Completed Project

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

This collaboration concerns “EXPLORING ENERGY MONITORING AND LEVERAGING ENERGY EFFICIENCY ON END-TO-END WORST EDGE FOG CLOUD CONTINUUM FOR EXTREME CLIMATE ENVIRONMENTS OBSERVATORIES”.

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.

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Slices PP – Preparatory Phase

The digital infrastructures research community continues to face numerous new challenges towards the design of the Next Generation Internet. This is an extremely complex ecosystem encompassing communication, networking, data-management and data-intelligence issues, supported by established and emerging technologies such as IoT, 5/6G, cloud-to-edge computing. Coupled with the enormous amount of data generated and exchanged over the network, this calls for incremental as well as radically new design paradigms. Experimentally-driven research is becoming worldwide a de-facto standard, which has to be supported by large- scale research infrastructures to make results trusted, repeatable and accessible to the research communities.
SLICES-RI (Research Infrastructure), which was recently included in the 2021 ESFRI roadmap, aims to answer these problems by building a large infrastructure needed for the experimental research on various aspects of distributed computing, networking, IoT and 5/6G networks. It will provide the resources needed to continuously design, experiment, operate and automate the full lifecycle management of digital infrastructures, data, applications, and services.
Based on the two preceding projects within SLICES-RI, SLICES-DS (Design Study) and SLICES-SC (Starting Community), the SLICES-PP (Preparatory Phase) project will validate the requirements to engage into the implementation phase of the RI lifecycle. It will set the policies and decision processes for the governance of SLICES-RI: i.e., the legal and financial frameworks, the business model, the required human resource capacities and training programme. It will also settle the final technical architecture design for implementation. It will engage member states and stakeholders to secure commitment and funding needed for the platform to operate. It will position SLICES as an impactful instrument to support European advanced research, industrial competitiveness and societal impact in the digital era.

Project Information

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Action Exploratoire INRIA ExODE

Coordinateur: Jonathan Rouzaud-Cornabas (INRIA Beagle, Liris)

Participants: Samuel Bernard (INRIA Dracula, Institut Camille Jordan), Thierry Gautier (Avalon)

Date: 2019-2022

En Français:
En biologie, la grande majorité des systèmes peut être modélisée sous la forme d’équations différentielles ordinaires (ODE). Modéliser plus finement des objets biologiques mène à augmenter le nombre d’équations. Simuler des systèmes toujours plus grands mène également à augmenter le nombre d’équations. Par conséquent, nous observons une explosion de la taille des systèmes d’ODE à résoudre. Un verrou majeur est la limitation des logiciels de résolutions numériques d’ODE (solveur ODE) à quelques milliers d’équations à cause de temps de calcul prohibitif. L’AEx ExODE s’attaque à ce verrou via 1) l’introduction de nouvelles méthodes numériques qui tireront parti de la précision mixte qui mélange plusieurs précisions de nombre flottant au sein d’un schéma de calcul, 2) l’adaptation de ces nouvelles méthodes pour des machines de calcul de prochaines générations qui sont fortement hiérarchiques et hétérogénes et composées d’un grand nombre de CPUs et GPUs. Depuis un an, une nouvelle approche du Deep Learning se propose de remplacer les Recurrent Neural Network (RNN) par des systèmes d’ODE. Les méthodes numériques et parallèles d’ExODE seront évalué et adapté dans ce cadre afin de permettre l’amélioration de la performance et de l’exactitude de ces nouvelles approches.

En Anglais:
In biology, the vast majority of systems can be modeled as ordinary differential equations (ODEs). Modeling more finely biological objects leads to increase the number of equations. Simulating ever larger systems also leads to increasing the number of equations. Therefore, we observe a large increase in the size of the ODE systems to be solved. A major lock is the limitation of ODE numerical resolution software (ODE solver) to a few thousand equations due to prohibitive calculation time. The AEx ExODE tackles this lock via 1) the introduction of new numerical methods that will take advantage of the mixed precision that mixes several floating number precisions within numerical methods, 2) the adaptation of these new methods for next generation highly hierarchical and heterogeneous computers composed of a large number of CPUs and GPUs. For the past year, a new approach to Deep Learning has been proposed to replace the Recurrent Neural Network (RNN) with ODE systems. The numerical and parallel methods of ExODE will be evaluated and adapted in this framework in order to improve the performance and accuracy of these new approaches.

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SLICES-DS: Slices – Design Study

Scientific Large-scale Infrastructure for Computing/Communication Experimental Studies – Design Study

Objective

Digital Infrastructures as the future Internet, constitutes the cornerstone of the digital transformation of our society. As such, Innovation in this domain represents an industrial need, a sovereignty concern and a security threat. Without Digital Infrastructure, none of the advanced services envisaged for our society is feasible. They are both highly sophisticated and diverse physical systems but at the same time, they form even more complex, evolving and massive virtual systems. Their design, deployment and operation are critical. In order to research and master Digital infrastructures, the research community needs to address significant challenges regarding their efficiency, trust, availability, reliability, range, end-to-end latency, security and privacy. Although some important work has been done on these topics, the stringent need for a scientific instrument, a test platform to support the research in this domain is an urgent concern. SLICES ambitions to provide a European-wide test-platform, providing advanced compute, storage and network components, interconnected by dedicated high-speed links. This will be the main experimental collaborative instrument for researchers at the European level, to explore and push further, the envelope of the future Internet. A strong, although fragmented expertise, exists in Europe and could be leveraged to build it. SLICES is our answer to this need. It is ambitious, practical but overall timely and necessary. The main objective of SLICES-DS is to adequately design SLICES in order to strengthen research excellence and innovation capacity of European researchers and scientists in the design and operation of Digital Infrastructures. The SLICES Design study will build upon the experience of the existing core group of partners, to prepare in details the conceptual and technical design of the new leading edge SLICES-RI for the next phases of the RI’s lifecycle.

Project Information

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PRACE 6th Implementation Phase Project

Summary

Prace
PRACE, the Partnership for Advanced Computing is the permanent pan-European High Performance Computing service providing world-class systems for world-class science. Systems at the highest performance level (Tier-0) are deployed by Germany, France, Italy, Spain and Switzerland, providing researchers with more than 17 billion core hours of compute time. HPC experts from 25 member states enabled users from academia and industry to ascertain leadership and remain competitive in the Global Race. Currently PRACE is finalizing the transition to PRACE 2, the successor of the initial five year period. The objectives of PRACE-6IP are to build on and seamlessly continue the successes of PRACE and start new innovative and collaborative activities proposed by the consortium. These include: assisting the development of PRACE 2; strengthening the internationally recognised PRACE brand; continuing and extend advanced training which so far provided more than 36 400 person·training days; preparing strategies and best practices towards Exascale computing, work on forward-looking SW solutions; coordinating and enhancing the operation of the multi-tier HPC systems and services; and supporting users to exploit massively parallel systems and novel architectures. A high level Service Catalogue is provided. The proven project structure will be used to achieve each of the objectives in 7 dedicated work packages. The activities are designed to increase Europe’s research and innovation potential especially through: seamless and efficient Tier-0 services and a pan-European HPC ecosystem including national capabilities; promoting take-up by industry and new communities and special offers to SMEs; assistance to PRACE 2 development; proposing strategies for deployment of leadership systems; collaborating with the ETP4HPC, CoEs and other European and international organisations on future architectures, training, application support and policies. This will be monitored through a set of KPIs.

Project Information

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Live VM migration scheduling with a dependency graph

Optimizing resource management in a data center is crucial for economic and ecological reasons.
One of the key points is to distribute all virtual machines running on a minimum number of physical servers. With this in mind, we propose to study an optimization problem which is the migration of a set of virtual machines under different constraints in a datacenter. The aim is to determine the best migration sequence for a set of virtual machines from an initial state to a final state, which minimizes the total migration time, with or without intermediate migration. The problem can be modeled by a state graph.

Duration: 2019-2021

Project in cooperation with LIRIS.

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EoCoE-II: Energy Oriented Center of Excellence: toward exascale for energy

Summary

Europe is undergoing a major transition in its energy generation and supply infrastructure. The urgent need to halt carbon dioxide emissions and prevent dangerous global temperature rises has received renewed impetus following the unprecedented international commitment to enforcing the 2016 Paris Agreement on climate change. Rapid adoption of solar and wind power generation by several EU countries has demonstrated that renewable energy can competitively supply significant fractions of local energy needs in favourable conditions. These and other factors have combined to create a set of irresistible environmental, economic and health incentives to phase out power generation by fossil fuels in favour of decarbonised, distributed energy sources. While the potential of renewables can no longer be questioned, ensuring reliability in the absence of constant conventionally powered baseload capacity is still a major challenge.

The EoCoE-II project will build on its unique, established role at the crossroads of HPC and renewable energy to accelerate the adoption of production, storage and distribution of clean electricity. How will we achieve this? In its proof-of-principle phase, the EoCoE consortium developed a comprehensive, structured support pathway for enhancing the HPC capability of energy-oriented numerical models, from simple entry-level parallelism to fully-fledged exascale readiness. At the top end of this scale, promising applications from each energy domain have been selected to form the basis of 5 new Energy Science Challenges in the present successor project EoCoE-II that will be supported by 4 Technical Challenges

Partners
CEA, FZJ, ENEA, BSC, CNRS, INRIA, CERFACS, MPG, FRAUNHOFER, FAU, CNR, UNITN, PSNC, ULB, UBAH, CIEMAT, IFPEN, DDN, RWTH, UNITOV

Project Information
EoCoE-II is a H2020 RIA european project, call H2020-INFRAEDI-2018-1.

Duration: 3 years, Jan 1st 2019, Dec 31st 2021.

Avalon Members: T. Gautier, C. Perez

Online Resources

URL: https://www.eocoe.eu/

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MRSEI Fennec

FastEr NaNo-Characterisation

L’objectif du projet FENNEC de l’ANR-MRSEI est de faciliter le montage d’un projet pour l’appel DT-NMBP-08-2019 intitulé « Real-time nano-characterisation technologies (RIA) ». La caractérisation à l’échelle nanométrique permet d’avoir des informations uniques sur la structuration et les propriétés des matériaux et des dispositifs mais nécessite une expérience pointue dépendant des matériaux envisagés, des temps d’acquisition, de dépouillement et d’analyse, incompatibles avec les contraintes d’une ligne de production industrielle. Cet appel souhaite réduire d’abord ces différentes contraintes notamment en accélérant l’acquisition et l’analyse des données instrumentales. Il veut aussi pouvoir valoriser plus facilement les résultats de nano-caractérisations avancées.

Start Date: 2018, August 1st

Duration: 24 months

Leader: T. Deutsch (CEA/MEM)

Avalon Members: C. Perez, E. Caron

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Associated joint team between Avalon and RDI2 Lab. in Rutgers University

  Associated joint team between Avalon and RDI2 Lab. in Rutgers University on   Sustainable Ultra Scale compuTing, dAta and energy Management

The collaboration proposed by the SUSTAM associate team focuses on aspects of sustainability in ultra-scale systems. Launched in 2017, the SUSTAM associated team aims to design a multi-criteria orchestration framework that manages resources, data and energy consumption in an efficient manner. The SUSTAM associate team will enable a long-term collaboration between the Inria Avalon and the RDI² team (Rutgers University . It will allow the teams to coordinate efforts and pursue common research activities in topics such as sustainable software solutions, resource and big-data management, elasticity of stream and batch applications, and energy efficiency. The involved members will contribute to the design of a common architecture and framework with components and algorithms adapted to various contexts.

Web Site: http://avalon.ens-lyon.fr/sustam/