Energy Efficient Traffic Engineering in Software Defined Networks [Radu Carpa. Thèse]

Soutenance de Thèse de Radu Carpa

Jeudi 26 Octobre. Amphi B. 14h00

Résumé

Ce travail a pour but d’améliorer l’efficacité énergétique des réseaux en éteignant un sous-ensemble de liens par une approche SDN. Nous nous différencions des nombreux travaux de ce domaine par une réactivité accrue aux variations des conditions réseaux. Cela a été rendu possible grâce à une complexité calculatoire réduite et une attention particulière au surcoût induit par les échanges de données.
L’architecture logicielle “SegmenT Routing based Energy Efficient Traffic Engineering” (STREETE) que nous proposons repose sur un re-routage dynamique du trafic en fonction de la charge du réseau. Grace à des méthodes d’équilibrage de charge, nous obtenons un placement presque optimal des flux dans le réseau.
STREETE a été validé sur une vraie plateforme SDN. Cela nous a permis de donner des indications sur des améliorations à prendre en compte afin d’éviter des instabilités causées par des basculements incontrôlés des flux réseau entre des chemins alternatifs.

Membres du Jury

  • Andrzej DUDA, Professeur – Grenoble INP-Ensimag (Examinateur)
  • Frédéric GIROIRE, Chargé de Recherches – CNRS Sophia Antipolis (Examinateur)
  • Brigitte JAUMARD, Professeure – Concordia University, Canada (Rapporteure)
  • Béatrice PAILLASSA, Professeure – Institut national polytechnique de Toulouse (Rapporteure)
  • Laurent LEFEVRE, Chargé de Recherches – Inria ENS Lyon (Directeur)
  • Olivier GLUCK, Maître de Conférences – UCBL Lyon1 (Co-encadrant)

ANR MapReduce

This project is devoted to using MapReduce programming paradigm on clouds and hybrid infrastructures. Partners: Argonne National Lab (USA), the University of Illinois at Urbana Champaign (USA), the UIUC-INRIA Joint Lab on Petascale Computing, IBM France, IBCP, MEDIT (SME) and the GRAAL/AVALON INRIA project-team.

ANR MapReduce

This project aims to overcome the limitations of current Map-Reduce frameworks such as Hadoop, thereby enabling highly-scalable Map-Reduce-based data processing on various physical platforms such as clouds, desktop grids, or on hybrid infrastructures built by combining these two types of infrastructures.To meet this global goal, several critical aspects will be investigated. Data storage and sharing architecture. First, we will explore advanced techniques for scalable, high-throughput, concurrency-optimized data and metadata management, based on recent preliminary contributions of the partners. Scheduling. Second, we will investigate various scheduling issues related to large executions of Map-Reduce instances. In particular, we will study how the scheduler of the Hadoop implementation of Map-Reduce can scale over heterogeneous platforms; other issues include dynamic data replication and fair scheduling of multiple parallel jobs. Fault tolerance and security. Finally, we intend to explore techniques to improve the execution of Map-Reduce applications on large-scale infrastructures with respect to fault tolerance and security.

Our global goal is to explore how combining these techniques can improve the behavior of Map-Reduce-based applications on the target large-scale infrastructures. To this purpose, we will rely on recent preliminary contributions of the partners associated in this project, illustrated though the following main building blocks. BlobSeer, a new approach to distributed data management being designed by the KerData team from INRIA Rennes – Bretagne Atlantique to enable scalable, efficient, fine-grain access to massive, distributed data under heavy concurrency. BitDew, a data-sharing platform being currently designed by the GRAAL team from INRIA Grenoble – Rhône-Alpes at ENS Lyon, with the goal of exploring the specificities of desktop grid infrastructures. Nimbus, a reference open source cloud management toolkit developed at the University of Chicago and Argonne National Laboratory (USA) with the goal of facilitating the operation of clusters as Infrastructure-as-a-Service (IaaS) clouds.

More information on the MapReduce web site.

ANR COOP

Multi-level Cooperative Resource Management

ANR COOP

The problem addressed by the COOP project (Dec. 2009 — May 2013) was to reconcile two layers – Programming Model Frameworks (PMF) and Resource Management Systems (RMS) – with respect to a number of tasks that they both try to handle independently. PMF needs to have a knowledge of resources to select the most efficient transformation of abstract programming concepts into executable ones. However, the actual management of resources is done by RMS in an opaque way, based on a simple abstraction of applications.

More details are available on the ANR COOP website.