Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique

Shuang Chen; Mahboobeh Ghorbani; Yanzhi Wang; Paul Bogdan; Massoud Pedram

doi:10.1109/BigData.Congress.2014.108

Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique

Shuang Chen, Mahboobeh Ghorbani, Yanzhi Wang, Paul Bogdan, Massoud Pedram

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
Editors	Peter Chen, Peter Chen, Hemant Jain
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	733-739
Number of pages	7
ISBN (Electronic)	9781479950577
DOIs	https://doi.org/10.1109/BigData.Congress.2014.108
State	Published - Sep 22 2014
Externally published	Yes
Event	3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States Duration: Jun 27 2014 → Jul 2 2014

Publication series

Name	Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

Other

Other	3rd IEEE International Congress on Big Data, BigData Congress 2014
Country/Territory	United States
City	Anchorage
Period	6/27/14 → 7/2/14

Keywords

Google cluster dataset
alpha-stable distribution
cloud computing
fractional order calculus

ASJC Scopus subject areas

Computer Science Applications

Access to Document

10.1109/BigData.Congress.2014.108

Cite this

Chen, S., Ghorbani, M., Wang, Y., Bogdan, P., & Pedram, M. (2014). Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique. In P. Chen, P. Chen, & H. Jain (Eds.), Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014 (pp. 733-739). [6906851] (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.Congress.2014.108

Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique. / Chen, Shuang; Ghorbani, Mahboobeh; Wang, Yanzhi et al.

Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. ed. / Peter Chen; Peter Chen; Hemant Jain. Institute of Electrical and Electronics Engineers Inc., 2014. p. 733-739 6906851 (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, S, Ghorbani, M, Wang, Y, Bogdan, P & Pedram, M 2014, Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique. in P Chen, P Chen & H Jain (eds), Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014., 6906851, Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014, Institute of Electrical and Electronics Engineers Inc., pp. 733-739, 3rd IEEE International Congress on Big Data, BigData Congress 2014, Anchorage, United States, 6/27/14. https://doi.org/10.1109/BigData.Congress.2014.108

Chen S, Ghorbani M, Wang Y, Bogdan P, Pedram M. Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique. In Chen P, Chen P, Jain H, editors, Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 733-739. 6906851. (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014). https://doi.org/10.1109/BigData.Congress.2014.108

Chen, Shuang ; Ghorbani, Mahboobeh ; Wang, Yanzhi et al. / Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique. Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. editor / Peter Chen ; Peter Chen ; Hemant Jain. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 733-739 (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014).

@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",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 3rd IEEE International Congress on Big Data, BigData Congress 2014 ; Conference date: 27-06-2014 Through 02-07-2014",

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",

}

TY - GEN

T1 - Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique

AU - Chen, Shuang

AU - Ghorbani, Mahboobeh

AU - Wang, Yanzhi

AU - Bogdan, Paul

AU - Pedram, Massoud

PY - 2014/9/22

Y1 - 2014/9/22

N2 - 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.

AB - 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.

KW - Google cluster dataset

KW - alpha-stable distribution

KW - cloud computing

KW - fractional order calculus

UR - http://www.scopus.com/inward/record.url?scp=84923920082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923920082&partnerID=8YFLogxK

U2 - 10.1109/BigData.Congress.2014.108

DO - 10.1109/BigData.Congress.2014.108

M3 - Conference contribution

AN - SCOPUS:84923920082

T3 - Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

SP - 733

EP - 739

BT - Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

A2 - Chen, Peter

A2 - Jain, Hemant

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Congress on Big Data, BigData Congress 2014

Y2 - 27 June 2014 through 2 July 2014

ER -

Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this