QoE Driven Server Selection for VoD in the Cloud

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Wang, C (Autor) Outra; Kim, H (Autor) Outra; Ricardo Morla (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings - 2015 IEEE 8th International Conference on Cloud Computing, CLOUD 2015

Páginas: 917-924

8th IEEE International Conference on Cloud Computing, CLOUD 2015

27 June 2015 through 2 July 2015

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 10 Citações

Outras Informações

ID Authenticus: P-00G-V40

DOI: 10.1109/cloud.2015.125

Abstract (EN): In commercial Video-on-Demand (VoD) systems, user's Quality of Experience (QoE) is the key factor for user satisfaction. In order to improve user's QoE, VoD providers replicate popular videos in geo-distributed Cloud and deploy cache servers close to users. Generally, the VoD provider selects a server for the user request according to the user's location. Usually geographically closely located servers would provide lower network delay. However, the performance of VoD servers deployed in cloud virtual machines (VM) depends not only on the network delay but also resource contention due to other VMs and highly dynamic user demands. Thus, QoE offered by the server varies greatly over time as user demands and network traffic fluctuate regardless of the location. Selecting a server close to users sometimes reduces the network delay but cannot guarantee QoE in general. We believe that end-users have the best perception of server performance in terms of their QoE rather than the servers themselves. What user perceives incorporate performance of all elements, such as network delay and server response time in VoD service. We propose VoD server selection schemes that dynamically select servers according to user's QoE feedback. We integrate our server selection schemes with Dynamic Adaptive Streaming over HTTP (DASH) clients and evaluate our system both in simulation and in Google Cloud. Results show our system improves user QoE up to 20% compared to existing solutions(1).

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8