Stochastic Modeling and Performance Analysis of Migration-Enabled and Error-Prone Clouds

Cloud computing is a promising paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Virtualization allows to instantiate virtual machines (VMs) on top of fewer physical systems managed by a VM manager. Performance evaluation of clouds is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quality of service (QoS) experienced by end-users. Such evaluation is not feasible by means of simulation or on-the-field measurement, due to the great scale of parameter spaces that have to be traversed. In this study, we present a stochastic-queuing-network-based approach to performance analysis of migration-enabled clouds in error-prone environment. Several performance metrics are defined and evaluated: utilization, expected task completion time, and task rejection rate under different load conditions and error intensities. To validate the proposed approach, we obtain experimental performance data through a real-world cloud and conduct a confidence-interval analysis. The analysis results suggest the perfect coverage of theoretical performance results by corresponding experimental confidence intervals.