Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Francieli Boito, Jim Brandt, Valeria Cardellini, Philip Carns, Florina M. Ciorba, Hilary Egan, Ahmed Eleliemy, Ann Gentile, Thomas Gruber, Jeff Hanson, Utz Uwe Haus, Kevin Huck, Thomas Ilsche, Thomas Jakobsche, Terry Jones, Sven Karlsson, Abdullah Mueen, Michael Ott, Tapasya Patki, Krishnan RaghavanStephen Simms, Kathleen Shoga, Michael Showerman, Devesh Tiwari, Torsten Wilde, Keiji Yamamoto

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE International Conference on Cluster Computing Workshops and Posters
PublisherIEEE
Publication date2023
Pages37-43
ISBN (Print)979-8-3503-7063-8
DOIs
Publication statusPublished - 2023
Event25th IEEE International Conference on Cluster Computing Workshops - Hilton Santa Fe Historic Plaza, Santa Fe, United States
Duration: 31 Oct 20233 Nov 2023

Conference

Conference25th IEEE International Conference on Cluster Computing Workshops
LocationHilton Santa Fe Historic Plaza
Country/TerritoryUnited States
CitySanta Fe
Period31/10/202303/11/2023

Keywords

  • Autonomy loops
  • High performance computing
  • MAPE-K
  • Monitoring and operational data analytics

Fingerprint

Dive into the research topics of 'Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations'. Together they form a unique fingerprint.

Cite this