Geographical Load Balancing across Green Datacenters

Giovanni Neglia 1 Matteo Sereno 2 Giuseppe Bianchi 3
1 MAESTRO - Models for the performance analysis and the control of networks
CRISAM - Inria Sophia Antipolis - Méditerranée
2 Dipartimento Informatica Universita Torino
3 Dipartimento di ingegneria elettronica, Università di Roma Tor Vergata
Abstract : "Geographic Load Balancing" is a strategy for reducing the energy cost of data centers spreading across different terrestrial locations. In this paper, we focus on load balancing among micro-datacenters powered by renewable energy sources. We model via a Markov Chain the problem of scheduling jobs by prioritizing datacenters where renewable energy is currently available. Not finding a convenient closed form solution for the resulting chain, we use mean field techniques to derive an asymptotic approximate model which instead is shown to have an extremely simple and intuitive steady state solution. After proving, using both theoretical and discrete event simulation results, that the system performance converges to the asymptotic model for an increasing number of datacenters, we exploit the simple closed form model's solution to investigate relationships and trade-offs among the various system parameters.
Keywords : Energy Efficiency Data Centers Geographical load balancing Fluid Models
Type de document :
Article dans une revue
ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery, 2016, Special issue on the 2016 Greenmetrics Workshop, 44 (2), pp.64 - 69. 〈10.1145/3003977.3003998〉
Liste complète des métadonnées
https://hal.inria.fr/hal-01413636
Contributeur : Giovanni Neglia <>
Soumis le : samedi 10 décembre 2016 - 12:44:55
Dernière modification le : lundi 30 avril 2018 - 14:30:10
Document(s) archivé(s) le : mardi 28 mars 2017 - 00:27:51

Fichiers

papero.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA

Citation

Giovanni Neglia, Matteo Sereno, Giuseppe Bianchi. Geographical Load Balancing across Green Datacenters. ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery, 2016, Special issue on the 2016 Greenmetrics Workshop, 44 (2), pp.64 - 69. 〈10.1145/3003977.3003998〉. 〈hal-01413636〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

183

Téléchargements de fichiers

80

Contact


archive-ouverte@inria.fr