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Estimating Energy Consumption of Cloud, Fog and Edge Computing Infrastructures

Ehsan Ahvar 1 Anne-Cécile Orgerie 1 Adrien Lebre 2, 3
1 MYRIADS - Design and Implementation of Autonomous Distributed Systems
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
2 STACK - Software Stack for Massively Geo-Distributed Infrastructures
Inria Rennes – Bretagne Atlantique , LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : In order to improve locality aspects, new Cloud-related architectures such as Edge Computing have been proposed. Despite the growing popularity of these new architectures, their energy consumption has not been well investigated yet. To move forward on such a critical question, we first introduce a taxonomy of different Cloud-related architectures. From this taxonomy, we then present an energy model to evaluate their consumption. Unlike previous proposals, our model comprises the full energy consumption of the computing facilities, including cooling systems, and the energy consumption of network devices linking end users to Cloud resources. Finally, we instantiate our model on different Cloud-related architectures, ranging from fully centralized to completely distributed ones, and compare their energy consumption. The results show that a completely distributed architecture, because of not using intra-data center network and large-size cooling systems, consumes between 14% and 25% less energy than fully centralized and partly distributed architectures respectively. To the best of our knowledge, our work is the first one to propose a model that enables researchers to analyze and compare energy consumption of different Cloud-related architectures.
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Submitted on : Thursday, March 28, 2019 - 4:49:04 PM
Last modification on : Friday, January 21, 2022 - 3:20:20 AM
Long-term archiving on: : Saturday, June 29, 2019 - 2:47:55 PM


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Ehsan Ahvar, Anne-Cécile Orgerie, Adrien Lebre. Estimating Energy Consumption of Cloud, Fog and Edge Computing Infrastructures. IEEE Transactions on Sustainable Computing, IEEE, 2019, pp.1-12. ⟨10.1109/TSUSC.2019.2905900⟩. ⟨hal-02083080⟩



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