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Model-driven Algorithms and Architectures for Self-Aware Computing Systems (Dagstuhl Seminar 15041) Kounev, Samuel Zhu, Xiaoyun Kephart, Jeffrey O. Kwiatkowska, Marta autonomic systems self-adaptive self-managing model-driven architecture-based systems management machine learning feedback-based design This report documents the program and the outcomes of Dagstuhl Seminar 15041 "Model-driven Algorithms and Architectures for Self-Aware Computing Systems". The design of self-aware computing systems calls for an integrated interdisciplinary approach building on results from multiple areas of computer science and engineering, including software and systems engineering, systems modeling, simulation and analysis, autonomic and organic computing, machine learning and artificial intelligence, data center resource management, and so on. The Dagstuhl Seminar 15041 served as a platform to raise the awareness about the relevant research efforts in the respective research communities as well as existing synergies that can be exploited to advance the state-of-the-art, formulate a new research agenda that takes a broader view on the problem following an integrated and interdisciplinary approach, and establish collaborations between academia and industry. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Samuel Kounev and Xiaoyun Zhu and Jeffrey O. Kephart and Marta Kwiatkowska 2015 Is Part Of Dagstuhl Reports, Volume 5, Issue 1 (2015) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.5.1.164 urn:nbn:de:0030-drops-50385 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.1.164 eng https://creativecommons.org/licenses/by/3.0/legalcode