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Communication Dans Un Congrès Année : 2022

Energy-efficient resource allocation in multi-tenant edge computing using Markov decision processes

Résumé

We address the problem of a Network Operator (NO) owning limited resources at the network edge. The NO wishes to enable advanced services, by virtualizing and allocating such resources among multiple tenants, i.e., third-party Service Providers, co-existing at the edge, with different Quality of Service (QoS) constraints. The NO applies a resource allocation policy with the objective of minimizing energy consumption via switching off non-used resources while guaranteeing tenants QoS requirements. We propose a resource allocation policy based on Markov Decision Processes (MDP). In simulation we show that our policy is able to reduce energy consumption, by turning off unused resources, while meeting heterogeneous SP requirements. Our code is available as open source.
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Dates et versions

hal-03524815 , version 1 (18-01-2022)

Identifiants

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Alessandro Spallina, Andrea Araldo, Tijani Chahed, Hind Castel-Taleb, Antonella Di Stefano, et al.. Energy-efficient resource allocation in multi-tenant edge computing using Markov decision processes. NOMS 2022: IEEE/IFIP Network Operations and Management Symposium, Apr 2022, Budapest, Hungary. pp.1-5, ⟨10.1109/NOMS54207.2022.9789942⟩. ⟨hal-03524815⟩
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