Reference : Service Performance Pattern Analysis and Prediction of Commercially Available Cloud P...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/29527

Title :	Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers
Language :	English
Author, co-author :	Wagle, Shyam Sharan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Guzek, Mateusz [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	12-Dec-2016
Main document title :	Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers
Pages :	8
Peer reviewed :	Yes
Audience :	International
Event name :	8th IEEE International Conference on Cloud Computing Technology and Science (Cloudcom2017)
Event date :	12-12-2016
Event organizer :	IEEE
Event place (city) :	Luxembourg City
Event country :	Luxembourg
Keywords :	[en] Cloud providers ; Pattern Analysis ; Performance Prediction ; Data Analytics
Abstract :	[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.
Funders :	National Research Fund (FNR) IShOP (POLLUX/13/IS/6466384) project
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/29527

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access Ccom2016.pdf Author postprint 1.39 MB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map