Energy-Efficient Cloud Computing
Status:: 🟩
Links:: Cloud Computing Sustainability
Metadata
Authors:: Berl, A.; Gelenbe, E.; Di Girolamo, M.; Giuliani, G.; De Meer, H.; Dang, M. Q.; Pentikousis, K.
Title:: Energy-Efficient Cloud Computing
Publication Title:: "The Computer Journal"
Date:: 2010
URL:: https://doi.org/10.1093/comjnl/bxp080
DOI:: 10.1093/comjnl/bxp080
Bibliography
Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M. Q., & Pentikousis, K. (2010). Energy-Efficient Cloud Computing. The Computer Journal, 53(7), 1045–1051. https://doi.org/10.1093/comjnl/bxp080
Zotero
Type:: #zotero/journalArticle
Zotero::
Keywords:: [⏳, Cloud Computing, Energy Efficiency, Virtualization, Resource Allocation]
Relations
Abstract
Energy efficiency is increasingly important for future information and communication technologies (ICT), because the increased usage of ICT, together with increasing energy costs and the need to reduce green house gas emissions call for energy-efficient technologies that decrease the overall energy consumption of computation, storage and communications. Cloud computing has recently received considerable attention, as a promising approach for delivering ICT services by improving the utilization of data centre resources. In principle, cloud computing can be an inherently energy-efficient technology for ICT provided that its potential for significant energy savings that have so far focused on hardware aspects, can be fully explored with respect to system operation and networking aspects. Thus this paper, in the context of cloud computing, reviews the usage of methods and technologies currently used for energy-efficient operation of computer hardware and network infrastructure. After surveying some of the current best practice and relevant literature in this area, this paper identifies some of the remaining key research challenges that arise when such energy-saving techniques are extended for use in cloud computing environments.
Notes & Annotations
📑 Annotations (imported on 2023-07-26#11:56:21)
Another approach that increases energy efficiency in data centres is based on server consolidation by service virtualization [21–25]. Virtualization partitions computational resources and allows the sharing of hardware. Many services often need only a small fraction of the available computational resources [26] of a data centre server. However, even when run at a low utilization, servers typically need up to 70% of their maximum power consumption [27]. Such services can be virtualized and run within a virtual machine (VM) resulting in significant increases in overall energy efficiency. Depending on their utilization, many VMs can run on a single hardware unit (server consolidation). Therefore, less hardware is needed overall, thus reducing energy wasted for cooling, while the deployed hardware utilization increases. This consolidation of shared hardware fosters energy efficiency, measured as work accomplished per unit of consumed energy [28].
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[22] IBM Project Big Green, http://www-03.ibm.com/press/us/ en/presskit/21440.wss. (Last accessed August 12, 2009).
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[26] IBM (2007) Virtualization can help power efficiency. http:// www.03.ibm.com/systems/virtualization/view/011607.html. (Last accessed August 12, 2009).
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In contrast to hardware-oriented optimization, software systems can potentially be optimized at development time by specifying their energy characteristics and by adapting the implementation.
The key current technology for energy-efficient operation of servers in data centres is virtualization. VMs that encapsulate virtualized services can be moved, copied, created and deleted depending on management decisions. Consolidating hardware and reducing redundancy can achieve energy efficiency. Unused servers can be turned off (or hibernated) to save energy. Some hardware gets higher load, which reduces the number of physical servers needed.
Research has shown that communications, in particular, is one of the largest consumers of energy, however, energy optimization for communications must deal with the trade-offs between performance, energy savings and QoS [51].