Towards Autonomic Science Infrastructure

Rajkumar Kettimuthu, Alok Choudhary, Zhengchun Liu, Ian Foster, Peter H. Beckman, Alex Sim, Kesheng Wu, Wei-keng Liao, Qiao Kang, Ankit Agrawal
2018 Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science - AI-Science'18  
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 of
more » ... systems has been limited, especially in scienti c computing environments. Growing interest and recent developments in machine learning have spurred proposals to apply machine learning for goal-based optimization of computing systems in an autonomous fashion. We review recent work that uses machine learning algorithms to improve computer system performance, identify gaps and open issues. We propose a hierarchical architecture that builds on the earlier proposals for autonomic computing systems to realize an autonomous science infrastructure.
doi:10.1145/3217197.3217205 dblp:conf/hpdc/KettimuthuLFBSW18 fatcat:q465b3cyibarnowssx4jny6jvu