Predico: A System for What-if Analysis in Complex Data Center Applications

Abstract

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.