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Gaussian process for predicting CPU utilization and its application to energy efficiency

Citation

Bui, D-M and Nguyen, H-Q and Yoon, Y and Jun, S and Amin, MB and Lee, S, Gaussian process for predicting CPU utilization and its application to energy efficiency, Applied Intelligence, 43, (4) pp. 874-891. ISSN 1573-7497 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 Springer Science+Business Media New York

DOI: doi:10.1007/s10489-015-0688-4

Abstract

For the past ten years, Gaussian process has become increasingly popular for modeling numerous inferences and reasoning solutions due to the robustness and dynamic features. Particularly concerning regression and classification data, the combination of Gaussian process and Bayesian learning is considered to be one of the most appropriate supervised learning approaches in terms of accuracy and tractability. However, due to the high complexity in computation and data storage, Gaussian process performs poorly when processing large input dataset. Because of the limitation, this method is ill-equipped to deal with the large-scale system that requires reasonable precision and fast reaction rate. To improve the drawback, our research focuses on a comprehensive analysis of Gaussian process performance issues, highlighting ways to drastically reduce the complexity of hyper-parameter learning and training phases, which could be applicable in predicting the CPU utilization in the demonstrated application. In fact, the purpose of this application is to save the energy by distributively engaging the Gaussian process regression to monitor and predict the status of each computing node. Subsequently, a migration mechanism is applied to migrate the system-level processes between multi-core and turn off the idle one in order to reduce the power consumption while still maintaining the overall performance.

Item Details

Item Type:Refereed Article
Keywords:proactive prediction, Bayesian learning, Gaussian process, parallel computing, energy efficiency, CPU utilization
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed systems and algorithms
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Applied computing
UTAS Author:Amin, MB (Dr Muhammad Bilal Amin)
ID Code:143690
Year Published:2015
Web of Science® Times Cited:10
Deposited By:Information and Communication Technology
Deposited On:2021-03-30
Last Modified:2021-05-12
Downloads:0

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