Modeling Cloud performance with Kriging

Abstract

Cloud infrastructures allow service providers to implement elastic applications. These can be scaled at runtime to dynamically adjust their resources allocation to maintain consistent quality of service in response to changing working conditions, like flash crowds or periodic peaks. Providers need models to predict the system performances of different resource allocations to fully exploit dynamic application scaling. Traditional performance models such as linear models and queueing networks might be simplistic for real Cloud applications; moreover, they are not robust to change. We propose a performance modeling approach that is practical for highly variable elastic applications in the Cloud and automatically adapts to changing working conditions. We show the effectiveness of the proposed approach for the synthesis of a self-adaptive controller.