Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers

Raul Minon, Josu Diaz-de-Arcayas, Ana Torre-Bastidas, Philipp Hartlieb

Publikation: Beitrag in FachzeitschriftArtikelForschungBegutachtung


Development and operations (DevOps), artificial intelligence (AI), big data and edge– fog–cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applying them in order to optimise productivity. Some of them are addressed in this article, concretely, with respect to the adequate management of information technology (IT) infrastructures for automated analysis processes in critical fields such as the mining industry. In this area, this paper presents a tool called Pangea aimed at automatically generating suitable execution environments for deploying analytic pipelines. These pipelines are decomposed into various steps to execute each one in the most suitable environment (edge, fog, cloud or on-premise) minimising latency and optimising the use of both hardware and software resources. Pangea is focused in three distinct objectives: (1) generating the required infrastructure if it does not previously exist; (2) provisioning it with the necessary requirements to run the pipelines (i.e., configuring each host operative system and software, install dependencies and download the code to execute); and (3) deploying the pipelines. In order to facilitate the use of the architecture, a representational state transfer application programming interface (REST API) is defined to interact with it. Therefore, in turn, a web client is proposed. Finally, it is worth noting that in addition to the production mode, a local development environment can be generated for testing and benchmarking purposes.

PublikationsstatusVeröffentlicht - 11 Juni 2022

Bibliographische Notiz

Funding Information:
Funding: This research has been funded in the context of the IlluMINEation project, from the European Union’s Horizon 2020 research and innovation program under grant agreement No 869379.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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