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

2026-03-04

AI Summary

This study addresses the challenges of high latency variability and limited scalability in service function chain (SFC) routing caused by the dynamic topology of low Earth orbit (LEO) satellite networks. The work reveals, for the first time, that end-to-end propagation delay over multi-hop paths exhibits statistical stability despite rapid topological changes. Leveraging this insight, the authors propose a stability-aware multi-stage graph routing mechanism (SA-MSGR), which integrates network function virtualization (NFV), statistical delay modeling, and precomputed average delays. By formulating SFC routing as a multi-stage graph optimization problem, SA-MSGR avoids the instability inherent in instantaneous state modeling. Extensive simulations demonstrate that SA-MSGR significantly reduces end-to-end SFC latency, enhances delay predictability, and outperforms representative state-of-the-art baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Service Function Chain
• LEO satellite networks
• dynamic topology
• inter-satellite link delay
• routing

Innovation

• Service Function Chain
• LEO satellite networks
• statistical stability
• delay-aware routing
• multi-stage graph optimization