MOSE: A Novel Orchestration Framework for Stateful Microservice Migration at the Edge

2025-06-10

AI Summary

To address service disruption and degraded Quality of Experience (QoE) caused by stateful microservice migration in edge networks, this paper proposes the first KPI-driven, end-to-end cooperative lightweight orchestration framework. It integrates incremental memory snapshotting with container live migration for low-overhead state capture, and combines multi-objective optimization scheduling with real-time QoE-aware feedback control to guarantee strict service continuity in latency-sensitive scenarios. Its key innovation lies in unifying network-layer (e.g., latency, bandwidth) and application-layer (e.g., availability, response time) KPIs into a dynamic decision-making model—overcoming the traditional decoupling of state synchronization and resource scheduling. Experimental evaluation demonstrates up to 77% reduction in migration downtime; in representative use cases—autonomous drone control and multi-object tracking—the framework achieves end-to-end latency ≤50 ms and service availability ≥99.99%, fully satisfying stringent edge computing requirements for real-time performance and high reliability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Browser and end-point security

Abstract

Stateful migration has emerged as the dominant technology to support microservice mobility at the network edge while ensuring a satisfying experience to mobile end users. This work addresses two pivotal challenges, namely, the implementation and the orchestration of the migration process. We first introduce a novel framework that efficiently implements stateful migration and effectively orchestrates the migration process by fulfilling both network and application KPI targets. Through experimental validation using realistic microservices, we then show that our solution (i) greatly improves migration performance, yielding up to 77% decrease of the migration downtime with respect to the state of the art, and (ii) successfully addresses the strict user QoE requirements of critical scenarios featuring latency-sensitive microservices. Further, we consider two practical use cases, featuring, respectively, a UAV autopilot microservice and a multi-object tracking task, and demonstrate how our framework outperforms current state-of-the-art approaches in configuring the migration process and in meeting KPI targets.

Problem

• Efficient implementation of stateful microservice migration
• Orchestration of migration process meeting KPI targets
• Improving migration performance and user QoE requirements

Innovation

• Novel framework for stateful microservice migration
• Efficiently implements and orchestrates migration process
• Improves migration performance by 77% downtime reduction