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Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

Abstract : The growing appetite of new technologies, such as Internet-of-Things, for Cloud resources leads to an unprecedented energy consumption for these infrastructures. In order to make these energy-hungry distributed systems more sustainable, Cloud providers resort more and more to on-site renewable energy production facilities like photovoltaic panels. Yet, this intermittent and variable electricity production is often uncor-related with the Cloud consumption induced by its workload. Geographical load balancing, virtual machine (VM) migration and consolidation can be used to exploit multiple Cloud data centers' locations and their associated photovoltaic panels for increasing their renewable energy consumption. However, these techniques cost energy and network bandwidth, and this limits their utilization. In this paper, we propose to rely on the flexibility brought by Smart Grids to exchange renewable energy between distributed sites and thus, to further increase the overall Cloud's self-consumption of the locally-produced renewable energy. Our solution is named SCORPIUS: Self-Consumption Optimization of Renewable energy Production In distribUted cloudS. It takes into account telecommunication network constraints and electrical grid requirements to optimize the Cloud's self-consumption by trading-off between VM migration and renewable energy exchange. Our simulation-based results show that SCORPIUS outperforms existing solutions on various workload traces of production Clouds in terms of both renewable self-consumption and overall energy consumption.
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https://hal.archives-ouvertes.fr/hal-01856660
Contributor : Anne-Cécile Orgerie <>
Submitted on : Monday, August 13, 2018 - 11:18:29 AM
Last modification on : Tuesday, October 6, 2020 - 4:20:05 PM
Long-term archiving on: : Wednesday, November 14, 2018 - 2:15:56 PM

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  • HAL Id : hal-01856660, version 1

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Benjamin Camus, Anne Blavette, Fanny Dufossé, Anne-Cécile Orgerie. Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds. Cluster 2018 - IEEE International Conference on Cluster Computing, Sep 2018, Belfast, United Kingdom. pp.1-11. ⟨hal-01856660⟩

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