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

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Titel: MOSE: A Novel Orchestration Framework for Stateful Microservice Migration at the Edge
Autoren: Calagna, Antonio, Yu, Yenchia, Giaccone, Paolo, Chiasserini, Carla Fabiana, 1970
Quelle: IEEE Transactions on Network and Service Management. 22(5):4827-4841
Schlagwörter: computer vision, machine learning, service migration, mobile networks, Edge computing
Beschreibung: 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.
Dateibeschreibung: electronic
Zugangs-URL: https://research.chalmers.se/publication/548731
https://research.chalmers.se/publication/548731/file/548731_Fulltext.pdf
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  Data: MOSE: A Novel Orchestration Framework for Stateful Microservice Migration at the Edge
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Calagna%2C+Antonio%22">Calagna, Antonio</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Yenchia%22">Yu, Yenchia</searchLink><br /><searchLink fieldCode="AR" term="%22Giaccone%2C+Paolo%22">Giaccone, Paolo</searchLink><br /><searchLink fieldCode="AR" term="%22Chiasserini%2C+Carla+Fabiana%22">Chiasserini, Carla Fabiana</searchLink>, 1970
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  Data: <i>IEEE Transactions on Network and Service Management</i>. 22(5):4827-4841
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  Data: <searchLink fieldCode="DE" term="%22computer+vision%22">computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22service+migration%22">service migration</searchLink><br /><searchLink fieldCode="DE" term="%22mobile+networks%22">mobile networks</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink>
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  Data: 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.
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      – SubjectFull: service migration
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              Y: 2025
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