Title :

A Multi-start Local Search Heuristic for an Energy Efficient VMs Assignment on top of the OpenNebula Cloud Manager

Author(s) :

Kessaci, Yacine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Nouredine, Melab [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Journal title :

Future Generation Computer Systems

Pages :

237-256

Publisher :

Elsevier

Publication date :

2013-08-06

ISSN :

0167-739X

English keyword(s) :

Resource allocation
Cloud computing
Energy-aware scheduling
Multi-start local search
OpenNebula
Cloud manager

HAL domain(s) :

Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

English abstract : [en]

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a large scale cloud. Minimizing energy consumption can significantly reduce the amount of energy bills, ...
Show more >Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a large scale cloud. Minimizing energy consumption can significantly reduce the amount of energy bills, and the greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present an Energy-aware Multi-start Local Search algorithm (EMLS-ONC) that optimizes the energy consumption of an OpenNebula based Cloud. Moreover, we propose a Pareto Multi-Objective version of the EMLS-ONC called EMLS-ONC-MO dealing with both the energy consumption and the Service Level Agreement (SLA). The objective is to find a Pareto tradeoff between reducing the energy consumption of the cloud while preserving the performance of Virtual Machines (VMs). The different schedulers have been experimented using different arrival scenarios of VMs and different hardware configurations (artificial and real). The results show that EMLS-ONC and EMLS-ONC-MO outperform the other energy- and performance-aware algorithms in addition to the one provided in OpenNebula by a significant margin on the considered criteria. Besides, EMLS-ONC and EMLS-ONC-MO are proved to be able to assign at least as many VMs as the other algorithms.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL