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
N1 - Publisher Copyright:
© 2014 IEEE.
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 - 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 -