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Auton. Adapt. Syst."],"published-print":{"date-parts":[[2020,6,30]]},"abstract":"<jats:p>\n            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 runtime 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 Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems.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 article, we present\n            <jats:italic>SARDE<\/jats:italic>\n            , a framework for self-adaptive resource demand estimation in continuous environments.\n            <jats:italic>SARDE<\/jats:italic>\n            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\n            <jats:italic>SARDE<\/jats:italic>\n            using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation,\n            <jats:italic>SARDE<\/jats:italic>\n            is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.\n          <\/jats:p>","DOI":"10.1145\/3463369","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T10:08:00Z","timestamp":1623233280000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["SARDE"],"prefix":"10.1145","volume":"15","author":[{"given":"Johannes","family":"Grohmann","sequence":"first","affiliation":[{"name":"University of W\u00fcrzburg, Germany"}]},{"given":"Simon","family":"Eismann","sequence":"additional","affiliation":[{"name":"University of W\u00fcrzburg, Germany"}]},{"given":"Andr\u00e9","family":"Bauer","sequence":"additional","affiliation":[{"name":"University of W\u00fcrzburg, Germany"}]},{"given":"Simon","family":"Spinner","sequence":"additional","affiliation":[{"name":"IBM, Boeblingen, Germany"}]},{"given":"Johannes","family":"Blum","sequence":"additional","affiliation":[{"name":"University of Konstanz, Konstanz, Germany"}]},{"given":"Nikolas","family":"Herbst","sequence":"additional","affiliation":[{"name":"University of W\u00fcrzburg, Germany"}]},{"given":"Samuel","family":"Kounev","sequence":"additional","affiliation":[{"name":"University of W\u00fcrzburg, Germany"}]}],"member":"320","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"volume-title":"Proceedings of the 2006 International Workshop on Integrating AI and Data Mining. 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