Campus Units
Statistics, Center for Survey Statistics and Methodology (CSSM)
Document Type
Conference Proceeding
Conference
IEEE Globecom 2016
Publication Version
Accepted Manuscript
Link to Published Version
https://doi.org/10.1109/GLOCOMW.2016.7849022
Publication Date
2016
Journal or Book Title
2016 IEEE Globecom Workshops (GC Wkshps)
DOI
10.1109/GLOCOMW.2016.7849022
Conference Title
IEEE Globecom 2016
Conference Date
December 4-8, 2016
City
Washington, DC
Abstract
Automatic resource scaling is one advantage of cloud systems. Cloud systems are able to scale the number of physical machines depending on user requests. Therefore, accurate request prediction brings a great improvement in cloud systems' performance. If we can make accurate requests prediction, the appropriate number of physical machines that can accommodate predicted amount of requests can be activated and cloud systems will save more energy by preventing excessive activation of physical machines. Also, cloud systems can implement advanced load distribution with accurate requests prediction. We propose a prediction model that predicts probability distribution parameters of requests for each time interval. Maximum Likelihood Estimation (MLE) and Local Linear Regression (LLR) are used to implement this algorithm. An evaluation of the proposed algorithm is performed with the Google cluster-trace data. The prediction is achieved in terms of the number of task arrivals, CPU requests, and memory resource requests. Then the accuracy of prediction is measured with Mean Absolute Percentage Error(MAPE) and Normalized Mean Squared Error (NMSE).
Rights
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
Copyright Owner
IEEE
Copyright Date
2016
Language
en
File Format
application/pdf
Recommended Citation
Yoon, Min Sang; Kamal, Ahmed E.; and Zhu, Zhengyuan, "Requests Prediction in Cloud with a Cyclic Window Learning Algorithm" (2016). Statistics Conference Proceedings, Presentations and Posters. 11.
https://lib.dr.iastate.edu/stat_las_conf/11
Comments
This is a manuscript of a proceeding published as Yoon, Min Sang, Ahmed E. Kamal, and Zhengyuan Zhu. "Requests prediction in cloud with a cyclic window learning algorithm." In 2016 IEEE Globecom Workshops (GC Wkshps), (2016). DOI: 10.1109/GLOCOMW.2016.7849022. Posted with permission.