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EAP: Energy-Awareness Predictor in Multicore CPU

Authors
Bui, Dinh-MaoHuynh-The, ThienYoon, YongIkJun, SungIkLee, Sungyoung
Issue Date
Dec-2015
Publisher
Springer Verlag
Keywords
Bayesian learning; CPU utilization; Energy efficiency; Gaussian process; Proactive prediction
Citation
Lecture Notes in Electrical Engineering, v.373, pp 361 - 366
Pages
6
Journal Title
Lecture Notes in Electrical Engineering
Volume
373
Start Page
361
End Page
366
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/10161
DOI
10.1007/978-981-10-0281-6_52
ISSN
1876-1100
Abstract
To deal with inference and reasoning problems, Gaussian process has been considered as a promising tool due to the robustness and flexibility features. Especially, solving the regression and classification, Gaussian process coupling with Bayesian learning is one of the most appropriate supervised learning approaches in terms of accuracy and tractability. Because of these features, it is reasonable to engage Gaussian process for energy saving purpose. In this paper, the research focuses on analyzing the capability of Gaussian process, implementing it to predict CPU utilization, which is used as a factor to predict the status of computing node. Subsequently, a migration mechanism is applied so as to migrate the system-level processes between CPU cores and turn off the idle ones in order to save the energy while still maintaining the performance.
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