DocumentCode
2236961
Title
Hourly server workload forecasting up to 168 hours ahead using Seasonal ARIMA model
Author
Tran, Van Giang ; Debusschere, Vincent ; Bacha, Seddik
Author_Institution
G2Elab - Grenoble Electr. Eng. Lab., INP - France, France
fYear
2012
fDate
19-21 March 2012
Firstpage
1127
Lastpage
1131
Abstract
Data center workload prediction is important to take decisions in resources management system. Seasonal ARIMA model provide a good server workload methodology for the server workload forecasting. A large set of our experiments confirm that it has high performance, scalability and reliability and will bee integrated in our system. This paper presents a general expression in development of our forecast model in the project EnergeTic-FUI, France.
Keywords
autoregressive moving average processes; computer centres; energy management systems; file servers; EnergeTic-FUI; data center workload prediction; forecast model; hourly server workload forecasting; resources management system; seasonal ARIMA model; server workload methodology; Atmospheric modeling; Biological system modeling; Knowledge engineering; Predictive models; Reliability engineering; Support vector machines; EnergeTIC-FUI; Seasonal ARIMA; data center workload forecasting; server workload;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Technology (ICIT), 2012 IEEE International Conference on
Conference_Location
Athens
Print_ISBN
978-1-4673-0340-8
Type
conf
DOI
10.1109/ICIT.2012.6210091
Filename
6210091
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