Availability Analysis of IaaS Cloud Using Analytic Models

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. In this chapter, we introduce the reader with a novel approach using interacting analytic sub-models and we show how such approach can deal with large scale Cloud availability analysis. We put our 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.