Date of Original Version
Abstract or Table of Contents
To be effective for automation, in practice, system models used for performance prediction and behavior checking must be robust. They must be able to cope with component upgrades, misconfigurations, and workload-system interactions that were not anticipated. This paper promotes making models self-evolving, such that they continuously evaluate their accuracy and adjust their predictions accordingly. Such self-evaluation also enables confidence values to be provided with predictions, including identification of situations where no trustworthy prediction can be produced. With a combination of expectation-based and observation based techniques, we believe that such self-evolving models can be achieved and used as a robust foundation for tuning, problem diagnosis, capacity planning, and administration tasks.