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Availability Analysis of IaaS Cloud Using Analytic Models
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Author(s): Francesco Longo (Università degli Studi di Messina, Italy), Rahul Ghosh (Duke University, USA), Vijay K. Naik (IBM T. J. Watson Research Center, USA)and Kishor S. Trivedi (Duke University, USA)
Copyright: 2012
Pages: 24
Source title:
Achieving Federated and Self-Manageable Cloud Infrastructures: Theory and Practice
Source Author(s)/Editor(s): Massimo Villari (Università degli Studi di Messina, Italy), Ivona Brandic (Vienna University of Technology, Austria)and Francesco Tusa (Università degli Studi di Messina, Italy)
DOI: 10.4018/978-1-4666-1631-8.ch008
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Abstract
Cloud based systems are inherently large scale. Failures in such a large distributed environment are quite common phenomena. To reduce the overall Cloud downtime and to provide a seamless service, providers need to assess the availability characteristics of their data centers. Such assessments can be done through controlled experimentations, large scale simulations and via analytic models. In the scale of Cloud, conducting repetitive experimentations or simulations might be costly and time consuming. Analytic models, on the other hand, can be used as a complement to small scale measurements and simulations since the analytic results can be obtained quickly. However, accurate analytic modeling requires dealing with large number of system states, leading to state-space explosion problem. To reduce the complexity of analysis, novel analytic methods are required. This chapter introduces the reader to a novel approach using interacting analytic sub-models and shows how such approach can deal with large scale Cloud availability analysis. The chapter puts the work in perspective of other existing and ongoing research in this area, describe how such approach can be useful to Cloud providers, especially in the case of federated scenarios, and summarize the open research questions that are yet to be solved.
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