Cost-Efficient Elastic Stream Processing Using Application-Agnostic Performance Prediction

Abstract

Cloud computing adds great on-demand scalability to stream processing systems with its pay-per-use cost model. However, to promise service level agreements to users while keeping resource allocation cost low is a challenging task due to uncertainties coming from various sources, such as the target application's scalability, future computational demand, and the target cloud infrastructure's performance variability. To deal with these uncertainties, it is essential to create accurate application performance prediction models. In cloud computing, the current state of the art in performance modelling remains application-specific. We propose an application-agnostic performance modeling that is applicable to a wide range of applications. We also propose an extension to probabilistic performance prediction. This paper reports the progress we have made so far.