Host Load Prediction Based on PSR and EA-GMDH for Cloud Computing System

Host Load Prediction is one of the most effective measures to improve resource utilization in the Cloud systems. As the drastic fluctuation of the host load in the Cloud, accurate prediction of host load is still a challenge. In this paper, we propose a new prediction method which combines the Phase...

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Bibliographic Details
Published in2013 International Conference on Cloud and Green Computing pp. 9 - 15
Main Authors Qiangpeng Yang, Chenglei Peng, Yao Yu, He Zhao, Yu Zhou, Ziqiang Wang, Sidan Du
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Evolutionary Algorithm
Google
Group Method of Data Handling
Host Load Prediction
Input variables
Load modeling
Neurons
Phase Space Reconstruction
Polynomials
Predictive models
Time series analysis
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Summary:Host Load Prediction is one of the most effective measures to improve resource utilization in the Cloud systems. As the drastic fluctuation of the host load in the Cloud, accurate prediction of host load is still a challenge. In this paper, we propose a new prediction method which combines the Phase Space Reconstruction (PSR) method and the Group Method of Data Handling (GMDH) based on Evolutionary Algorithm (EA). Our proposed method could predict not only the mean load in consecutive future time intervals, but also the actual load in each consecutive future time interval. We evaluate our method using the host load trace in the Google data center with thousands of machines. According to the experiment results, our method outperforms the other methods by more than 60% in mean load prediction, and preforms well on actual load prediction over different time intervals, i.e. 0.5h to 3h.
DOI:10.1109/CGC.2013.10