Modern data center applications are complex distributed systems with tens or hundreds of interacting software components. An important management task in data centers is to predict the impact of a certain workload or reconfiguration change on the performance of the application. Such predictions require the design of “what-if” models of the application that take as input hypothetical changes in the application’s workload or environment and estimate its impact on performance.We present Predico, a workload-based what-if analysis system that uses commonly available monitoring information in large scale systems to enable the administrators to ask a variety of workload-based “what-if” queries about the system. Predico uses a network of queues to analytically model the behavior of large distributed applications. It automatically generates node-level queueing models and then uses model composition to build system-wide models. Predico employs a simple what-if query language and an intelligent query execution algorithm that employs on-the-fly model construction and a change propagation algorithm to efficiently answer queries on large scale systems. We have built a prototype of Predico and have used traces from two large production applications from a financial institution as well as real-world synthetic applications to evaluate its what-if modeling framework. Our experimental evaluation validates the accuracy of Predico’s node-level resource usage, latency and workload-models and then shows how Predico enables what-if analysis in two different applications.