期刊
APPLIED INTELLIGENCE
卷 43, 期 4, 页码 874-891出版社
SPRINGER
DOI: 10.1007/s10489-015-0688-4
关键词
Proactive prediction; Bayesian learning; Gaussian process; Parallel computing; Energy efficiency; CPU utilization
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2014R1A2A2A01003914]
- IT R&D program of MSIP/IITP [14-000-09-001]
- Ministry of Public Safety & Security (MPSS), Republic of Korea [R0101-15-237] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2014R1A2A2A01003914] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据