Performability analysis of a cloud system

Abstract

Cloud computing has recently emerged as an important filed with numerous novel features, particularly, large-scale resource integration and virtualized resource provisioning. Since a cloud system essentially aims at service-oriented computing, service performance becomes the primary metric that needs analyzing in detail. However, in a realistic scenario, operation of virtual machines (VM) may be interrupted by random resource failures. This demonstrates that service performance is indeed affected by resource reliability. Thus, connecting performance and reliability is essential for making more precise evaluation. In this paper, we present a theoretical modeling approach for performability analysis of cloud services and the cloud system. This flexible modeling approach first builds two tractable submodels that consider an important correlation factor (i.e., available resource capacity that is not only decided by reliability but also has a significant effect on performance) to ensure the required fidelity. Then, a Bayesian method is applied to connect the submodels, which can make our performability model more scalable. In contrast to a monolithic modeling method, our approach that combines interacting submodels can effectively reduce computing complexity for a large-scale cloud system. Numerical examples are illustrated.