Towards autonomic science infrastructure: Architecture, limitations, and open issues

Rajkumar Kettimuthu, Zhengchun Liu, Ian Foster, Peter H. Beckman, Alex Sim, Kesheng Wu, Wei keng Liao, Qiao Kang, Ankit Agrawal, Alok Choudhary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Scientific 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 scientific computing landscape. Even though researchers proposed architectures and design choices for autonomic computing systems more than a decade ago, practical realization of such systems has been limited, especially in scientific 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 1st International Workshop on Autonomous Infrastructure for Science, AI-Science 2018 - In conjunction with HPDC
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450358620
DOIs
StatePublished - Jun 11 2018
Event1st International Workshop on Autonomous Infrastructure for Science, AI-Science 2018 - Tempe, United States
Duration: Jun 11 2018 → …

Publication series

NameProceedings of the 1st International Workshop on Autonomous Infrastructure for Science, AI-Science 2018 - In conjunction with HPDC

Other

Other1st International Workshop on Autonomous Infrastructure for Science, AI-Science 2018
CountryUnited States
CityTempe
Period6/11/18 → …

Keywords

  • Autonomic Management System; Autonomic Science Infrastructure

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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