SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation

JOHANNES GROHMANN, University of Würzburg, Germany SIMON EISMANN, University of Würzburg, Germany ANDRÉ BAUER, University of Würzburg, Germany SIMON SPINNER, IBM, Germany JOHANNES BLUM, University of Konstanz, Germany NIKOLAS HERBST, University of Würzburg, Germany SAMUEL KOUNEV, University of Würzburg, Germany Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in run-time environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this paper, we present SARDE, a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic data sets. One set of different micro-benchmarks reflecting different possible system states and one data set consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique. CCS Concepts: • Computing methodologies → Learning paradigms; Model development and analysis; • Software and its engineering→ Software performance. Additional