While public cloud computing platforms have become popular in recent years, private clouds - operated by enterprises for their internal use - have also begun gaining traction. The configuration and continuous tuning of a private cloud to meet user demands is a complex task. While private cloud management frameworks provide a number of flexible configuration options for this purpose, they leave it to the administrator to determine how to best configure and tune the cloud platform for local needs. In this paper, we argue for an adaptive control plane for private clouds that simplifies the tasks of configuring and operating a private cloud such that each control plane service is adaptive to the workload seen due to end-user requests. We present a logistic regression model to automate the provisioning and dynamic reconfiguration of control plane services in a private cloud. We implement our approach for two control plane services - monitoring and messaging - for OpenStack-based private clouds. Our experimental results on a laboratory private cloud testbed and using public cloud workloads demonstrates the ability of our approach to provision and adapt such services from very small to very large private cloud configurations. © 2013 ACM.