Litoiu, MarinRouf, Yar Akhter2025-07-232025-07-232025-04-072025-07-23https://hdl.handle.net/10315/43028Modern Software has become increasingly complex with the use of microservice architectures and cloud computing becoming essential practices in the continuous deployment cycle. Self-Adaptive Systems can help with automating service deployment, maintenance, and optimization of such applications. However, these cloud applications are becoming large-scale and complex in nature, often deployed on multi-node clusters on multiple cloud platforms. To improve the self-adaptive process for large-scale cloud applications, we introduce four main contributions in this thesis: (1) We present a Self-Adaptive MAPE-K (Monitor, Analysis, Planning, Execution) framework that is built with existing Components-off-the-Shelf (COTS) that interacts with each other to perform self-adaptive actions on multi-cloud environments. (2) We propose a novel method to identify a performance model that predicts metrics at any unexplored operational point of a cloud environment. (3) We introduce an interference detection method for industrial strength at-scale deployments and evaluate the method using several model types. (4) We propose a proactive dynamic model identification technique to predict the impact of cloud consolidation and co-location for large at-scale deployments. We evaluated each contribution with an extensive set of experiments ranging from feasibility to prediction accuracy.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2025-07-23Self-adaptive systemsCloud computingMicroservicePerformance modelingMachine learningDevOps