Machine Learning Workflows in the Computing Continuum for Environmental Monitoring

Alessio Catalfamo; Atakan Aral; Ivona Brandic; Ewa Deelman; Massimo Villari

doi:10.1007/978-3-031-63775-9_27

Machine Learning Workflows in the Computing Continuum for Environmental Monitoring

Alessio Catalfamo
, Atakan Aral
, Ivona Brandic
, Ewa Deelman
, Massimo Villari

Research Group Scientific Computing

Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

Abstract

Cloud-Edge Continuum is an innovative approach that exploits the strengths of the two paradigms: Cloud and Edge computing. This new approach gives us a holistic vision of this environment, enabling new kinds of applications that can exploit both the Edge computing advantages (e.g., real-time response, data security, and so on) and the powerful Cloud computing infrastructure for high computational requirements. This paper proposes a Cloud-Edge computing Workflow solution for Machine Learning (ML) inference in a hydrogeological use case. Our solution is designed in a Cloud-Edge Continuum environment thanks to Pegasus Workflow Management System Tools that we use for the implementation phase. The proposed work splits the inference tasks, transparently distributing the computation performed by each layer between Cloud and Edge infrastructure. We use two models to implement a proof-of-concept of the proposed solution.

Original language	English
Title of host publication	Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings
Editors	Leonardo Franco, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	368-382
Number of pages	15
ISBN (Print)	9783031637742
DOIs	https://doi.org/10.1007/978-3-031-63775-9_27
Publication status	Published - 2024
Event	24th International Conference on Computational Science, ICCS 2024 - Malaga, Spain Duration: 2 Jul 2024 → 4 Jul 2024

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14836 LNCS
ISSN	0302-9743

Conference

Conference	24th International Conference on Computational Science, ICCS 2024
Country/Territory	Spain
City	Malaga
Period	2/07/24 → 4/07/24

Funding

This research was funded in part by the Austrian Science Fund (FWF) through following projects: Transprecise Edge Computing (Tri-ton) 10.55776/P36870; Trustworthy and Sustainable Code Offloading (Themis) 10.55776/PAT1668223; Sustainable Watershed Management Through IoT-Driven AI (Swain) 10.55776/I5201, and by the Austrian Research Promotion Agency (FFG) through the following project: Satellite-based Monitoring of Livestock in the Alpine Region (Virtual Shepherd), FFG Austrian Space Applications Programme ASAP 2022 #53079251. This research was also funded by the Italian Ministry of Health, Piano Operativo Salute (POS) trajectory 4 \u201CBiotechnology, bioinformatics and pharmaceutical development\u201D, through the Pharma-HUB Project \u201CHub for the repositioning of drugs in rare diseases of the nervous system in children\u201D (CUP J43C22000500006) and by Piano Operativo Salute (POS) trajectory 2 \u201CeHealth, diagnostica avanzata, medical device e mini invasivit\u00E0\u201D through the project \u201CRete eHealth: AI e strumenti ICT Innovativi orientati alla Diagnostica Digitale (RAIDD)\u201D(CUP J43C22000380001). Ewa Deelman\u2019s work was funded by the U.S. National Science Foundation under grants numbers 2331153 and 2103508 and by the U.S. Department of Energy under grant number DE-SC0024387.

Austrian Fields of Science 2012

102038 Cloud computing
102019 Machine learning

Keywords

Cloud-Edge
Continuum
Machine Learning
Pegasus
Worfklow

Access to Document

10.1007/978-3-031-63775-9_27

Cite this

Catalfamo, A., Aral, A., Brandic, I., Deelman, E., & Villari, M. (2024). Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. In L. Franco, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings (pp. 368-382). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-63775-9_27

Catalfamo, Alessio ; Aral, Atakan ; Brandic, Ivona et al. / Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings. editor / Leonardo Franco ; Clélia de Mulatier ; Maciej Paszynski ; Valeria V. Krzhizhanovskaya ; Jack J. Dongarra ; Peter M. A. Sloot. Springer Science and Business Media Deutschland GmbH, 2024. pp. 368-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14836 LNCS).

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author = "Alessio Catalfamo and Atakan Aral and Ivona Brandic and Ewa Deelman and Massimo Villari",

note = "Funding Information: This research was funded in part by the Austrian Science Fund (FWF) through following projects: Transprecise Edge Computing (Tri-ton) 10.55776/P36870; Trustworthy and Sustainable Code Offloading (Themis) 10.55776/PAT1668223; Sustainable Watershed Management Through IoT-Driven AI (Swain) 10.55776/I5201, and by the Austrian Research Promotion Agency (FFG) through the following project: Satellite-based Monitoring of Livestock in the Alpine Region (Virtual Shepherd), FFG Austrian Space Applications Programme ASAP 2022 \#53079251. This research was also funded by the Italian Ministry of Health, Piano Operativo Salute (POS) trajectory 4 \textbackslash{}u201CBiotechnology, bioinformatics and pharmaceutical development\textbackslash{}u201D, through the Pharma-HUB Project \textbackslash{}u201CHub for the repositioning of drugs in rare diseases of the nervous system in children\textbackslash{}u201D (CUP J43C22000500006) and by Piano Operativo Salute (POS) trajectory 2 \textbackslash{}u201CeHealth, diagnostica avanzata, medical device e mini invasivit\textbackslash{}u00E0\textbackslash{}u201D through the project \textbackslash{}u201CRete eHealth: AI e strumenti ICT Innovativi orientati alla Diagnostica Digitale (RAIDD)\textbackslash{}u201D(CUP J43C22000380001). Ewa Deelman\textbackslash{}u2019s work was funded by the U.S. National Science Foundation under grants numbers 2331153 and 2103508 and by the U.S. Department of Energy under grant number DE-SC0024387. Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 24th International Conference on Computational Science, ICCS 2024 ; Conference date: 02-07-2024 Through 04-07-2024",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Catalfamo, A, Aral, A, Brandic, I, Deelman, E & Villari, M 2024, Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. in L Franco, C de Mulatier, M Paszynski, VV Krzhizhanovskaya, JJ Dongarra & PMA Sloot (eds), Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings. Springer Science and Business Media Deutschland GmbH, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14836 LNCS, pp. 368-382, 24th International Conference on Computational Science, ICCS 2024, Malaga, Spain, 2/07/24. https://doi.org/10.1007/978-3-031-63775-9_27

Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. / Catalfamo, Alessio; Aral, Atakan; Brandic, Ivona et al.
Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings. ed. / Leonardo Franco; Clélia de Mulatier; Maciej Paszynski; Valeria V. Krzhizhanovskaya; Jack J. Dongarra; Peter M. A. Sloot. Springer Science and Business Media Deutschland GmbH, 2024. p. 368-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14836 LNCS).

Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

TY - GEN

T1 - Machine Learning Workflows in the Computing Continuum for Environmental Monitoring

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AU - Brandic, Ivona

AU - Deelman, Ewa

AU - Villari, Massimo

N1 - Funding Information: This research was funded in part by the Austrian Science Fund (FWF) through following projects: Transprecise Edge Computing (Tri-ton) 10.55776/P36870; Trustworthy and Sustainable Code Offloading (Themis) 10.55776/PAT1668223; Sustainable Watershed Management Through IoT-Driven AI (Swain) 10.55776/I5201, and by the Austrian Research Promotion Agency (FFG) through the following project: Satellite-based Monitoring of Livestock in the Alpine Region (Virtual Shepherd), FFG Austrian Space Applications Programme ASAP 2022 #53079251. This research was also funded by the Italian Ministry of Health, Piano Operativo Salute (POS) trajectory 4 \u201CBiotechnology, bioinformatics and pharmaceutical development\u201D, through the Pharma-HUB Project \u201CHub for the repositioning of drugs in rare diseases of the nervous system in children\u201D (CUP J43C22000500006) and by Piano Operativo Salute (POS) trajectory 2 \u201CeHealth, diagnostica avanzata, medical device e mini invasivit\u00E0\u201D through the project \u201CRete eHealth: AI e strumenti ICT Innovativi orientati alla Diagnostica Digitale (RAIDD)\u201D(CUP J43C22000380001). Ewa Deelman\u2019s work was funded by the U.S. National Science Foundation under grants numbers 2331153 and 2103508 and by the U.S. Department of Energy under grant number DE-SC0024387. Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Cloud-Edge Continuum is an innovative approach that exploits the strengths of the two paradigms: Cloud and Edge computing. This new approach gives us a holistic vision of this environment, enabling new kinds of applications that can exploit both the Edge computing advantages (e.g., real-time response, data security, and so on) and the powerful Cloud computing infrastructure for high computational requirements. This paper proposes a Cloud-Edge computing Workflow solution for Machine Learning (ML) inference in a hydrogeological use case. Our solution is designed in a Cloud-Edge Continuum environment thanks to Pegasus Workflow Management System Tools that we use for the implementation phase. The proposed work splits the inference tasks, transparently distributing the computation performed by each layer between Cloud and Edge infrastructure. We use two models to implement a proof-of-concept of the proposed solution.

AB - Cloud-Edge Continuum is an innovative approach that exploits the strengths of the two paradigms: Cloud and Edge computing. This new approach gives us a holistic vision of this environment, enabling new kinds of applications that can exploit both the Edge computing advantages (e.g., real-time response, data security, and so on) and the powerful Cloud computing infrastructure for high computational requirements. This paper proposes a Cloud-Edge computing Workflow solution for Machine Learning (ML) inference in a hydrogeological use case. Our solution is designed in a Cloud-Edge Continuum environment thanks to Pegasus Workflow Management System Tools that we use for the implementation phase. The proposed work splits the inference tasks, transparently distributing the computation performed by each layer between Cloud and Edge infrastructure. We use two models to implement a proof-of-concept of the proposed solution.

KW - Cloud-Edge

KW - Continuum

KW - Machine Learning

KW - Pegasus

KW - Worfklow

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DO - 10.1007/978-3-031-63775-9_27

M3 - Contribution to proceedings

AN - SCOPUS:85199530255

SN - 9783031637742

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 368

EP - 382

BT - Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings

A2 - Franco, Leonardo

A2 - de Mulatier, Clélia

A2 - Paszynski, Maciej

A2 - Krzhizhanovskaya, Valeria V.

A2 - Dongarra, Jack J.

A2 - Sloot, Peter M. A.

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 2 July 2024 through 4 July 2024

ER -

Catalfamo A, Aral A, Brandic I, Deelman E, Villari M. Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. In Franco L, de Mulatier C, Paszynski M, Krzhizhanovskaya VV, Dongarra JJ, Sloot PMA, editors, Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 368-382. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14836 LNCS). doi: 10.1007/978-3-031-63775-9_27