Author, as appears in the article.: Chaabouni, R.; Sánchez-Artigas, M.; García-López, P.; Pàmies-Juàrez, L.

Department: Enginyeria Informàtica i Matemàtiques

URV's Author/s: CHAABOUNI -, RAHMA; SANCHEZ ARTIGAS, MARC; GARCÍA LÓPEZ, PEDRO ANTONIO; PAMIES JUAREZ, LLUIS

Abstract: Users are unceasingly relying on personal clouds (like Dropbox, Box, etc) to store, edit and retrieve their files stored in remote servers. These systems generally follow a client–server model to distribute the files to end-users. This means that they require a huge amount of bandwidth to meet the requirements of their clients. Personal clouds with limited bandwidth budget can benefit from the upload speed of the clients interested in the same content to improve the quality of service. This can be done by introducing a peer-to-peer protocol, BitTorrent for instance, when the load on a certain content becomes high. The main challenge is to decide when to switch to BitTorrent and how to allocate the cloud's available bandwidth to the different clients. In this paper, we propose an algorithm for the allocation of the cloud׳s bandwidth. Based on the current load and the predefined quality of service constraints, the algorithm identifies the most suitable protocol for each swarm and provides the corresponding bandwidth allocation. We validate the algorithm using a real trace of the Ubuntu One system and the results show important gains in the download times experienced by the clients.

Research group: Arquitectura i Serveis Telemàtics

Thematic Areas: Mathematics Matemáticas Matemàtiques

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 1084-8045

Author identifier: 0000-0002-9196-1397; 0000-0002-9700-7318; 0000-0002-9848-1492;

Record's date: 2017-06-14

Last page: 71

Journal volume: 65

Papper version: info:eu-repo/semantics/submittedVersion

Link to the original source: http://www.psicothema.com/psicothema.asp?id=4351

Funding program: plan; Servicios Cloud y Redes Comunitarias; Cloud4Comm; TIN-2013-47245-C2-2-R european; IOStack: Software-Defined Storage for Big Data; IOSTACK; 644182

Article's DOI: 10.7334/psicothema2016.113

Entity: Universitat Rovira i Virgili

Journal publication year: 2016

First page: 48

Publication Type: Article Artículo Article