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Doctorat de FERREIRA LEITE Alessandro
Equipe : Systèmes Parallèles

A User-Centered and Autonomic Multi-Cloud Architecture for High Performance Computing Applications

Début le 01/10/2012
Direction : EISENBEIS, Christine

Ecole doctorale : ED STIC 580
Etablissement d'inscription : Université Paris-Saclay

Lieu de déroulement : ORSAY & Brasilia

Soutenue le 02/12/2014 devant le jury composé de :
Directrice de thèse :
- Christine Eisenbeis, INRIA/LRI/U-PSud
- Alba Melo, Université de Brasília

Co-directeur de thèse :
- Claude Tadonki, Mines ParisTech

Rapporteurs :
- Christophe Cérin, Université Paris 13
- Jean-Louis Pazat, INSA Rennes

Examinatrices :
- Christine Froidevaux, Université Paris-Sud XI
- Célia Ghedini Ralha, Université de Brasília
- Christine Morin, INRIA/IRISA

Activités de recherche :

Résumé :
Cloud computing has been seen as an option to execute high performance
computing (HPC) applications. While traditional HPC platforms such as grid
and supercomputers offer a stable environment in terms of failures,
performance, and number of resources, cloud computing offers on-demand
resources generally with unpredictable performance at low financial cost.
Furthermore, in cloud environment, failures are part of its normal
operation. To overcome the limits of a single cloud, clouds can be combined,
forming a cloud federation often with minimal additional costs for the
users. A cloud federation can help both cloud providers and cloud users to
achieve their goals such as to reduce the execution time, to achieve minimum
cost, to increase availability, to reduce power consumption, among others.
Hence, cloud federation can be an elegant solution to avoid over
provisioning, thus reducing the operational costs in an average load
situation, and removing resources that would otherwise remain idle and
wasting power consumption, for instance. However, cloud federation increases
the range of resources available for the users. As a result, cloud or system
administration skills may be demanded from the users, as well as a
considerable time to learn about the available options. In this context,
some questions arise such as: (a) which cloud resource is appropriate for a
given application? (b) how can the users execute their HPC applications with
acceptable performance and financial costs, without needing to re-engineer
the applications to fit clouds' constraints? (c) how can non-cloud
specialists maximize the features of the clouds, without being tied to a
cloud provider? and (d) how can the cloud providers use the federation to
reduce power consumption of the clouds, while still being able to give
service-level agreement (SLA) guarantees to the users? Motivated by these
questions, this thesis presents a SLA-aware application consolidation
solution for cloud federation. Using a multi-agent system (MAS) to negotiate
virtual machine (VM) migrations between the clouds, simulation results show
that our approach could reduce up to 46% of the power consumption, while
trying to meet performance requirements. Using the federation, we developed
and evaluated an approach to execute a huge bioinformatics application at
zero-cost. Moreover, we could decrease the execution time in 22.55% over the
best single cloud execution. In addition, this thesis presents a cloud
architecture called Excalibur to auto-scale cloud-unaware application.
Executing a genomics workflow, Excalibur could seamlessly scale the
applications up to 11 virtual machines, reducing the execution time by 63%
and the cost by 84% when compared to a user's configuration. Finally, this
thesis presents a product line engineering (PLE) method to handle the
variabilities of infrastructure-as-a-service (IaaS) clouds, and an autonomic
multi-cloud architecture that uses this method to configure and to deal with
failures autonomously. The PLE method uses extended feature model (EFM) with
attributes to describe the resources and to select them based on users'
objectives. Experiments realized with two different cloud providers show
that using the proposed model, the users could execute their application in
a cloud federation environment, without needing to know the variabilities
and constraints of the clouds.