CloudXplor

Abstract

Configuration planning for modern information systems is a highly challenging task due to the implications of various factors such as the cloud paradigm, multi-bottleneck workloads, and Green IT efforts. Nonetheless, there is currently little or no support to help decision makers find sustainable configurations that are systematically designed according to economic principles (e.g., profit maximization). This paper explicitly addresses this shortcoming and presents a novel approach to configuration planning in clouds based on empirical data. The main contribution of this paper is our unique approach to configuration planning based on an iterative and interactive data refinement process. More concretely, our methodology correlates economic goals with sound technical data to derive intuitive domain insights. We have implemented our methodology as the CloudXplor Tool to provide a proof of concept and exemplify a concrete use case. CloudXplor, which can be modularly embedded in generic resource management frameworks, illustrates the benefits of empirical configuration planning. In general, this paper is a working example on how to navigate large quantities of technical data to provide a solid foundation for economical decisions.