Integrating Run-Time Observations and Design Component Models for Cloud System Analysis

Abstract

Abstract. Run-time models have been proven beneficial in the past for predicting upcoming quality flaws in cloud applications. Observation ap-proaches relate measurements to executed code whereas prediction mod-els oriented towards design components are commonly applied to reflect reconfigurations in the cloud. Levels of abstraction differ between code observations and these prediction models. In this position paper, we ad-dress the specification of causal relations between observation data and a component-based run-time prediction model. We introduce a meta-model for observation data, based on which we propose a mapping language to (a) bridge divergent levels of abstraction and (b) trigger model updates. 1