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Scienti c computing systems are becoming increasingly complex and indeed are close to reaching a critical limit in manageability when using current human-in-the-loop techniques. In order to address this problem, autonomic, goal-driven management actions based on machine learning must be applied end to end across the scienti c computing landscape. Even though researchers proposed architectures and design choices for autonomic computing systems more than a decade ago, practical realization ofdoi:10.1145/3217197.3217205 dblp:conf/hpdc/KettimuthuLFBSW18 fatcat:q465b3cyibarnowssx4jny6jvu