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To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft\u2019s Azure cloud environment, and its efficiency in other providers\u2019 computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS\/IaaS) over the tested period.<\/jats:p>","DOI":"10.1007\/s10723-021-09561-3","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T11:03:35Z","timestamp":1620471815000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4512-9337","authenticated-orcid":false,"given":"Piotr","family":"Nawrocki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patryk","family":"Osypanka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"key":"9561_CR1","doi-asserted-by":"crossref","unstructured":"Adhikari, M., Amgoth, T.: Multi-objective accelerated particle swarm optimization technique for scientific workflows in iaas cloud. 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