Characterizing Private Clouds: A Large-Scale Empirical Analysis of Enterprise Clusters

Abstract

There is an increasing trend in the use of on-premise clusters within companies. Security, regulatory constraints, and enhanced service quality push organizations to work in these so called private cloud environments. On the other hand, the deployment of private enterprise clusters requires careful consideration of what will be necessary or may happen in the future, both in terms of compute demands and failures, as they lack the public cloud's flexibility to immediately provision new nodes in case of demand spikes or node failures. In order to better understand the challenges and tradeoffs of operating in private settings, we perform, to the best of our knowledge, the first extensive characterization of on-premise clusters. Specifically, we analyze data ranging from hardware failures to typical compute/storage requirements and workload profiles, from a large number of Nutanix clusters deployed at various companies. We show that private cloud hardware failure rates are lower, and that load/demand needs are more predictable than in other settings. Finally, we demonstrate the value of the measurements by using them to provide an analytical model for computing durability in private clouds, as well as a machine learning-driven approach for characterizing private clouds' growth.