Cloud computing is a model of Internet-based computing. High availability is one of the key characteristics of Infrastructure-as-a-Service (IaaS) cloud. A scalable method for availability analysis of large scale IaaS cloud using analytic models. Stochastic analytic models can be utilized for cloud service availability analysis. Monolithic models become intractable as the size of cloud increases. The data are stored in the cloud using IaaS (Infrastructure as a Service). There various IaaS available for storing the data for example Amazon, IBM smart Cloud etc. Maintaining the availability in that IaaS plays a difficult task. The process of maintaining the availability in the cloud a new scalable method is introduced. The scalable method reduces the complexity and the solution time by making use of the Markov Chain process. Markov chain is facilitated by the use of high level Petrinet based paradigm known as stochastic reward net (SRN). Overall solution is composed by iteration over individual SRN sub-model solutions. Dependencies among the sub-models are resolved using fixed-point iteration, for which existence of a solution is proved. The solution obtained from the interacting sub-models with a monolithic model and show that errors introduced by decomposition are insignificant.