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; Ivy Peng; Krishnan Raghavan; Stephen Simms; Kathleen Shoga; Michael Showerman; Devesh Tiwari; Torsten Wilde; Keiji Yamamoto

doi:10.1109/CLUSTERWorkshops61457.2023.00016

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, Ivy PengKrishnan Raghavan, Stephen Simms, Kathleen Shoga, Michael Showerman, Devesh Tiwari, Torsten Wilde, Keiji Yamamoto

Intelligent Systems and Facilities

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	37-43
Number of pages	7
ISBN (Electronic)	9798350370621
DOIs	https://doi.org/10.1109/CLUSTERWorkshops61457.2023.00016
State	Published - 2023
Event	25th IEEE International Conference on Cluster Computing Workshops, CLUSTER Workshops 2023 - Santa Fe, United States Duration: Oct 31 2023 → Nov 3 2023

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)	1552-5244

Conference

Conference	25th IEEE International Conference on Cluster Computing Workshops, CLUSTER Workshops 2023
Country/Territory	United States
City	Santa Fe
Period	10/31/23 → 11/3/23

Funding

This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357. This work was funded by the European Union under the Horizon Europe program\u2019s OpenCUBE project, grant agreement 101092984. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology& Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Hon-eywell International Inc., for the U.S. Department of Energy\u2019s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344(LLNL-CONF-851925). The work is jointly supported by the European Union\u2019s Horizon 2020 research and innovation programme (grant agreement No. 957407, DAPHNE).

Keywords

MAPE-K
autonomy loops
high performance computing
monitoring and operational data analytics

Access to Document

10.1109/CLUSTERWorkshops61457.2023.00016

Cite this

Boito, F., Brandt, J., Cardellini, V., Carns, P., Ciorba, F. M., Egan, H., Eleliemy, A., Gentile, A., Gruber, T., Hanson, J., Haus, U. U., Huck, K., Ilsche, T., Jakobsche, T., Jones, T., Karlsson, S., Mueen, A., Ott, M., Patki, T., ... Yamamoto, K. (2023). Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. In Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023 (pp. 37-43). (Proceedings - IEEE International Conference on Cluster Computing, ICCC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTERWorkshops61457.2023.00016

Boito, Francieli ; Brandt, Jim ; Cardellini, Valeria et al. / Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 37-43 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

@inproceedings{18a88d57576b4e6b8e7a345780cef402,

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

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.",

keywords = "MAPE-K, autonomy loops, high performance computing, monitoring and operational data analytics",

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

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 25th IEEE International Conference on Cluster Computing Workshops, CLUSTER Workshops 2023 ; Conference date: 31-10-2023 Through 03-11-2023",

year = "2023",

doi = "10.1109/CLUSTERWorkshops61457.2023.00016",

language = "English",

series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "37--43",

booktitle = "Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023",

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Boito, F, Brandt, J, Cardellini, V, Carns, P, Ciorba, FM, Egan, H, Eleliemy, A, Gentile, A, Gruber, T, Hanson, J, Haus, UU, Huck, K, Ilsche, T, Jakobsche, T, Jones, T, Karlsson, S, Mueen, A, Ott, M, Patki, T, Peng, I, Raghavan, K, Simms, S, Shoga, K, Showerman, M, Tiwari, D, Wilde, T & Yamamoto, K 2023, Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. in Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023. Proceedings - IEEE International Conference on Cluster Computing, ICCC, Institute of Electrical and Electronics Engineers Inc., pp. 37-43, 25th IEEE International Conference on Cluster Computing Workshops, CLUSTER Workshops 2023, Santa Fe, United States, 10/31/23. https://doi.org/10.1109/CLUSTERWorkshops61457.2023.00016

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. / Boito, Francieli; Brandt, Jim; Cardellini, Valeria et al.
Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 37-43 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Carns, Philip

AU - Ciorba, Florina M.

AU - Egan, Hilary

AU - Eleliemy, Ahmed

AU - Gentile, Ann

AU - Gruber, Thomas

AU - Hanson, Jeff

AU - Haus, Utz Uwe

AU - Huck, Kevin

AU - Ilsche, Thomas

AU - Jakobsche, Thomas

AU - Jones, Terry

AU - Karlsson, Sven

AU - Mueen, Abdullah

AU - Ott, Michael

AU - Patki, Tapasya

AU - Peng, Ivy

AU - Raghavan, Krishnan

AU - Simms, Stephen

AU - Shoga, Kathleen

AU - Showerman, Michael

AU - Tiwari, Devesh

AU - Wilde, Torsten

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BT - Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023

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Boito F, Brandt J, Cardellini V, Carns P, Ciorba FM, Egan H et al. Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. In Proceedings - 2023 IEEE International Conference on Cluster Computing Workshops and Posters, CLUSTER Workshops 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 37-43. (Proceedings - IEEE International Conference on Cluster Computing, ICCC). doi: 10.1109/CLUSTERWorkshops61457.2023.00016

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

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