The interactive performance of SLIM: a stateless, thin-client architecture
Proceedings of the seventeenth ACM symposium on Operating systems principles
Multimedia servers: applications, environments, and design
Multimedia servers: applications, environments, and design
A Cluster-Based Active Router Architecture Supporting Video/Audio Stream Transcoding Service
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
A System Architecture for Managing Mobile Streaming Media Services
ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Critical Bandwidth Allocation Techniques for Stored Video Delivery Across Best-Effort Networks
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
Performance Guarantees for Cluster-Based Internet Services
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
On Optimal Batching Policies for Video-on-Demand Storage Servers
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Scalable Server and Storage Architectures for Video Streaming
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Integrated system for multimedia delivery over broadband IP networks
IEEE Transactions on Consumer Electronics
Multimedia Tools and Applications
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The recent advance in wireless network technologies has enabled the streaming media service on the mobile devices such as PDAs and cellular phones. Since the wireless network has low bandwidth channels and mobile devices are actually composed of limited hardware specifications, the transcoding technology is needed to adapt streaming media to the given mobile devices. When large scale mobile clients demand the streaming service, load distribution strategies among transcoding servers highly impact on the total number of QoS streams. In this paper, the resource weighted load distribution strategy is proposed for the fair load balancing and the more scalable performance in cluster-based transcoding servers. Our proposed strategy is based on the weight of resources consumed for transcoding to classified client grades and the maximum number of QoS streams actually measured in transcoding servers. The proposed policy is implemented on cluster-based transcoding system. In experiments, we evaluate its fair load distribution and scalable performance according to the increase of transcoding servers.