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Renewable Energy Prediction for Improved Utilization and Efficiency in Datacenters and Backbone Networks

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Computational Sustainability

Part of the book series: Studies in Computational Intelligence ((SCI,volume 645))

Abstract

Datacenters are one of the important global energy consumers and carbon producers. However, their tight service level requirements prevent easy integration with highly variable renewable energy sources. Short-term green energy prediction can mitigate this variability. In this work, we first explore the existing short-term solar and wind energy prediction methods, and then leverage prediction to allocate and migrate workloads across geographically distributed datacenters to reduce brown energy consumption costs. Unlike previous works, we also study the impact of wide area networks (WAN) on datacenters, and investigate the use of green energy prediction to power WANs. Finally, we present two different studies connecting datacenters and WANs: the case where datacenter operators own and manage their WAN and the case where datacenters lease networks from WAN providers. The results show that prediction enables up to 90 % green energy utilization, a 3\(\times \) improvement over the existing methods. The cost minimization algorithm reduces expenses by up to 16 % and increases performance by 27 % when migrating workloads across datacenters. Furthermore, the savings increase up to 30 % compared with no migration when servers are made energy-proportional. Finally, in the case of leasing the WAN, energy proportionality in routers can increase the profit of network providers by 1.6\(\times \).

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Authors and Affiliations

  1. University of California, San Diego, La Jolla, CA, USA

    Baris Aksanli, Jagannathan Venkatesh & Tajana Simunic Rosing

  2. Lawrence Berkeley National Laboratory, Energy Sciences Network, Berkeley, CA, USA

    Inder Monga

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  1. Baris Aksanli
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  2. Jagannathan Venkatesh
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  3. Inder Monga
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  4. Tajana Simunic Rosing
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Correspondence to Baris Aksanli .

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Editors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Zittau/Gö, Goerlitz, Germany

    Jörg Lässig

  2. Fraunhofer IAIS, University of Bonn, Sankt Augustin, Germany

    Kristian Kersting

  3. Department of Computer Science, TU Dortmund University, Dortmund, Germany

    Katharina Morik

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Aksanli, B., Venkatesh, J., Monga, I., Rosing, T.S. (2016). Renewable Energy Prediction for Improved Utilization and Efficiency in Datacenters and Backbone Networks. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-319-31858-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-31858-5_4

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