MOSE: A Novel Orchestration Framework for Stateful Microservice Migration at the Edge
Artikel i vetenskaplig tidskrift, 2025

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 AAV 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.

computer vision

machine learning

service migration

mobile networks

Edge computing

Författare

Antonio Calagna

Politecnico di Torino

Yenchia Yu

Politecnico di Torino

Paolo Giaccone

Politecnico di Torino

Carla Fabiana Chiasserini

Politecnico di Torino

Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)

Chalmers, Data- och informationsteknik, Dator- och nätverkssystem

Göteborgs universitet

IEEE Transactions on Network and Service Management

19324537 (eISSN)

Vol. 22 5 4827-4841

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

Signalbehandling

DOI

10.1109/TNSM.2025.3579051

Mer information

Senast uppdaterat

2025-10-20