PHC Aurora with University of Tromso (Norway) (2023-2024) : Extrem Edge-Fog-Cloud continuum


This project is leaded by Laurent Lefevre and Issam Rais (UiT, Tromso, Norway).

The arctic tundra is one of the most sensitive eco-systems to climate change. It is a large area with presently too few large-scale observation sites. Scientific observatories for extreme climate environments (based on ICT facilities and sensors infrastructure) provide on-field data to researchers (e.g ecologists, biologists) in order to observe and model complex environments with rapidly changing conditions. Gathering, processing and reporting observations are often limited by the availability of sufficient energy. The reporting is also limited by the availability of a communication network with sufficient bandwidth and latency. The opportunities provided by the data are limited by the availability of the critical resources: energy and communication networks.

Collected data must be processed, transported, and stored on relevant ICT infrastructures. Such resources can be deployed in various geographic locations depending on the proximity of sensors and actuators. For extreme scientific observatories, we target ICT infrastructure based on a continuum of resources from sensors and actuators to fog nodes (with limited capabilities) and up to cloud infrastructures.

Extreme climate conditions imply highly heterogeneous systems. Two extremes are represented here: (i) Clouds, highly monitored and maintained, and (ii) edge devices in extreme conditions, needing high monitoring and maintenance but can’t have it (or in a very limited amount), as it would cost too much (e.g in human resources, devices, energy). In between, fog and edge devices can be in both situations, depending on their context. Such high heterogeneity creates hierarchies and cliques of nodes that have very different access to resources, monitoring and even availability.

The challenges that we want to address in this aurora project are how to (i) provide the needed mechanisms to reproduce the characteristics of extreme environments with an in-lab testbed (ii) provide an end-to-end energy monitoring of considered worst continuum infrastructure, (iii) discover the most impactful energy leverages to sustain observations and monitoring (iv) deploy a proof of concept (simulated and really implemented) that validates the abstraction, architecture, design and implementation choices.

CCGrid 2023 : The ATF (Avalon Task Force) was here

Mathilde Jay, Vladimir Ostapenco and Laurent Lefevre participated in the 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid2023) in Bangalore in India from May 1-4, 2023.

Work presented

An experimental comparison of software-based power meters: focus on CPU and GPU“, Mathilde Jay, Vladimir Ostapenco, Laurent Lefevre, Denis Trystram, Anne-Cécile Orgerie and Benjamin Fichel -> link to the article on hal:

This work is part of the “FrugalCloud” Inria and OVHcloud partnership.

Mathilde participated also in the Diversity, Equity and Inclusion (DEI) sessions and Laurent, as steering committee member, presented the candidature of Norway for hosting CCGrid2025 in Tromso, Norway (Issam Rais (ex ATF member and now Associate Professor in UiT at Tromso) will be the main organizer).

Avalon plonge dans le calcul à bain d’huile…

Le partenariat entre l’École normale supérieure de Lyon, TotaLinuX SAS et ITrium Cloud & Business s’est concrétisé le 31 août 2021 par la mise en route des premiers serveurs immergés au Pôle Scientifique de Modélisation Numérique avec le concours du Laboratoire de l’Informatique du Parallélisme (LIP – UMR CNRS 5668 | ENS de Lyon | UCB Lyon 1 | INRIA).

Côté Avalon, Laurent Lefevre, Edddy Caron, ainsi qu’un ingénieur recruté pendant 12 mois, vont mener une campagne d’évaluation énergétique, thermique et économique de ces solutions de calcul à immersion.

WG Avalon : Analysis of energy consumption in a precision beekeeping system

Hugo Hadjur (joint work with Laurent Lefevre and Doreid Ammar (Aivancity group))
October 13, 2020 – 2.30pm – 3.30pm
Honey bees have been domesticated by humans for several thousand years and mainly provide honey and pollination, which is fundamental for plant reproduction. Nowadays, the work of beekeepers is constrained by external factors that stress their production (parasites and pesticides among others). Taking care of large numbers of beehives is time-consuming, so integrating sensors to track their status can drastically simplify the work of beekeepers. Precision beekeeping complements beekeepers’ work thanks to the Internet of Things (IoT) technology. If used correctly, data can help to make the right diagnosis for honey bees colony, increase honey production and decrease bee mortality. Providing enough energy for on-hive and in-hive sensors is a challenge. Some solutions rely on energy harvesting, others target usage of large batteries. Either way, it is mandatory to analyze the energy usage of embedded equipment in order to design an energy efficient and autonomous bee monitoring system. This paper relies on a fully autonomous IoT framework that collects environmental and image data of a beehive. It consists of a data collecting node (environmental data sensors, camera, Raspberry Pi and Arduino) and a solar energy supplying node. Supported services are analyzed task by task from an energy profiling and efficiency standpoint, in order to identify the highly pressured areas of the framework. This first step will guide our goal of designing a sustainable precision beekeeping system, both technically and energy-wise.

Entretiens Jacques Cartier – Colloque : “Effets rebonds dans le numérique. Comment les détecter ? Comment les mesurer ? Comment les éviter ?”

2 Novembre 2020 – 9h00-12h00 (Heure Québec / Canada) // 15h00-18h00 (Heure Française)

Organisé par le Centre Jacques Cartier, l’Ecole Normale Supérieure de Lyon, Inria, le laboratoire LIP, l’Université de Sherbrooke, le CIRAIG et le GDS EcoInfo avec des intervenants franco-québécois en sciences du numérique et en sciences humaines et sociales.

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