News POLARIS Bootcamp (May 2019)

POLARIS Bootcamp (May 2019)

Arnaud LEGRAND 2019/05/24 2019/06/14News

Internal use: the pad where we took notes on the organization.

Long talks:

Arnaud Legrand: Introduction to Bayesian sampling (sources)
Bary Pradelski: Preferences, Utilities and Identity Economics
Nicolas Gast: Bandits and online learning (sources)
Vincent Danjean: Continuous integration
Bruno Gaujal: Markov Decision Processs and Q-learning
Guillaume Huard: Memory and cache issues
Jonatha Anselmi: Scalable and efficient load balancing
Jean-Marc Vincent: Enseignement de l’informatique: réformes en cours et à venir

2 minute madness:

Olivier Bilenne
Alexis Janon
Pedro Bruel
Vitalii Emelianov
Benjamin Roussillon
Stéphan Plassart
Tom Cornebize
Christian Heinrich: Org-mode cool unknown features

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News
- DATAMOVE/POLARIS BBQ 2019 2019/06/14
- POLARIS Bootcamp (May 2019) 2019/05/24
- slides of Andras Gyorgy 2016/01/15
- kickoff meeting on Feb. 11 2016/01/13

November 2020
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2	3	4 PhD defense of Bruno Donassolo: IoT Orchestration in the Fog November 4, 2020 2:00 pm – 5:00 pm Bâtiment IMAG (amphitheater) Saint-Martin-d'Hères, 38400 France Map I am pleased to invite you to my thesis defense entitled: « IoT Orchestration in the Fog ». This thesis has been conducted in the context of the <I/O lab>, a joint lab between Inria and Orange Labs. The defense will be in English and will take place in the auditorium of the IMAG building (700 Avenue Centrale, 38401 Saint-Martin-d'Hères). Video-conference link: https://meet.univ-grenoble-alpes.fr/b/bru-m9j-mfv You are also invited to share a “pot” following the defense (if the sanitary conditions allow) The members of jury are: Mme E. Veronica BELMEGA, Maître de conférences, Université CY Cergy Paris, Reviewer M. Adrien LEBRE, Professeur des Universités, IMT Atlantique, Reviewer M. Frédéric DEPREZ, Directeur de recherche, INRIA Grenoble Rhône-Alpes, Examiner Mme Nathalie MITTON, Directrice de recherche, Inria Lille-Nord Europe, Examiner M. Ola ANGELSMARK, Ingénieur de recherche, Ericsson Research, Examiner M. Arnaud LEGRAND, Directeur de recherche, CNRS, Supervisor M. Panayotis MERTIKOPOULOS, Chargé de recherche, CNRS, Co-supervisor Mme Ilhem FAJJARI, Ingénieur de recherche, Orange Labs, Co-supervisor Abstract: Internet of Things (IoT) continues its evolution, causing a drastically growth of traffic and processing demands. Consequently, 5G players are urged to rethink their infrastructures. In this context, Fog computing bridges the gap between Cloud and edge devices, providing nearby devices with analytics and data storage capabilities, increasing considerably the capacity of the infrastructure. However, the Fog raises several challenges which decelerate its adoption. Among them, the orchestration is crucial, handling the life-cycle management of IoT applications. In this thesis, we are mainly interested in: i) the provisioning problem, i.e., placing multi-component IoT applications on the heterogeneous Fog infrastructure; and ii) the reconfiguration problem, i.e., how to dynamically adapt the placement of applications, depending on application needs and evolution of resource usage. To perform the orchestration studies, we first propose FITOR, an orchestration system for IoT applications in the Fog environment. This solution addresses the lack of practical Fog solutions, creating a realistic environment on which we can evaluate the orchestration proposals. We study the Fog service provisioning issue in this practical environment. In this regard, we propose two novel strategies, O-FSP and GO-FSP, which optimize the placement of IoT application components while coping with their strict performance requirements. To do so, we first propose an Integer Linear Programming formulation for the IoT application provisioning problem. Based on extensive experiments, the results obtained show that the proposed strategies are able to decrease the provisioning cost while meeting the application requirements. Finally, we tackle the reconfiguration problem, proposing and evaluating a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments, we demonstrate that the performance strongly depends on the quality and availability of information from Fog infrastructure and IoT applications. In addition, we show that a reactive and greedy strategy can overcome the performance of state-of-the-art online learning algorithms, as long as the strategy has access to a little extra information. Read more	5	6	7	8
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