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Communication Dans Un Congrès Année : 2021

Adaptive IoT service configuration optimization in edge networks

Résumé

The collaboration of Internet of Things (IoT) devices promotes the computation at the network edge to satisfy latency-sensitive requests. The functionalities provided by IoT devices are encapsulated as IoT services, and the satisfaction of requests is reduced to the composition of services. Due to the hard-to-prediction of forthcoming requests, an adaptive service configuration is essential, when latency constraints are satisfied by composed services. This problem is formulated as a continuous time Markov decision process model constructed with updating system states, taking actions and assessing rewards constantly. A temporal-difference learning approach is developed to optimize the configuration, while taking long-term service latency and energy efficiency into consideration. Experimental results show that our approach outperforms the state-of-art's techniques for achieving close-to-optimal service configurations.

Domaines

Informatique
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Dates et versions

hal-03520023 , version 1 (10-01-2022)

Identifiants

Citer

Mengyu Sun, Zhangbing Zhou, Walid Gaaloul. Adaptive IoT service configuration optimization in edge networks. INFOCOM 2021: IEEE Conference on Computer Communications Workshops, May 2021, Vancouver (online), Canada. pp.1-2, ⟨10.1109/INFOCOMWKSHPS51825.2021.9484465⟩. ⟨hal-03520023⟩
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