To support latency sensitive microservices at the edge, stateful container migration has gathered momentum as a key solution to ensure a satisfying experience to mobile users. In this paper, we first investigate experimentally the stateful migration process, by using state-of-the-art tools, namely, Podman and CRIU. We then characterize the main migration KPIs, i.e., migration duration and downtime, and develop an analytical model that can effectively assess whether stateful migration is feasible while meeting the user’s QoE requirements. Importantly, our model is validated using real-world microservices and, by accounting for all relevant real-world aspects of stateful migration, significantly outperforms state-of-the-art models.

Processing-aware Migration Model for Stateful Edge Microservices / Calagna, A.; Yu, Y.; Giaccone, P.; Chiasserini, C. F.. - STAMPA. - (2023). (Intervento presentato al convegno IEEE ICC 2023 tenutosi a Rome (Italy) nel 28 May 2023 - 01 June 2023) [10.1109/ICC45041.2023.10278877].

Processing-aware Migration Model for Stateful Edge Microservices

A. Calagna;Y. Yu;P. Giaccone;C. F. Chiasserini
2023

Abstract

To support latency sensitive microservices at the edge, stateful container migration has gathered momentum as a key solution to ensure a satisfying experience to mobile users. In this paper, we first investigate experimentally the stateful migration process, by using state-of-the-art tools, namely, Podman and CRIU. We then characterize the main migration KPIs, i.e., migration duration and downtime, and develop an analytical model that can effectively assess whether stateful migration is feasible while meeting the user’s QoE requirements. Importantly, our model is validated using real-world microservices and, by accounting for all relevant real-world aspects of stateful migration, significantly outperforms state-of-the-art models.
2023
978-1-5386-7462-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2974824