Online Learning of Run-Time Models for Performance and Resource Management in Data Centers

In this chapter, we explain how to extract and learn run-time models that a system can use for self-aware performance and resource management in data centers. We abstract from concrete formalisms and identify extraction aspects relevant to performance models. We categorize the learning aspects into: (i) model structure, (ii) model parametrization (estimation and calibration of model parameters), and (iii) model adaptation options (change point detection and run-time reconfiguration). The chapter identifies alternative approaches for the respective model aspects. The type and granularity of each aspect depend on the characteristic of the concrete performance models.