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.
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Acknowledgements
This research was funded in part by the Austrian Science Fund (FWF) through following projects: Transprecise Edge Computing (Triton) 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 “Biotechnology, bioinformatics and pharmaceutical development”, through the Pharma-HUB Project “Hub for the repositioning of drugs in rare diseases of the nervous system in children” (CUP J43C22000500006) and by Piano Operativo Salute (POS) trajectory 2 “eHealth, diagnostica avanzata, medical device e mini invasività” through the project “Rete eHealth: AI e strumenti ICT Innovativi orientati alla Diagnostica Digitale (RAIDD)”(CUP J43C22000380001). Ewa Deelman’s 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.
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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: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14836. Springer, Cham. https://doi.org/10.1007/978-3-031-63775-9_27
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