�ݺ�ߣshows by User: GillesFedak

�ݺ�ߣshows by User: GillesFedak / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: GillesFedak / Sat, 02 Mar 2019 07:42:28 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: GillesFedak Which Computing Infrastructure for the Decentralized World ? /slideshow/which-computing-infrastructure-for-the-decentralized-world/134075605 iexec-huaweiresearcheurope1-190302074228
Huawei Research Europe Symposium]]>
Huawei Research Europe Symposium]]> Sat, 02 Mar 2019 07:42:28 GMT /slideshow/which-computing-infrastructure-for-the-decentralized-world/134075605 GillesFedak@slideshare.net(GillesFedak) Which Computing Infrastructure for the Decentralized World ? GillesFedak Huawei Research Europe Symposium <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iexec-huaweiresearcheurope1-190302074228-thumbnail.jpg?width=120&height=120&fit=bounds" /> Huawei Research Europe Symposium

Which Computing Infrastructure for the Decentralized World ? from Gilles Fedak

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0 Devcon3 : iExec Allowing Scalable, Efficient, and Virtualized Off-chain Execution for Arbitrary Applications /slideshow/devcon3-iexec-allowing-scalable-efficient-and-virtualized-offchain-execution-for-arbitrary-applications/82386561 devcon3version3-171120154848
Talk presented at the Ethereum Developer Conference, Cancun, Mexico, November 2017]]>
Talk presented at the Ethereum Developer Conference, Cancun, Mexico, November 2017]]> Mon, 20 Nov 2017 15:48:48 GMT /slideshow/devcon3-iexec-allowing-scalable-efficient-and-virtualized-offchain-execution-for-arbitrary-applications/82386561 GillesFedak@slideshare.net(GillesFedak) Devcon3 : iExec Allowing Scalable, Efficient, and Virtualized Off-chain Execution for Arbitrary Applications GillesFedak Talk presented at the Ethereum Developer Conference, Cancun, Mexico, November 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/devcon3version3-171120154848-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk presented at the Ethereum Developer Conference, Cancun, Mexico, November 2017

Devcon3 : iExec Allowing Scalable, Efficient, and Virtualized Off-chain Execution for Arbitrary Applications from Gilles Fedak

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0 The iEx.ec Distributed Cloud: Latest Developments and Perspectives /slideshow/the-iexec-distributed-cloud-latest-developments-and-perspectives/72239020 iexecedcon-170216174655
Talk given at the European Ethereum Developper Conference EDCON Paris 2017]]>
Talk given at the European Ethereum Developper Conference EDCON Paris 2017]]> Thu, 16 Feb 2017 17:46:55 GMT /slideshow/the-iexec-distributed-cloud-latest-developments-and-perspectives/72239020 GillesFedak@slideshare.net(GillesFedak) The iEx.ec Distributed Cloud: Latest Developments and Perspectives GillesFedak Talk given at the European Ethereum Developper Conference EDCON Paris 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iexecedcon-170216174655-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk given at the European Ethereum Developper Conference EDCON Paris 2017

The iEx.ec Distributed Cloud: Latest Developments and Perspectives from Gilles Fedak

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0 iExec: Blockchain-based Fully Distributed Cloud Computing /slideshow/iexec-blockchainbased-fully-distributed-cloud-computing/66277802 iexecdevconv3-160921223415
Talk at the Etherreum Developper Conference. Presents our approach to build a fully decentralized Cloud Infrastructure based on the Ethereum blockchain and Desktop Grid middleware.]]>
Talk at the Etherreum Developper Conference. Presents our approach to build a fully decentralized Cloud Infrastructure based on the Ethereum blockchain and Desktop Grid middleware.]]> Wed, 21 Sep 2016 22:34:14 GMT /slideshow/iexec-blockchainbased-fully-distributed-cloud-computing/66277802 GillesFedak@slideshare.net(GillesFedak) iExec: Blockchain-based Fully Distributed Cloud Computing GillesFedak Talk at the Etherreum Developper Conference. Presents our approach to build a fully decentralized Cloud Infrastructure based on the Ethereum blockchain and Desktop Grid middleware. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iexecdevconv3-160921223415-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk at the Etherreum Developper Conference. Presents our approach to build a fully decentralized Cloud Infrastructure based on the Ethereum blockchain and Desktop Grid middleware.

iExec: Blockchain-based Fully Distributed Cloud Computing from Gilles Fedak

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0 How Blockchain and Smart Buildings can Reshape the Internet /slideshow/how-blockchain-and-smart-buildings-can-reshape-the-internet/66036690 gfedaksmartcityv1-160914225350
Talk at the Smart Building Alliance Summer School, Lyon, September 15]]>
Talk at the Smart Building Alliance Summer School, Lyon, September 15]]> Wed, 14 Sep 2016 22:53:50 GMT /slideshow/how-blockchain-and-smart-buildings-can-reshape-the-internet/66036690 GillesFedak@slideshare.net(GillesFedak) How Blockchain and Smart Buildings can Reshape the Internet GillesFedak Talk at the Smart Building Alliance Summer School, Lyon, September 15 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gfedaksmartcityv1-160914225350-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk at the Smart Building Alliance Summer School, Lyon, September 15

How Blockchain and Smart Buildings can Reshape the Internet from Gilles Fedak

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0 Big Data, Beyond the Data Center /slideshow/big-data-beyond-the-data-center/41797882 cebss-fedak-141120045929-conversion-gate02
Big Data, Beyond the Data Center Increasingly the next scientific discoveries and the next industrial innovative breakthroughs will depend on the capacity to extract knowledge and sense from gigantic amount of information. Examples vary from processing data provided by scientific instruments such as the CERN’s LHC; collecting data from large-scale sensor networks; grabbing, indexing and nearly instantaneously mining and searching the Web; building and traversing the billion-edges social network graphs; anticipating market and customer trends through multiple channels of information. Collecting information from various sources, recognizing patterns and distilling insights constitutes what is called the Big Data challenge. However, As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key challenge is to handle the complexity of data management on Hybrid distributed infrastructures, i.e assemblage of Cloud, Grid or Desktop Grids. In this talk, I will overview our works in this research area; starting with BitDew, a middleware for large scale data management on Clouds and Desktop Grids. Then I will present our approach to enable MapReduce on Desktop Grids. Finally, I will present our latest results around Active Data, a programming model for managing data life cycle on heterogeneous systems and infrastructures.]]>
Big Data, Beyond the Data Center Increasingly the next scientific discoveries and the next industrial innovative breakthroughs will depend on the capacity to extract knowledge and sense from gigantic amount of information. Examples vary from processing data provided by scientific instruments such as the CERN’s LHC; collecting data from large-scale sensor networks; grabbing, indexing and nearly instantaneously mining and searching the Web; building and traversing the billion-edges social network graphs; anticipating market and customer trends through multiple channels of information. Collecting information from various sources, recognizing patterns and distilling insights constitutes what is called the Big Data challenge. However, As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key challenge is to handle the complexity of data management on Hybrid distributed infrastructures, i.e assemblage of Cloud, Grid or Desktop Grids. In this talk, I will overview our works in this research area; starting with BitDew, a middleware for large scale data management on Clouds and Desktop Grids. Then I will present our approach to enable MapReduce on Desktop Grids. Finally, I will present our latest results around Active Data, a programming model for managing data life cycle on heterogeneous systems and infrastructures.]]> Thu, 20 Nov 2014 04:59:29 GMT /slideshow/big-data-beyond-the-data-center/41797882 GillesFedak@slideshare.net(GillesFedak) Big Data, Beyond the Data Center GillesFedak Big Data, Beyond the Data Center Increasingly the next scientific discoveries and the next industrial innovative breakthroughs will depend on the capacity to extract knowledge and sense from gigantic amount of information. Examples vary from processing data provided by scientific instruments such as the CERN’s LHC; collecting data from large-scale sensor networks; grabbing, indexing and nearly instantaneously mining and searching the Web; building and traversing the billion-edges social network graphs; anticipating market and customer trends through multiple channels of information. Collecting information from various sources, recognizing patterns and distilling insights constitutes what is called the Big Data challenge. However, As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key challenge is to handle the complexity of data management on Hybrid distributed infrastructures, i.e assemblage of Cloud, Grid or Desktop Grids. In this talk, I will overview our works in this research area; starting with BitDew, a middleware for large scale data management on Clouds and Desktop Grids. Then I will present our approach to enable MapReduce on Desktop Grids. Finally, I will present our latest results around Active Data, a programming model for managing data life cycle on heterogeneous systems and infrastructures. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cebss-fedak-141120045929-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Big Data, Beyond the Data Center Increasingly the next scientific discoveries and the next industrial innovative breakthroughs will depend on the capacity to extract knowledge and sense from gigantic amount of information. Examples vary from processing data provided by scientific instruments such as the CERN’s LHC; collecting data from large-scale sensor networks; grabbing, indexing and nearly instantaneously mining and searching the Web; building and traversing the billion-edges social network graphs; anticipating market and customer trends through multiple channels of information. Collecting information from various sources, recognizing patterns and distilling insights constitutes what is called the Big Data challenge. However, As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key challenge is to handle the complexity of data management on Hybrid distributed infrastructures, i.e assemblage of Cloud, Grid or Desktop Grids. In this talk, I will overview our works in this research area; starting with BitDew, a middleware for large scale data management on Clouds and Desktop Grids. Then I will present our approach to enable MapReduce on Desktop Grids. Finally, I will present our latest results around Active Data, a programming model for managing data life cycle on heterogeneous systems and infrastructures.

Big Data, Beyond the Data Center from Gilles Fedak

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0 Active Data PDSW'13 /slideshow/active-datapdsw13/33635995 active-data-pdsw13-140417034622-phpapp01
]]>
]]> Thu, 17 Apr 2014 03:46:22 GMT /slideshow/active-datapdsw13/33635995 GillesFedak@slideshare.net(GillesFedak) Active Data PDSW'13 GillesFedak <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/active-data-pdsw13-140417034622-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Active Data PDSW'13 from Gilles Fedak

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0 Active Data: Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures /GillesFedak/active-data-a-programming-model-to-manage-data-life-cycle-across-heterogeneous-systems-and-infrastructures active-data-gilles-130527042012-phpapp02
Active Data : Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures The Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the 'Data Life Cycle', i.e. the various operations performed on data: transfer, archiving, replication, deletion, etc. Indeed, data-intensive applications span over a large variety of devices and e-infrastructures which implies that many systems are involved in data management and processing. ''Active Data'' is new approach to automate and improve the expressiveness of data management applications. It consists of * a 'formal model' for Data Life Cycle, based on Petri Net, that allows to describe and expose data life cycle across heterogeneous systems and infrastructures. * a 'programming model' allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data.]]>
Active Data : Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures The Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the 'Data Life Cycle', i.e. the various operations performed on data: transfer, archiving, replication, deletion, etc. Indeed, data-intensive applications span over a large variety of devices and e-infrastructures which implies that many systems are involved in data management and processing. ''Active Data'' is new approach to automate and improve the expressiveness of data management applications. It consists of * a 'formal model' for Data Life Cycle, based on Petri Net, that allows to describe and expose data life cycle across heterogeneous systems and infrastructures. * a 'programming model' allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data.]]> Mon, 27 May 2013 04:20:11 GMT /GillesFedak/active-data-a-programming-model-to-manage-data-life-cycle-across-heterogeneous-systems-and-infrastructures GillesFedak@slideshare.net(GillesFedak) Active Data: Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures GillesFedak Active Data : Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures The Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the 'Data Life Cycle', i.e. the various operations performed on data: transfer, archiving, replication, deletion, etc. Indeed, data-intensive applications span over a large variety of devices and e-infrastructures which implies that many systems are involved in data management and processing. ''Active Data'' is new approach to automate and improve the expressiveness of data management applications. It consists of * a 'formal model' for Data Life Cycle, based on Petri Net, that allows to describe and expose data life cycle across heterogeneous systems and infrastructures. * a 'programming model' allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/active-data-gilles-130527042012-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Active Data : Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures The Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the 'Data Life Cycle', i.e. the various operations performed on data: transfer, archiving, replication, deletion, etc. Indeed, data-intensive applications span over a large variety of devices and e-infrastructures which implies that many systems are involved in data management and processing. ''Active Data'' is new approach to automate and improve the expressiveness of data management applications. It consists of * a 'formal model' for Data Life Cycle, based on Petri Net, that allows to describe and expose data life cycle across heterogeneous systems and infrastructures. * a 'programming model' allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data.

Active Data: Managing Data-Life Cycle on Heterogeneous Systems and Infrastructures from Gilles Fedak

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0 SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures /slideshow/spequlos-a-qos-service-for-bot-applications-using-best-effort-distributed-computing-infrastructures/21990900 hpdc12-130527041405-phpapp02
SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures Simon Delamare Gilles Fedak Derrick Kondo Oleg Lodygensky High-Performance Parallel and Distributed Computing, 2012]]>
SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures Simon Delamare Gilles Fedak Derrick Kondo Oleg Lodygensky High-Performance Parallel and Distributed Computing, 2012]]> Mon, 27 May 2013 04:14:05 GMT /slideshow/spequlos-a-qos-service-for-bot-applications-using-best-effort-distributed-computing-infrastructures/21990900 GillesFedak@slideshare.net(GillesFedak) SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures GillesFedak SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures Simon Delamare Gilles Fedak Derrick Kondo Oleg Lodygensky High-Performance Parallel and Distributed Computing, 2012 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hpdc12-130527041405-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures Simon Delamare Gilles Fedak Derrick Kondo Oleg Lodygensky High-Performance Parallel and Distributed Computing, 2012

SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Computing Infrastructures from Gilles Fedak

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0 Mapreduce Runtime Environments: Design, Performance, Optimizations /slideshow/mapreduce-rte/21990287 mapreduce-rte-130527040524-phpapp01
II Escola Regional de Alto Desempenho - Região Nordeste, Savaldor de Bahia, Brazil, October 22, 2013 Seminar Datenverarbeitung mit Map-Reduce University of Heidelberg (15/05/2012). ]]>
II Escola Regional de Alto Desempenho - Região Nordeste, Savaldor de Bahia, Brazil, October 22, 2013 Seminar Datenverarbeitung mit Map-Reduce University of Heidelberg (15/05/2012). ]]> Mon, 27 May 2013 04:05:24 GMT /slideshow/mapreduce-rte/21990287 GillesFedak@slideshare.net(GillesFedak) Mapreduce Runtime Environments: Design, Performance, Optimizations GillesFedak II Escola Regional de Alto Desempenho - Região Nordeste, Savaldor de Bahia, Brazil, October 22, 2013 Seminar Datenverarbeitung mit Map-Reduce University of Heidelberg (15/05/2012). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mapreduce-rte-130527040524-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> II Escola Regional de Alto Desempenho - Região Nordeste, Savaldor de Bahia, Brazil, October 22, 2013 Seminar Datenverarbeitung mit Map-Reduce University of Heidelberg (15/05/2012).

Mapreduce Runtime Environments: Design, Performance, Optimizations from Gilles Fedak

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0 https://cdn.slidesharecdn.com/profile-photo-GillesFedak-48x48.jpg?cb=1551512359 I am a permanent INRIA research scientist since 2004 and I am currently working at ENS-Lyon, France (Homepage: http://graal.ens-lyon.fr/~gfedak/). After receiving my Ph.D degree from University Paris Sud in 2003, I followed a postdoctoral fellowship at University California San Diego. My research interests are in Parallel and Distributed Computing, with a particular emphasis on the problematic of using large and loosely coupled distributed computing infrastructures to support highly demanding computational and data-intensive Science. I produced pioneering software and algorithms in the field of Grid and Cloud Computing that allow people to easily harness large parallel systems consisting ... http://graal.ens-lyon.fr/~gfedak/ https://cdn.slidesharecdn.com/ss_thumbnails/iexec-huaweiresearcheurope1-190302074228-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/which-computing-infrastructure-for-the-decentralized-world/134075605 Which Computing Infras... https://cdn.slidesharecdn.com/ss_thumbnails/devcon3version3-171120154848-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/devcon3-iexec-allowing-scalable-efficient-and-virtualized-offchain-execution-for-arbitrary-applications/82386561 Devcon3 : iExec Allowi... https://cdn.slidesharecdn.com/ss_thumbnails/iexecedcon-170216174655-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-iexec-distributed-cloud-latest-developments-and-perspectives/72239020 The iEx.ec Distributed...