Please use this identifier to cite or link to this item:
https://doi.org/10.1007/978-3-642-38827-9_28
DC Field | Value | |
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dc.title | Estimating operating system process energy consumption in real time | |
dc.contributor.author | Dutta, K. | |
dc.contributor.author | Singh, V.K. | |
dc.contributor.author | VanderMeer, D. | |
dc.date.accessioned | 2016-05-17T10:45:19Z | |
dc.date.available | 2016-05-17T10:45:19Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Dutta, K.,Singh, V.K.,VanderMeer, D. (2013). Estimating operating system process energy consumption in real time. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7939 LNCS : 400-404. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-38827-9_28" target="_blank">https://doi.org/10.1007/978-3-642-38827-9_28</a> | |
dc.identifier.isbn | 9783642388262 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/124008 | |
dc.description.abstract | The power consumption in data centers due to Information and Communication Technologies (ICT) is significant across the globe. With recent developments in Service Oriented Architecture (SOA), we notice a paradigm shift in computing. Desktops (PCs) and laptops are being replaced by smart phones and tablets. A major impact of this architecture is a shift of computing resources from personal desktops and laptops to centralized server farms. This implies increases in power consumption in the large-scale servers used in these infrastructures. In such a scenario, optimizing the IT resources for power consumption is a necessity. The first step of such an optimization at the application level is the knowledge of how much energy the application is consuming. A major challenge in this domain is to develop a software-based energy metering tool that can measure the energy consumptions at the OS process level. We have developed an OS process-level power metering tool that can accurately estimate the energy usage based on system resource usages, and demonstrated that our tool provides energy measurement for complex e-business applications with greater than 95% accuracy. © 2013 Springer-Verlag. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-38827-9_28 | |
dc.source | Scopus | |
dc.subject | Energy | |
dc.subject | Operating Systems | |
dc.subject | Power Meter | |
dc.subject | SVM Model | |
dc.type | Conference Paper | |
dc.contributor.department | INFORMATION SYSTEMS | |
dc.description.doi | 10.1007/978-3-642-38827-9_28 | |
dc.description.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.description.volume | 7939 LNCS | |
dc.description.page | 400-404 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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