Analysis of Resource Sharing and Cache Management in Scalable Video-on-Demand

Authors:
Bashar Qudah;Nabil J. Sarhan
Affiliations:
Wayne State University, USA;Wayne State University, USA
Venue:
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
Year:
2006

Citing 0
Cited 8

Analysis of waiting-time predictability in scalable media streaming

Proceedings of the 15th international conference on Multimedia
Scalable delivery and pricing of streaming media with advertisements

Proceedings of the 15th international conference on Multimedia
Can VoD streaming service co-exist with other services on a VM-based virtualized computing platform?

CHINA HPC '07 Proceedings of the 2007 Asian technology information program's (ATIP's) 3rd workshop on High performance computing in China: solution approaches to impediments for high performance computing
Performance and Waiting-Time Predictability Analysis of Design Options in Cost-Based Scheduling for Scalable Media Streaming

MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
An effective batching scheme utilizing the segment interval time for wireless IPTV

CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
An enhanced periodic broadcasting scheme for VoD with heterogeneous receivers

CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Efficient delivery of on-demand video streams to heterogeneous receivers

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Detailed performance and waiting-time predictability analysis of scheduling options in on-demand video streaming

Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009

Quantified Score

Hi-index	0.00

Visualization

Abstract

The required real-time and high-rate transfers for multimedia data severely limit the number of requests that can be serviced by Video-on-Demand (VOD) servers. Resource sharing techniques can be used to address this problem. We evaluate through extensive simulation major resource sharing techniques, considering both the True Video-on- Demand (TVOD) and Near Video-on-Demand (NVOD) service models. Moreover, we propose a statistical approach for cache management and derive analytical models for optimal cache allocation to reduce the demands on the disk I/O when various resource sharing techniques are used.