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145 results on '"d'Orazio, Laurent"'

1. GUESS: MonitorinG Join qUery Execution in Serverless and Serverful Spark

Author

Tran Phan, An-Truong, D’orazio, Laurent, Phan, Thuong-Cang, Gruenwald, Le, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Onizuka, Makoto, editor, Lee, Jae-Gil, editor, Tong, Yongxin, editor, Xiao, Chuan, editor, Ishikawa, Yoshiharu, editor, Amer-Yahia, Sihem, editor, Jagadish, H. V., editor, and Lu, Kejing, editor

Published
2024
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2. Form-Based Semantic Caching on Time Series

Author

Le, Trung-Dung, Kantere, Verena, d’Orazio, Laurent, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Garau, Chiara, editor, Taniar, David, editor, C. Rocha, Ana Maria A., editor, and Faginas Lago, Maria Noelia, editor

Published
2024
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3. RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources

Author

Yalles, Sidahmed, Handaoui, Mohamed, Dartois, Jean-Emile, Barais, Olivier, d'Orazio, Laurent, and Boukhobza, Jalil

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract

One of the main objectives of Cloud Providers (CP) is to guarantee the Service-Level Agreement (SLA) of customers while reducing operating costs. To achieve this goal, CPs have built large-scale datacenters. This leads, however, to underutilized resources and an increase in costs. A way to improve the utilization of resources is to reclaim the unused parts and resell them at a lower price. Providing SLA guarantees to customers on reclaimed resources is a challenge due to their high volatility. Some state-of-the-art solutions consider keeping a proportion of resources free to absorb sudden variation in workloads. Others consider stable resources on top of the volatile ones to fill in for the lost resources. However, these strategies either reduce the amount of reclaimable resources or operate on less volatile ones such as Amazon Spot instance. In this paper, we proposed RISCLESS, a Reinforcement Learning strategy to exploit unused Cloud resources. Our approach consists of using a small proportion of stable on-demand resources alongside the ephemeral ones in order to guarantee customers SLA and reduce the overall costs. The approach decides when and how much stable resources to allocate in order to fulfill customers' demands. RISCLESS improved the CPs' profits by an average of 15.9% compared to state-of-the-art strategies. It also reduced the SLA violation time by an average of 36.7% while increasing the amount of used ephemeral resources by 19.5% on average

Published
2022

5. ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources

Author

Handaoui, Mohamed, Dartois, Jean-Emile, Boukhobza, Jalil, Barais, Olivier, and d'Orazio, Laurent

Subjects
Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Cloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources (referred to as ephemeral resources) at a lower price. However, reselling resources needs to meet the expectations of its customers in terms of Quality of Service. The goal is so to maximize the amount of reclaimed resources while avoiding SLA penalties. To achieve that, cloud providers have to estimate their future utilization to provide availability guarantees. The prediction should consider a safety margin for resources to react to unpredictable workloads. The challenge is to find the safety margin that provides the best trade-off between the amount of resources to reclaim and the risk of SLA violations. Most state-of-the-art solutions consider a fixed safety margin for all types of metrics (e.g., CPU, RAM). However, a unique fixed margin does not consider various workloads variations over time which may lead to SLA violations or/and poor utilization. In order to tackle these challenges, we propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. The strategy learns from past prediction errors (that caused SLA violations). Our solution reduces significantly the SLA violation penalties on average by 2.7x and up to 3.4x. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%.

Published
2020

7. SLA-Aware Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization

Author

Wang, Chenxiao, Gruenwald, Le, d’Orazio, Laurent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wrembel, Robert, editor, Gamper, Johann, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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8. Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization

Author

Wang, Chenxiao, Gruenwald, Le, d’Orazio, Laurent, Leal, Eleazar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Attiogbé, Christian, editor, and Ben Yahia, Sadok, editor

Published
2021
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9. Cost Models for Selecting Materialized Views in Public Clouds

Author

Perriot, Romain, Pfeifer, Jérémy, 'Orazio, Laurent D, Bachelet, Bruno, Bimonte, Sandro, and Darmont, Jérôme

Subjects
Computer Science - Databases
Abstract

Data warehouse performance is usually achieved through physical data structures such as indexes or materialized views. In this context, cost models can help select a relevant set ofsuch performance optimization structures. Nevertheless, selection becomes more complex in the cloud. The criterion to optimize is indeed at least two-dimensional, with monetary cost balancing overall query response time. This paper introduces new cost models that fit into the pay-as-you-go paradigm of cloud computing. Based on these cost models, an optimization problem is defined to discover, among candidate views, those to be materialized to minimize both the overall cost of using and maintaining the database in a public cloud and the total response time ofa given query workload. We experimentally show that maintaining materialized views is always advantageous, both in terms of performance and cost.

Published
2017

11. A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems

Author

Arani, Zachary, Chapman, Drake, Wang, Chenxiao, Gruenwald, Le, d’Orazio, Laurent, Basiuk, Taras, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bellatreche, Ladjel, editor, Bieliková, Mária, editor, Boussaïd, Omar, editor, Catania, Barbara, editor, Darmont, Jérôme, editor, Demidova, Elena, editor, Duchateau, Fabien, editor, Hall, Mark, editor, Merčun, Tanja, editor, Novikov, Boris, editor, Papatheodorou, Christos, editor, Risse, Thomas, editor, Romero, Oscar, editor, Sautot, Lucile, editor, Talens, Guilaine, editor, Wrembel, Robert, editor, and Žumer, Maja, editor

Published
2020
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12. Cooking Related Carbon Footprint Evaluation and Optimisation

Author

de Toledo, Damien Alvarez, d’Orazio, Laurent, Andres, Frederic, Leite, Maria C. A., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bellatreche, Ladjel, editor, Bieliková, Mária, editor, Boussaïd, Omar, editor, Catania, Barbara, editor, Darmont, Jérôme, editor, Demidova, Elena, editor, Duchateau, Fabien, editor, Hall, Mark, editor, Merčun, Tanja, editor, Novikov, Boris, editor, Papatheodorou, Christos, editor, Risse, Thomas, editor, Romero, Oscar, editor, Sautot, Lucile, editor, Talens, Guilaine, editor, Wrembel, Robert, editor, and Žumer, Maja, editor

Published
2020
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13. Dynamic Estimation and Grid Partitioning Approach for Multi-objective Optimization Problems in Medical Cloud Federations

Author

Le, Trung-Dung, Kantere, Verena, d’Orazio, Laurent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hameurlain, Abdelkader, editor, and Tjoa, A Min, editor

Published
2020
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14. Toward FPGA-Based Semantic Caching for Accelerating Data Analysis with Spark and HDFS

Author

Maghzaoui, Marouan, d’Orazio, Laurent, Lallet, Julien, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kotzinos, Dimitris, editor, Laurent, Dominique, editor, Spyratos, Nicolas, editor, Tanaka, Yuzuru, editor, and Taniguchi, Rin-ichiro, editor

Published
2019
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15. On Complex Value Relations in Hive

Author

Pilven, Matthieu, Scherzinger, Stefanie, d’Orazio, Laurent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Guizzardi, Giancarlo, editor, Gailly, Frederik, editor, and Suzana Pitangueira Maciel, Rita, editor

Published
2019
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17. A large-scale and fault-tolerant approach of subgraph mining using density-based partitioning

Author

Aridhi, Sabeur, d'Orazio, Laurent, Maddouri, Mondher, and Nguifo, Engelbert Mephu

Subjects
Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Social and Information Networks
Abstract

Recently, graph mining approaches have become very popular, especially in domains such as bioinformatics, chemoinformatics and social networks. In this scope, one of the most challenging tasks is frequent subgraph discovery. This task has been motivated by the tremendously increasing size of existing graph databases. Since then, an important problem of designing efficient and scaling approaches for frequent subgraph discovery in large clusters, has taken place. However, failures are a norm rather than being an exception in large clusters. In this context, the MapReduce framework was designed so that node failures are automatically handled by the framework. In this paper, we propose a large-scale and fault-tolerant approach of subgraph mining by means of a density-based partitioning technique, using MapReduce. Our partitioning aims to balance computation load on a collection of machines. We experimentally show that our approach decreases significantly the execution time and scales the subgraph discovery process to large graph databases., Comment: The paper is under reviewing and we want to cancel the submission. Thank you for your understanding

Published
2012
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21. Storing and Querying DICOM Data with HYTORMO

Author

Nguyen-Cong, Danh, d’Orazio, Laurent, Tran, Nga, Hacid, Mohand-Said, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Fusheng, editor, Yao, Lixia, editor, and Luo, Gang, editor

Published
2017
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23. A Semantic Matrix for Aggregate Query Rewriting

Author

Perriot, Romain, d’Orazio, Laurent, Laurent, Dominique, Spyratos, Nicolas, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Grant, Emanuel, editor, Kotzinos, Dimitris, editor, Laurent, Dominique, editor, Spyratos, Nicolas, editor, and Tanaka, Yuzuru, editor

Published
2016
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24. A Theoretical and Experimental Comparison of Filter-Based Equijoins in MapReduce

Author

Phan, Thuong-Cang, d’Orazio, Laurent, Rigaux, Philippe, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hameurlain, Abdelkader, editor, Küng, Josef, editor, and Wagner, Roland, editor

Published
2016
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25. Time-, Energy-, and Monetary Cost-Aware Cache Design for a Mobile-Cloud Database System

Author

Perrin, Mikael, Mullen, Jonathan, Helff, Florian, Gruenwald, Le, d’Orazio, Laurent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Fusheng, editor, Luo, Gang, editor, Weng, Chunhua, editor, Khan, Arijit, editor, Mitra, Prasenjit, editor, and Yu, Cong, editor

Published
2016
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26. HeROfake: Heterogeneous Resources Orchestration in a Serverless Cloud – An Application to Deepfake Detection

Author

Lannurien, Vincent, d'Orazio, Laurent, Barais, Olivier, Bernard, Esther, Weppe, Olivier, Beaulieu, Laurent, Kacete, Amine, Paquelet, Stéphane, Boukhobza, Jalil, Domaine Hypermedia (IRT b<>com) (Hypermedia), Institut de Recherche Technologique b-com (IRT b-com), B<>COM [Cesson Sévigné], Equipe Software/HArdware and unKnown Environment inteRactions (Lab-STICC_SHAKER), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institute of Research and Technology b<>com, dedicated to digital technologies, funded by the French government through the ANR Investment referenced ANR-A0-AIRT-07

Subjects
serverless, allocation, workload characterization, deepfake, energy consumption, heterogeneous resources, GPU, [INFO]Computer Science [cs], scheduling, SLA, FPGA
Abstract

International audience; Serverless is a trending service model for cloud computing. It shifts a lot of the complexity from customers to service providers. However, current serverless platforms mostly consider the provider's infrastructure as homogeneous, as well as the users' requests. This limits possibilities for the provider to leverage heterogeneity in their infrastructure to improve function response time and reduce energy consumption. We propose a heterogeneity-aware serverless orchestrator for private clouds that consists of two components: the autoscaler allocates heterogeneous hardware resources (CPUs, GPUs, FPGAs) for function replicas, while the scheduler maps function executions to these replicas. Our objective is to guarantee function response time, while enabling the provider to reduce resource usage and energy consumption. This work considers a case study for a deepfake detection application relying on CNN inference. We devised a simulation environment that implements our model and a baseline Knative orchestrator, and evaluated both policies with regard to consolidation of tasks, energy consumption and SLA penalties. Experimental results show that our platform yields substantial gains for all those metrics, with an average of 35% less energy consumed for function executions while consolidating tasks on less than 40% of the infrastructure's nodes, and more than 60% less SLA violations.

Published
2023

27. Multi-objective query optimization in Spark SQL

Author

Georgoulakis, Michail, Kantere, Verena, D’orazio, Laurent, d'Orazio, Laurent, National Technical University of Athens [Athens] (NTUA), A Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]
Abstract

International audience

Published
2022

30. The Lannion report on Big Data and Security Monitoring Research

Author

d'Orazio, Laurent, primary, Boukhobza, Jalil, additional, Rana, Omer, additional, Agoun, Juba, additional, Gruenwald, Le, additional, Rannou, Herve, additional, Bertino, Elisa, additional, Hacid, Mohand-Said, additional, Saidi, Taofik, additional, Bossert, Georges, additional, Nguyen Huu, Van Long, additional, Tombroff, Dimitri, additional, and Onizuka, Makoto, additional

Published
2022
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31. ASSIST: Outil pour l'extraction et l'analyse statistique d'articles

Author

Fouillé, Justine, Lan Huong Nguyen, Thi, Alix, Baptiste, Becker, Brett, Rochard, Matthieu, de Ribaupierre, Hélène, d'Orazio, Laurent, A Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

National audience; Il y a moins d'auteures que d'auteurs dans la recherche scientifique. Cependant, il n'existe pas encore, à notre connaissance, de système permettant différentes analyses sur les données disponibles et de confirmer cette hypothese. Ce travail propose une extension d'un outil précédemment réalisé, afin de le rendre plus performant et d'ajouter de nouvelles fonctionnalités. Ces nouvelles fonctionnalités sont le nuage de mots-clés ou la nouvelle fonctionnalité statistique. Les sources, références et autres informations sur l'article seront affichées pour chaque article récupéré. Les sexes des auteurs seront déterminés à l'aide d'une base de données reliant les prénoms aux sexes, afin de pouvoir obtenir des statistiques sur un grand nombre d'articles collectés.

Published
2022

33. Cooperative Database Caching within Cloud Environments

Author

Vancea, Andrei, Machado, Guilherme Sperb, d’Orazio, Laurent, Stiller, Burkhard, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sadre, Ramin, editor, Novotný, Jiří, editor, Čeleda, Pavel, editor, Waldburger, Martin, editor, and Stiller, Burkhard, editor

Published
2012
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34. A Catalog-Based Caching Strategy for Structured P2P Systems

Author

Arnedo, Mile, del Pilar Villamil, Maria, Villanueva, Ricardo, Castro, Harold, d’Orazio, Laurent, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hameurlain, Abdelkader, editor, Morvan, Franck, editor, and Tjoa, A Min, editor

Published
2010
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35. Multidimensional Arrays for Warehousing Data on Clouds

Author

d’Orazio, Laurent, Bimonte, Sandro, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hameurlain, Abdelkader, editor, Morvan, Franck, editor, and Tjoa, A Min, editor

Published
2010
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36. Merging File Systems and Data Bases to Fit the Grid

Author

Denneulin, Yves, Labbé, Cyril, d’Orazio, Laurent, Roncancio, Claudia, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hameurlain, Abdelkader, editor, Morvan, Franck, editor, and Tjoa, A Min, editor

Published
2010
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37. DHTCache: A Distributed Service to Improve the Selection of Cache Configurations within a Highly-Distributed Context

Author

Gómez, Carlos E., del Pilar Villamil, María, Castro, Harold E., d’Orazio, Laurent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Hameurlain, Abdelkader, editor, and Tjoa, A Min, editor

Published
2009
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38. Context-Aware Cache Management in Grid Middleware

Author

Jouanot, Fabrice, d’Orazio, Laurent, Roncancio, Claudia, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Hameurlain, Abdelkader, editor

Published
2008
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40. Distributed Semantic Caching in Grid Middleware

Author

d’Orazio, Laurent, Jouanot, Fabrice, Denneulin, Yves, Labbé, Cyril, Roncancio, Claudia, Valentin, Olivier, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wagner, Roland, editor, Revell, Norman, editor, and Pernul, Günther, editor

Published
2007
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42. From Cloud to Serverless: MOO in the new Cloud epoch

Author

Georgoulakis Misegiannis, Michail, D’orazio, Laurent, Kantere, Verena, National Technical University of Athens [Athens] (NTUA), A Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

International audience; During the last 10 years, the volume of global data has risen more than tenfold. The commercial rise of cloud computing eased the process of storing, processing and managing big data. Recently, the cloud evolved with the emergence of serverless computing platforms that offer an even more abstracted service model. The elasticity of cloud computing creates significant optimization problems, which can be tackled either with a single objective, or as multi-objective opimization problems (MOO). When it comes to data management, the two main MOO problems in a cloud computing environment are query optimization and task scheduling. Some of the techniques used for solving MOO problems in the cloud are the weighted sum model, mathematical-programming based algorithms and genetic algorithms. We propose the presentation of a tutorial that will underline the main MOO problems of a cloud computing environment in regards to data management, and evaluate the use of serverless computing for such problems. The tutorial will offer the audience a better understanding of current MOO challenges and applications in the cloud, while also giving them an overview of different solutions to such problems and the techniques that can be used for solving them. * The supervisors' names are included in alphabetical order, both contributed equally.

Published
2022

45. ASSIST: Article eXtraction and statIstical AnalysiS

Author

Fouillé, Justine, Nguyen, Thi Lan Huong, Alix, Baptiste, Becker, Brett, Rochard, Matthieu, de Ribaupierre, Hélène, d'Orazio, Laurent, A Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), School of Computer Sciences & Informatics [Cardiff], and Cardiff University

Subjects
Data Extraction, Statistical Analysis, Data Analytic, [INFO]Computer Science [cs]
Abstract

International audience; There are fewer female authors than male authors in the field of scientific research. However, there is not yet a system that provides a way to analyze the data that is available, and to backup that claim. This paper illustrates the upgrade of a tool previously made, in order to make it more efficient and add new features. Such new features are the keywords cloud or the new statistical functionality. Sources, references and other information on the article will be displayed for each articles retrieved. Genders of the authors will be determined using a database linking first names to genders, to be able to get accurate statistics on a large number of gathered articles.

Published
2021

49. FRESQUE: A Scalable Ingestion Framework for Secure Range Query Processing on Clouds

Author

van Tran, Hoang, Allard, Tristan, d'Orazio, Laurent, El Abbadi, Amr, A Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Can Tho University [Vietnam] (CTU), Declarative & Reliable management of Uncertain, user-generated Interlinked Data (DRUID), Department of Computer Science [Santa Barbara] (CS-UCSB), University of California [Santa Barbara] (UCSB), University of California-University of California, This work was partially funded by LTC and Pôle d’Excellence Cyber, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Security & PrIvaCY (SPICY), SYSTÈMES LARGE ÉCHELLE (IRISA-D1), University of California [Santa Barbara] (UC Santa Barbara), and University of California (UC)-University of California (UC)

Subjects
[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract

International audience; Performing non-aggregate range queries over encrypted data stored on untrusted clouds has been considered by a large body of work over the last years. However, prior schemes mainly concentrate on improving query performance while the scalability dimension still remains challenging. Due to heavily pre-processing incoming data at a trusted component such as encrypting data and building secure indexes, existing solutions cannot provide a satisfactory ingestion throughput. In this paper, we overcome this limitation by introducing a framework for secure range query processing, FRESQUE, that enables a scalable consumption throughput while still maintaining strong privacy protection for outsourced data. Our experiments on real-world datasets show that FRESQUE can support over 160 thousand record insertions in a second, when running on a 12-computing node cluster. It also significantly outperforms one of the most efficient schemes such as PINED-RQ++ by 43 times on ingestion throughput.

Published
2021

50. Optimizing DICOM data management with NSGA-G

Author

Le, Trung-Dung, Kantere, Verena, Orazio, Laurent, Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), University of Ottawa [Ottawa], d'Orazio, Laurent, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]
Abstract

International audience; Cloud-based systems enable to manage ever-increasing medical data. The Digital Imaging and Communication in Medicine (DI-COM) standard has been widely accepted to store and transfer the medical data, which uses single (row/column) or hybrid data storage technique (row-column). In particular, hybrid systems leverage the advantages of both techniques and allow to take into account various kinds of queries from full records retrieval (online transaction processing) to analytics (online analytical processing) queries. Additionally, the pay-as-you-go model and elasticity of cloud computing raise an important issue regarding to Multiple Objective Optimization (MOO) to find a data configuration according to users preferences such as storage space, processing response time, monetary cost, quality, etc. In such a context, the considerable space of solutions in MOO leads to generation of Pareto-optimal front with high complexity. Pareto-dominated based Multiple Objective Evolutionary Algorithms are often used as an alternative solution, e.g., Non-dominated Sorting Genetic Algorithms (NSGA) which provide less computational complexity. This paper presents NSGA-G, an NSGA based on Grid Partitioning to improve the complexity and quality of current NSGAs and to obtain efficient storage and querying of DICOM hybrid data. Experimental results on DTLZ test problems [10] and DICOM hybrid data prove the relevance of the proposed algorithm.

Published
2019

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