Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients

Abstract : The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world Cloud client application traces. We also present an overall evaluation of this approach , its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.
Type de document :
Article dans une revue
Journal of Grid Computing, Springer Verlag, 2011, 9, pp.49 - 64. 〈10.1007/s10723-010-9178-4〉
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01426826
Contributeur : Eddy Caron <>
Soumis le : jeudi 5 janvier 2017 - 09:12:14
Dernière modification le : vendredi 20 avril 2018 - 15:44:26

Licence


Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Pas de modification 4.0 International License

Lien texte intégral

Identifiants

Collections

Citation

Eddy Caron, Frédéric Desprez, Adrian Muresan. Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients. Journal of Grid Computing, Springer Verlag, 2011, 9, pp.49 - 64. 〈10.1007/s10723-010-9178-4〉. 〈hal-01426826〉

Partager

Métriques

Consultations de la notice

288