ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum

Author:

Rosendo Daniel1,Mattoso Marta2,Costan Alexandru1,Souza Renan3,Pina Débora2,Valduriez Patrick4,Antoniu Gabriel1

Affiliation:

1. University of Rennes, Inria,CNRS, IRISA,Rennes,France

2. Federal University of Rio de Janeiro,Brazil

3. Oak Ridge National Laboratory,USA

4. University of Montpellier, Inria,CNRS, LIRMM,Montpellier,France

Publisher

IEEE

Reference70 articles.

1. Capturing provenance to improve the model training of pinns: first handon experiences with grid5000;silva;CILAMCE-PANACM 2021-Proceedings of the joint XLII Ibero-Latin-American Congress on Computational Methods in Engineering and III Pan-American Congress on Computational Mechanics,2021

2. Mqtt for sensor networks (mqtt-sn) protocol specification;stanford-clark;International Business Machines Corporation version 1,2013

3. Keeping track of user steering actions in dynamic workflows

4. A Comparative Evaluation of AMQP, MQTT and HTTP Protocols Using Real-Time Public Smart City Data

5. Provenance Supporting Hyperparameter Analysis in Deep Neural Networks

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