The document presents a new hierarchical correlation model for evaluating reliability, performance, and power consumption of cloud services. The model uses Markov models, queuing theory, and Bayesian approaches to analyze how these metrics are correlated. It accounts for characteristics of cloud systems like virtual machines hosted on servers, common cause failures from server issues, and multicore CPU mapping. The model also develops a tradeoff parameter and profit optimization approach to evaluate performance versus power consumption tradeoffs.

1. A Hierarchical Correlation Model for Evaluating Reliability, Performance,
and Power Consumption of a Cloud Service
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.