Platform-independent modeling and prediction of application resource usage characteristics

Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to address parts of the problem by either focusing on a specific application, or a specific platform, or on a small subset of system resources. We propose a simple and flexible approach for modeling application resource usage in a platform-independent manner that enables the prediction of application resource usage on unseen platforms. The technique proposed is application agnostic, requiring no modification to the application (binary or source) and no knowledge of application-semantics. We implement a Linux-based prototype and evaluate it using four different workloads including real-world applications and benchmarks. Our experiments reveal prediction errors that are bound within 6-24% of the observed for these workloads when using the proposed approach.