Reference : Service Performance Pattern Analysis and Prediction of Commercially Available Cloud P...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/29527
Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers
English
Wagle, Shyam Sharan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Guzek, Mateusz mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
In press
Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers
8
Yes
International
8th IEEE International Conference on Cloud Computing Technology and Science (Cloudcom2017)
12-12-2016
IEEE
Luxembourg City
Luxembourg
[en] Cloud providers ; Pattern Analysis ; Performance Prediction ; Data Analytics
[en] The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible, but prolonged observations provide more reliable information. However, due to technical complexities and costs of monitoring services, it may not be possible to access the service performance of cloud provider for longer time durations. The extended observation periods are also a necessity for prediction of future behavior of services. These predictions have very high value for decision making both for private and corporate cloud users, as the uncertainty about the future performance of purchased cloud services is an important risk factor. Predictions can be used by specialized entities, such as cloud service brokers (CSBs) to optimally recommend cloud services to the cloud users. In this paper, we address the challenge of prediction. To achieve this, the current service performance patterns of cloud providers are analyzed and future performance of cloud providers are predicted using to the observed service performance data. It is done using two automatic predicting approaches: ARIMA and ETS. Error measures of entire service performance prediction of cloud providers are evaluated against the actual performance of the cloud providers computed over a period of one month. Results obtained in the performance prediction show that the methodology is applicable for both short- term and long-term performance prediction.
National Research Fund (FNR) IShOP (POLLUX/13/IS/6466384) project
http://hdl.handle.net/10993/29527

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