Mahasen: Distributed Storage Resource Broker

Abstract : Modern day systems are facing an avalanche of data, and they are being forced to handle more and more data intensive use cases. These data comes in many forms and shapes: Sensors (RFID, Near Field Communication, Weather Sensors), transaction logs, Web, social networks etc. As an example, weather sensors across the world generate a large amount of data throughout the year. Handling these and similar data require scalable, efficient, reliable and very large storages with support for efficient metadata based searching. This paper present Mahasen, a highly scalable storage for high volume data intensive applications built on top of a peer-to-peer layer. In addition to scalable storage, Mahasen also supports efficient searching, built on top of the Distributed Hash table (DHT)
Type de document :
Communication dans un congrès
Ching-Hsien Hsu; Xiaoming Li; Xuanhua Shi; Ran Zheng. 10th International Conference on Network and Parallel Computing (NPC), Sep 2013, Guiyang, China. Springer, Lecture Notes in Computer Science, LNCS-8147, pp.380-392, 2013, Network and Parallel Computing. 〈10.1007/978-3-642-40820-5_32〉
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01513774
Contributeur : Hal Ifip <>
Soumis le : mardi 25 avril 2017 - 14:33:40
Dernière modification le : mardi 25 avril 2017 - 14:35:49
Document(s) archivé(s) le : mercredi 26 juillet 2017 - 14:13:43

Fichier

978-3-642-40820-5_32_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

K. Perera, T. Kishanthan, H. Perera, D. Madola, Malaka Walpola, et al.. Mahasen: Distributed Storage Resource Broker. Ching-Hsien Hsu; Xiaoming Li; Xuanhua Shi; Ran Zheng. 10th International Conference on Network and Parallel Computing (NPC), Sep 2013, Guiyang, China. Springer, Lecture Notes in Computer Science, LNCS-8147, pp.380-392, 2013, Network and Parallel Computing. 〈10.1007/978-3-642-40820-5_32〉. 〈hal-01513774〉

Partager

Métriques

Consultations de la notice

33

Téléchargements de fichiers

42