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6 results on '"Bruno Donassolo"'

1. Online Reconfiguration of IoT Applications in the Fog: The Information-Coordination Trade-off

Author

Arnaud Legrand, Panayotis Mertikopoulos, Bruno Donassolo, Ilhem Fajjari, Orange Labs [Chatillon], Orange Labs, Performance analysis and optimization of LARge Infrastructures and Systems (POLARIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), GRID5000, ANR-16-CE33-0004,ORACLESS,Stratégies adaptatives d'allocation des ressources dans les réseaux sans fil dynamiques(2016), Laboratoire d'Informatique de Grenoble (LIG), Université Grenoble Alpes (UGA)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Criteo AI Lab, and Criteo [Paris]

Subjects
020203 distributed computing, Computer science, business.industry, Online learning, Quality of service, Distributed computing, Testbed, Internet of Things, Control reconfiguration, Cloud computing, 02 engineering and technology, Online scheduling, Scheduling (computing), Computational Theory and Mathematics, Hardware and Architecture, Signal Processing, Reconfiguration, 0202 electrical engineering, electronic engineering, information engineering, Fog computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, 5G
Abstract

The evolution of the Internet of Things (IoT) is driving an extraordinary growth of traffic and processing demands, persuading 5G players to change their infrastructures. In this context, Fog computing emerges as a potential solution, providing nearby resources to run IoT applications. However, the Fog raises several challenges which hinders its adoption. In this article, we consider the reconfiguration problem , i.e., how to dynamically adapt the placement of IoT applications running on the Fog, depending on application needs and evolution of resource usage. We propose and evaluate a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments in a realistic testbed, we demonstrate that the performance strongly depends on the quality and availability of information from both Fog infrastructure and IoT applications. This information mainly concerns the application’s resource usage (estimated by the user during the design of the application) and the availability of resources in the infrastructure (collected by commercial off-the-shelf monitoring tools). Finally, we show that a reactive and greedy strategy, which relies on this additional information, can overcome the performance of state-of-the-art online learning algorithms, even in a scenario with inaccurate information.

Published
2022
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2. Load Aware Provisioning of IoT Services on Fog Computing Platform

Author

Ilhem Fajjari, Bruno Donassolo, Panayotis Mertikopoulos, Arnaud Legrand, Instituto de Informática da UFRGS (UFRGS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Orange Labs [Issy les Moulineaux], France Télécom, Performance analysis and optimization of LARge Infrastructures and Systems (POLARIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
IoT, Edge device, business.industry, Computer science, Distributed computing, 05 social sciences, application placement, 050801 communication & media studies, Provisioning, Cloud computing, Fog Computing, Load balancing (computing), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 0508 media and communications, service provisioning, Analytics, Software deployment, 0502 economics and business, [INFO]Computer Science [cs], 050211 marketing, Orchestration (computing), business
Abstract

International audience; To support the drastically increasing traffic generated by devices at the edge of the network, 5G players are urged to rethink their infrastructure design. Unfortunately, conventional Cloud infrastructures struggle to adapt to the huge volume of traffic. In this context, Fog computing has been developed to bridge Cloud data centers and edge devices servicing a multitude of heterogeneous devices. These nearby nodes offer analytics and data storage capabilities increasing considerably the capacity of the infrastructure. However, provisioning IoT applications on such a heterogeneous infrastructure, while meeting their stringent requirements is extremely challenging. In this paper, we study the Fog service provisioning issue in a practical manner. In this regard , we propose a novel strategy, which we call GO-FSP. GO-FSP optimizes the placement of IoT application components while coping with their strict performance requirements. To do so, we first propose an Integer Linear Programming (ILP) formulation for the IoT application provisioning problem. The latter targets to minimize the deployment cost while ensuring a load balancing between heterogeneous devices. Then, a GRASP-based approach is proposed to achieve the aforementioned objectives. Finally, we make use of the FITOR orchestration system to evaluate the performance of our solution under real conditions. Obtained results show that our scheme outperforms the related strategies.

Published
2019
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3. Fog Based Framework for IoT Service Provisioning

Author

Bruno Donassolo, Panayotis Mertikopoulos, Ilhem Fajjari, Arnaud Legrand, Orange Labs [Chatillon], Orange Labs, Performance analysis and optimization of LARge Infrastructures and Systems (POLARIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and ANR-16-CE33-0004,ORACLESS,Stratégies adaptatives d'allocation des ressources dans les réseaux sans fil dynamiques(2016)

Subjects
IoT, business.industry, Computer science, Distributed computing, application placement, 020206 networking & telecommunications, Context (language use), Provisioning, Cloud computing, 02 engineering and technology, Fog Computing, Service provisioning, Resource (project management), service provisioning, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Orchestration (computing), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Internet of Things, business, Edge computing
Abstract

International audience; To this day, the Internet of Things (IoT) continues its explosive growth. Nevertheless, with the exceptional evolution of traffic demand, existing infrastructures are struggling to resist. In this context, Fog computing is shaping the future of IoT applications. It offers nearby computational, networking and storage resources to respond to the stringent requirements of these applications. However, despite its several advantages, Fog computing raises new challenges which slow its adoption down. Hence, there is a lack of practical solutions to enable the exploitation of this novel concept. To deal with this shortcoming, we propose FITOR, an orchestration system for IoT applications in the Fog environment. This solution builds a realistic Fog environment while offering efficient orchestration mechanisms. In order to optimize the provisioning of Fog-Enabled IoT applications , FITOR relies on O-FSP, an optimized fog service provisioning strategy which aims to minimize the provisioning cost of IoT applications, while meeting their requirements. Based on extensive experiments, the results obtained show that O-FSP optimizes the placement of IoT applications and outperforms the related strategies in terms of i) provisioning cost ii) resource usage and iii) acceptance rate.

Published
2019
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4. Demo: Fog Based Framework for IoT Service Orchestration

Author

Arnaud Legrand, Bruno Donassolo, Ilhem Fajjari, and Panayotis Mertikopoulos

Subjects
business.industry, Computer science, Fog computing, 0202 electrical engineering, electronic engineering, information engineering, Bandwidth (computing), 020206 networking & telecommunications, 020201 artificial intelligence & image processing, 02 engineering and technology, Orchestration (computing), Architecture, Internet of Things, business, Computer network
Abstract

In recent years, Fog computing paradigm has received the attention of academic and industrial communities. By offering nearby computational, storage and network resources, this new architecture deals with the explosion of IoT (Internet of Things) traffic while responding to the stringent requirements of new applications. Unfortunately, as of today, there is a lack of practical solutions to enable the exploitation of this novel paradigm. To deal with this shortcoming, this demo gives an insight into FITOR, our proposed orchestration system for IoT applications in Fog. Our solution makes use of both Grid5000 [1] and FIT/IoT-LAB [2] to build a realistic fog environment. FITOR is responsible for the orchestration of micro-service based IoT applications while making use of a holistic monitoring of the fog infrastructure.

Published
2019
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5. Non-cooperative Scheduling Considered Harmful in Collaborative Volunteer Computing Environments

Author

Cl´udio Geyer, Arnaud Legrand, Bruno Donassolo, Instituto de Informática da UFRGS (UFRGS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Middleware efficiently scalable (MESCAL), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Collaborative software, [INFO.INFO-GT]Computer Science [cs]/Computer Science and Game Theory [cs.GT], business.industry, Computer science, Distributed computing, 02 engineering and technology, BOINC Credit System, Scheduling (computing), Shared resource, 020204 information systems, Server, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Instruction cycle, Internetworking, Game theory
Abstract

International audience; Advances in inter-networking technology and computing components have enabled Volunteer Computing (VC) systems that allows volunteers to donate their computers' idle CPU cycles to a given project. BOINC is the most popular VC infrastructure today with over 580,000 hosts that deliver over 2,300 TeraFLOP per day. BOINC projects usually have hundreds of thousands of independent tasks and are interested in overall throughput. Each project has its own server which is responsible for distributing work units to clients, recovering results and validating them. The BOINC scheduling algorithms are complex and have been used for many years now. Their efficiency and fairness have been assessed in the context of throughput oriented projects. Yet, recently, burst projects, with fewer tasks and interested in response time, have emerged. Many works have proposed new scheduling algorithms to optimize individual response time but their use may be problematic in presence of other projects. In this article we show that the commonly used BOINC scheduling algorithms are unable to enforce fairness and project isolation. Burst projects may dramatically impact the performance of all other projects (burst or non-burst). To study such interactions, we perform a detailed, multi-player and multi-objective game theoretic study. Our analysis and experiments provide a good understanding on the impact of the different scheduling param- eters and show that the non-cooperative optimization may result in inefficient and unfair share of the resources.

Published
2011
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6. Fast and scalable simulation of volunteer computing systems using SimGrid

Author

Arnaud Legrand, Pedro Velho, Henri Casanova, Bruno Donassolo, Instituto de Informática da UFRGS (UFRGS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Information and Computer Sciences [Hawaii] (ICS), University of Hawai‘i [Mānoa] (UHM), Middleware efficiently scalable (MESCAL), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), ACM, Grid'5000, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Distributed computing, Simulation modeling, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Commodity computing, 020206 networking & telecommunications, 02 engineering and technology, Set (abstract data type), Volunteer computing, Scalability, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), 020201 artificial intelligence & image processing, Research questions, Internetworking
Abstract

International audience; Advances in internetworking technology and the decreasing cost-performance ratio of commodity computing components have enabled Volunteer Computing (VC). VC platforms aggregate tens or hundreds of thousands of hosts. These hosts are typically volatile, which raises di cult research questions. Most research in this area relies on simulation. The main issue when developing VC simulators is scalability: How to perform simulations of large-scale VC platforms with reasonable amounts of memory and reasonably fast? To achieve scalability, state-of-the-art VC simulators employ simplistic simulation models and/or target on narrow platform and application scenarios. In this paper we enable VC simulations using the general-purpose SimGrid simulation framework, which provides signi cantly more realistic and exible simulation capabilities than the aforementioned simulators. Our key contribution is a set of improvements to SimGrid so that it brings these bene ts to VC simulations while achieving good scalability.

Published
2010
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