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Statistics, Center for Survey Statistics and Methodology (CSSM)

Document Type

Conference Proceeding


IEEE Globecom 2016

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Accepted Manuscript

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Journal or Book Title

2016 IEEE Globecom Workshops (GC Wkshps)



Conference Title

IEEE Globecom 2016

Conference Date

December 4-8, 2016


Washington, DC


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).


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.


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