The Energy Efficiency Potential of Cloud-Based Software: A U.S. Case Study

TitleThe Energy Efficiency Potential of Cloud-Based Software: A U.S. Case Study
Publication TypeReport
LBNL Report NumberLBNL-6298E
Year of Publication2013
AuthorsMasanet, Eric R., Jiaqi Liang, XiaoHui Ma, Benjamin Walker, Arman Shehabi, Lavanya Ramakrishnan, Valerie Hendrix, and Pradeep Mantha
Date Published06/2013
Abstract

The energy use of data centers is a topic th at has received much attention, given that data centers currently account for 1–2% of global electricity use. However, cloud computing holds great potential to reduce data center energy demand moving forward, due to both large reductions in total servers through consolidation and large increases in facility efficiencies compared to traditional local data centers. However, analyzing the net energy implications of shifts to the cloud can be very difficult, because data center services can affect many different components of society’s economic and energy systems. This report summarizes research by Lawrence Berkeley National Laboratory and Northwestern University to address this net energy analysis challenge in two important ways:

  1. We developed a comprehensive yet user friendly open-access model for assessing the net energy and emissions implications of cloud services in different regions and at different levels of market adoption. The model—named the Cloud Energy and Emissions Research (CLEER) Model—aims to provide full transparency on calculations and input value assumptions so that its results can be replicated and its data and methods can be easily refined and improved by the global research community. The CLEER Model has been made freely available online.
  2. We applied the CLEER Model in a case study to assess the technical potential of cloud-based business software for reducing energy use and greenhouse gas emissions in the United States. We focused on three common business applications—email, productivity software, and customer relationship management (CRM) software—which are currently used by tens of millions of U.S. workers.
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