph Routing (SA-MSGR) algorithm, which incorporates pre-computed average delays into a multi-stage graph optimization framework. Extensive simulations demonstrate the superior performance of SA-MSGR, achieving significantly lower and more predictable end-to-end SFC delays compared to representative baseline strategies.