@inproceedings{b003cdfd50154271b9027a52cd5aec83,
title = "Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique",
abstract = "The problem of big data analytics is gaining increasing research interest because of the rapid growth in the volume of data to be analyzed in various areas of science and technology. In this paper, we investigate the characteristics of the cloud computing requests received by the cloud infrastructure operators. The cluster usage dataset released by Google is thoroughly studied. To address the self-similarity and non-stationarity characteristics of the workload profile in a cloud computing system, fractal modeling techniques similar to some cyber-physical system (CPS) applications are exploited. A trace-based prediction of the job inter-arrival time and aggregated resource request sent to server cluster in the near future is effectively performed by solving fractional-order differential equations. The distributions of important parameters including job/task duration time and resource request per task in terms of CPU, memory, and storage are extracted from the cluster dataset are fitted using the alpha-stable distribution.",
keywords = "Google cluster dataset, alpha-stable distribution, cloud computing, fractional order calculus",
author = "Shuang Chen and Mahboobeh Ghorbani and Yanzhi Wang and Paul Bogdan and Massoud Pedram",
year = "2014",
month = sep,
day = "22",
doi = "10.1109/BigData.Congress.2014.108",
language = "English (US)",
series = "Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "733--739",
editor = "Peter Chen and Peter Chen and Hemant Jain",
booktitle = "Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014",
note = "3rd IEEE International Congress on Big Data, BigData Congress 2014 ; Conference date: 27-06-2014 Through 02-07-2014",
}