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19 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

6. 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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10. 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

11. 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

12. 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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13. 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

15. 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

17. 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

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