期刊
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
卷 74, 期 1, 页码 1820-1832出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2013.10.001
关键词
Hostload prediction; Bayesian model; Google data center
资金
- Google Research Award
- ANR project Clouds@home [ANR-09-JCJC-0056-01]
- Agence Nationale de la Recherche (ANR) [ANR-09-JCJC-0056] Funding Source: Agence Nationale de la Recherche (ANR)
We design a novel prediction method with Bayes model to predict a. load fluctuation pattern over a long-term interval, in the context of Google data centers. We exploit a set of features that capture the expectation, trend, stability and patterns of recent host loads. We also investigate the correlations among these features and explore the most effective combinations of features with various training periods. All of the prediction methods are evaluated using Google trace with 10,000+ heterogeneous hosts. Experiments show that our Bayes method improves the long-term load prediction accuracy by 5.6%-50%, compared to other state-of-the-art methods based on moving average, auto-regression, and/or noise filters. Mean squared error of pattern prediction with Bayes method can be approximately limited in [10(-8), 10(-5)]. Through a load balancing scenario, we confirm the precision of pattern prediction in finding a set of idlest/busiest hosts from among 10,000+ hosts can be improved by about 7% on average. (C) 2013 Elsevier Inc. All rights reserved.
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