SAMOVAR UMR 5157

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Accueil > Productions scientifiques > Thèses SAMOVAR > Thèses 2017

« Allocation de ressources pour un cloud green et distribué »

« Allocation de ressources pour un cloud green et distribué »

L’Ecole doctorale EDITE - Ecole doctorale informatique, télécommunications et électronique et Télécom SudParis avec le Laboratoire de recherche SAMOVAR -
présentent l’AVIS DE SOUTENANCE de Monsieur Ehsan AHVAR

Autorisé à présenter ses travaux en vue de l’obtention du Doctorat de Télécom SudParis avec l’Université Paris 6 en :
Informatique et Télécommunications

Titre : « Allocation de ressources pour un cloud green et distribué »

Quand:le 9 janvier 2017 à 9:30 - Salle A003

Où : Télécom SudParis - 9 Rue Charles Fourier, 91000 Évry

Directeur de thèse : Noël CRESPI - Professeur

Membres du jury :
- Rapporteurs :

Ramin YAHYAPOUR Professor - Georg-August Universität Göttingen - Allemagne
Bahman JAVADI Senior Lecturer - Western Sydney University - Australie

- Examinateurs :

Guy PUJOLLE Professeur - Université Paris 6
Walid BEN AMEUR Professeur - Télécom SudParis
Imen Grida BEN YAIA Ingénieure - Orange Labs Networks - France

Résumé :
L’objectif de cette thèse est de présenter de nouveaux algorithmes de placement de machines virtuelles (VMs) à fin d’optimiser le coût et les émissions de carbone dans les Clouds distribués. La thèse se concentre d’abord sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. La thèse comprend deux principales parties : la première propose, développe et évalue les algorithmes de placement statiques de VMs (où un premier placement d’une VM détient pendant toute la durée de vie de la VM). La deuxième partie propose des algorithmes de placement dynamiques de VMs où le placement initial de VM peut changer dynamiquement (par exemple, grâce à la migration de VMs et à leur consolidation).

Abstract :
Virtual machine (VM) placement (i.e., resource allocation) method has a direct effect on both cost and carbon emission. Considering the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of VMs for cost and carbon efficiency even more critical and complex than in centralized clouds.
The goal of this thesis is to present new VM placement algorithms to optimize cost and carbon emission in a distributed cloud. It first focuses on cost efficiency in distributed clouds and, then, extends the goal to optimization of both cost and carbon emission at the same time. Thesis includes two main parts. The first part of thesis proposes, develops and evaluates static VM placement algorithms to reach the mentioned goal where an initial placement of a VM holds throughout the lifetime of the VM. The second part proposes dynamic VM placement algorithms where the initial placement of VMs is allowed to change (e.g., through VM migration and consolidation).
The first contribution is a survey of the state of the art on cost and carbon emission resource allocation in distributed cloud environments. The second contribution targets the challenge of optimizing inter-DC communication cost for large-scale tasks and proposes a Network-Aware Cost-Efficient Resource allocation method, called NACER, for distributed clouds. The goal is to minimize the network communication cost of running a task in a distributed cloud by selecting the DCs to provision the VMs in such a way that the total network distance (hop count or any reasonable measure) among the selected DCs is minimized. The third contribution proposes a Network-Aware Cost Efficient VM Placement method (called NACEV) for Distributed Clouds. NACEV is an extended version of NACER. While NACER only considers inter-DC communication cost, NACEV optimizes both communication and computing cost at the same time and also proposes a mapping algorithm to place VMs on Physical Machines (PMs) inside of the selected DCs. NACEV also considers some aspects such as heterogeneity of VMs, PMs and switches, variety of energy prices, multiple paths between PMs, effects of workload on cost (energy consumption) of cloud devices (i.e., switches and PMs) and also heterogeneity of energy model of cloud elements. The forth contribution presents a Cost and Carbon Emission-Efficient VM Placement Method (called CACEV) for green distributed clouds. CACEV is an extended version of NACEV. In addition to cost efficiency, CACEV considers carbon emission efficiency and green distributed clouds. It is a VM placement algorithm for joint optimization of computing and network resources, which also considers price, location and carbon emission rate of resources. It also, unlike previous contributions of thesis, considers IaaS Service Level Agreement (SLA) violation in the system model.
To get a better performance, the fifth contribution proposes a dynamic Cost and Carbon Emission-Efficient VM Placement method (D-CACEV) for green distributed clouds. D-CACEV is an extended version of our previous work, CACEV, with additional figures, description and also live VM migration mechanisms. We show that our joint VM placement-reallocation mechanism can constantly optimize both cost and carbon emission at the same time in a distributed cloud.