End-to-end performability analysis for Infrastructure-as-a-Service cloud: An interacting stochastic models approach
Handling diverse client demands and managing unexpected failures without degrading performance are two key promises of a cloud delivered service. However, evaluation of a cloud service quality becomes difficult as the scale and complexity of a cloud system increases. In a cloud environment, service request from a user goes through a variety of provider specific processing steps from the instant it is submitted until the service is fully delivered. Measurement-based evaluation of cloud service quality is expensive especially if many configurations, workload scenarios, and management methods are to be analyzed. To overcome these difficulties, in this paper we propose a general analytic model based approach for an end-to-end performability analysis of a cloud service. We illustrate our approach using Infrastructure-as-a-Service (IaaS) cloud, where service availability and provisioning response delays are two key QoS metrics. A novelty of our approach is in reducing the complexity of analysis by dividing the overall model into submodels and then obtaining the overall solution by iteration over individual sub-model solutions. In contrast to a single one-level monolithic model, our approach yields a high fidelity model that is tractable and scalable. Our approach and underlying models can be readily extended to other types of cloud services and are applicable to public, private and hybrid clouds. © 2010 IEEE.
