Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations
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2024
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| author | Boito, Francieli Brandt, Jim Cardellini, Valeria Carns, Philip Ciorba, Florina M. Egan, Hilary Eleliemy, Ahmed Gentile, Ann Gruber, Thomas Hanson, Jeff Haus, Utz-Uwe Huck, Kevin Ilsche, Thomas Jakobsche, Thomas Jones, Terry Karlsson, Sven Mueen, Abdullah Ott, Michael Patki, Tapasya Peng, Ivy Raghavan, Krishnan Simms, Stephen Shoga, Kathleen Showerman, Michael Tiwari, Devesh Wilde, Torsten Yamamoto, Keiji |
| author_facet | Boito, Francieli Brandt, Jim Cardellini, Valeria Carns, Philip Ciorba, Florina M. Egan, Hilary Eleliemy, Ahmed Gentile, Ann Gruber, Thomas Hanson, Jeff Haus, Utz-Uwe Huck, Kevin Ilsche, Thomas Jakobsche, Thomas Jones, Terry Karlsson, Sven Mueen, Abdullah Ott, Michael Patki, Tapasya Peng, Ivy Raghavan, Krishnan Simms, Stephen Shoga, Kathleen Showerman, Michael Tiwari, Devesh Wilde, Torsten Yamamoto, Keiji |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16971 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations Boito, Francieli Brandt, Jim Cardellini, Valeria Carns, Philip Ciorba, Florina M. Egan, Hilary Eleliemy, Ahmed Gentile, Ann Gruber, Thomas Hanson, Jeff Haus, Utz-Uwe Huck, Kevin Ilsche, Thomas Jakobsche, Thomas Jones, Terry Karlsson, Sven Mueen, Abdullah Ott, Michael Patki, Tapasya Peng, Ivy Raghavan, Krishnan Simms, Stephen Shoga, Kathleen Showerman, Michael Tiwari, Devesh Wilde, Torsten Yamamoto, Keiji Distributed, Parallel, and Cluster Computing 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. |
| title | Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2401.16971 |