Month: October 2018

by

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/

by

Thesis Defense – Hadrien Croubois

Hadrien Croubois: Toward an autonomic engine for scientific workflows and elastic Cloud infrastructure

Everyone is welcome to attend Hadrien Croubois’s thesis defense, which will take place Tuesday 16th October at 14h at Salle des thèse (ENS de Lyon).

You are also invited to the cocktail that follows the defense.

Advisors:

Eddy Caron, ENS de Lyon


Committee members:

Noël De Palma, Université Jospeh Fourier, reviewer
Johan Montagnat, CNRS, Laboratoire I3S UMR 7271, reviewer
Luciana Arantes, Université Sorbonne, examiner
Frédéric Desprez, Inria, examiner
Pushpinder Kaur Chouhan,Ulster University, examiner


Abstract:

The constant development of scientific and industrial computation infrastructures requires the concurrent development of scheduling and deployment mechanisms to manage such infrastructures. Throughout the last decade, the emergence of the Cloud paradigm raised many hopes, but achieving full platform autonomicity is still an ongoing challenge.

Work undertaken during this Ph.D. aimed at building a workflow engine that integrated the logic needed to manage workflow execution and Cloud deployment on its own. More precisely, we focus on Cloud solutions with a dedicated Data as a Service (DaaS) data management component. Our objective was to automate the execution of workflows submitted by many users on elastic Cloud resources.

This contribution proposes a modular middleware infrastructure and details the implementation of the underlying modules:

  • A workflow clustering algorithm that optimises data locality in the context of DaaS-centered communications;
  • A dynamic scheduler that executes clustered workflows on Cloud resources;
  • A deployment manager that handles the allocation and deallocation of Cloud resources according to the workload characteristics and users’ requirements.

All these modules have been implemented in a simulator to analyse their behaviour and measure their effectiveness when running both synthetic and real scientific workflows. We also implemented these modules in the DIET middleware to give it new features and prove the versatility of this approach. Simulation running the WASABI workflow (waves analysis based inference, a framework for the reconstruction of gene regulatory networks) showed that our approach can decrease the deployment cost by up to 44% while meeting the required deadlines.

by

Phd Crossover

Phd Crossover

LIP & LBMC – Avalon & Gandrillon – Hadrien & Arnaud –

En octobre vous pouvez découvrir le crossover entre la thèse d’Hadrien Croubois (Avalon) du laboratoire de l’Informatique du Parallélisme (LIP) de l’ENS  et d’Arnaud Bonnafoux (Gandrillon) du Laboratoire de Biologie Moléculaire de la Cellule (LBMC) de l’ENS.


au LBMC

Commencez par découvrir le vendredi 12 octobre 2018, à 14h à l’ENS de Lyon site Monod (salle de réunion M6) comment Arnaud Bonnafoux a conçu WASABI. (Plus d’information ici)


au LIP

Puis venez découvrir le mardi 16 octobre 2018, à 14h à l’ENS de Lyon site Monod (salle des thèses) comment le moteur de workflow d’Hadrien Croubois est profitable aux besoins en calcul de WASABI. (Plus d’information ici)


by

WG – Hadrien Croubois: Toward an autonomic engine for scientific workflows and elastic Cloud infrastructure

2018-10-02

Title: Toward an autonomic engine for scientific workflows and elastic Cloud infrastructure

Speaker: Hadrien Croubois

Abstract: The constant development of scientific and industrial computation infrastructures requires the concurrent development of scheduling and deployment mechanisms to manage such infrastructures. Throughout the last decade, the emergence of the Cloud paradigm raised many hopes, but achieving full platform autonomicity is still an ongoing challenge.

Work undertaken during this Ph.D. aimed at building a workflow engine that integrated the logic needed to manage workflow execution and \cloud deployment on its own. More precisely, we focus on \cloud solutions with a dedicated Data as a Service (DaaS) data management component. Our objective was to automate the execution of workflows submitted by many users on elastic Cloud resources.

This contribution proposes a modular middleware infrastructure and details the implementation of the underlying modules:

– A workflow clustering algorithm that optimises data locality in the context of DaaS-centered communications;

– A dynamic scheduler that executes clustered workflows on Cloud resources;

– A deployment manager that handles the allocation and deallocation of Cloud resources according to the workload characteristics and users’ requirements.

All these modules have been implemented in a simulator to analyse their behaviour and measure their effectiveness when running both synthetic and real scientific workflows. We also implemented these modules in the DIET middleware to give it new features and prove the versatility of this approach. Simulation running the WASABI workflow (waves analysis based inference, a framework for the reconstruction of gene regulatory networks) showed that our approach can decrease the deployment cost by up to 44% while meeting the required deadlines.