Abstract
Cloud computing is a new emerging technology aimed at large-scale resource sharing and service-oriented computing. To achieve the efficient use of cloud resources for supporting a cloud service, many important factors need to be considered, particularly, reliability, performance, and power consumption of the cloud service. Evaluation of these metrics is essential for further designing rational resource scheduling strategies. However, these metrics are closely related; they do affect one another. The cloud system should consider correlations among the metrics to make more precise evaluation. Most of the existing approaches and models handle these metrics separately, and thus they cannot be used to study the correlations. This paper presents a new hierarchical correlation model for analyzing and evaluating these correlated metrics, which encompasses Markov models, queuing theory, and a Bayesian approach. Various distinctive characteristics of the cloud system are investigated and captured in the model, such as multiple virtual machines (VMs) hosted on the same server, common cause failures of co-located VMs caused by server failures, and logical mapping mechanisms for multicore CPUs. Moreover, for evaluating and balancing the tradeoff between performance and power consumption, a tradeoff parameter and a pure profit optimization model are developed based on the presented correlation model. Numerical examples are provided.