Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

Daniel Rosendo; Alexandru Costan; Gabriel Antoniu; Matthieu Simonin; Jean Christophe Lombardo; Alexis Joly; Patrick Valduriez

doi:10.1109/Cluster48925.2021.00043

Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

Daniel Rosendo
, Alexandru Costan
, Gabriel Antoniu
, Matthieu Simonin
, Jean Christophe Lombardo
, Alexis Joly
, Patrick Valduriez

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

8 Scopus citations

Abstract

In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of reallife applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-toCloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	23-34
Number of pages	12
ISBN (Electronic)	9781728196664
DOIs	https://doi.org/10.1109/Cluster48925.2021.00043
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Cluster Computing, Cluster 2021 - Virtual, Portland, United States Duration: Sep 7 2021 → Sep 10 2021

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
Volume	2021-September
ISSN (Print)	1552-5244

Conference

Conference	2021 IEEE International Conference on Cluster Computing, Cluster 2021
Country/Territory	United States
City	Virtual, Portland
Period	09/7/21 → 09/10/21

Funding

This work was funded by Inria through the HPC-BigData Inria Challenge (IPL) and by French ANR OverFlow project (ANR-15- CE25-0003). Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations. We also would like to thank Romain Egele, Jaehoon Koo, Prasanna Balaprakash, and Orcun Yildiz from Argonne National Laboratory for their support. This work was funded by Inria through the HPC-BigData Inria Challenge (IPL) and by French ANR OverFlow project (ANR-15-CE25-0003). Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations. We also would like to thank Romain Egele, Jaehoon Koo, Prasanna Balaprakash, and Orcun Yildiz from Argonne National Laboratory for their support.

Keywords

Computing Continuum
Methodology
Optimization
Reproducibility

Access to Document

10.1109/Cluster48925.2021.00043

Cite this

Rosendo, D., Costan, A., Antoniu, G., Simonin, M., Lombardo, J. C., Joly, A., & Valduriez, P. (2021). Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. In Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021 (pp. 23-34). (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2021-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/Cluster48925.2021.00043

Rosendo, Daniel ; Costan, Alexandru ; Antoniu, Gabriel et al. / Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 23-34 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

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title = "Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum",

abstract = "In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of reallife applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-toCloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.",

keywords = "Computing Continuum, Methodology, Optimization, Reproducibility",

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doi = "10.1109/Cluster48925.2021.00043",

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Rosendo, D, Costan, A, Antoniu, G, Simonin, M, Lombardo, JC, Joly, A & Valduriez, P 2021, Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. in Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021. Proceedings - IEEE International Conference on Cluster Computing, ICCC, vol. 2021-September, Institute of Electrical and Electronics Engineers Inc., pp. 23-34, 2021 IEEE International Conference on Cluster Computing, Cluster 2021, Virtual, Portland, United States, 09/7/21. https://doi.org/10.1109/Cluster48925.2021.00043

Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. / Rosendo, Daniel; Costan, Alexandru; Antoniu, Gabriel et al.
Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 23-34 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2021-September).

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

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T1 - Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

AU - Rosendo, Daniel

AU - Costan, Alexandru

AU - Antoniu, Gabriel

AU - Simonin, Matthieu

AU - Lombardo, Jean Christophe

AU - Joly, Alexis

AU - Valduriez, Patrick

PY - 2021

Y1 - 2021

N2 - In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of reallife applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-toCloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.

AB - In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of reallife applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-toCloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.

KW - Computing Continuum

KW - Methodology

KW - Optimization

KW - Reproducibility

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M3 - Conference contribution

AN - SCOPUS:85126033251

T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC

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BT - Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021

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ER -

Rosendo D, Costan A, Antoniu G, Simonin M, Lombardo JC, Joly A et al. Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. In Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 23-34. (Proceedings - IEEE International Conference on Cluster Computing, ICCC). doi: 10.1109/Cluster48925.2021.00043

Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

Abstract

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Keywords

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