Using Parametric Models to Represent Private Cloud Workloads

Cloud computing has become a popular metaphor for dynamic and secure self-service access to computational and storage capabilities. In this study, we analyze and model workloads gathered from enterprise-operated commercial private clouds that implement “Infrastructure as a Service.” Our results show that 3-phase hyperexponential distributions fit using the Estimation Maximization (E-M) algorithm capture workload attributes accurately. In addition, these models of individual attributes compose to produce estimates of overall cloud performance that our results verify to be accurate. As an early study of commercial enterprise private clouds, this work provides guidance to those researching, designing, or maintaining such installations. In particular, the cloud workloads under study do not exhibit “heavy-tailed” distributional properties in the same way that “bare metal” operating systems do, potentially leading to different design and engineering tradeoffs.