A Demand Adaptive and Locality Aware (DALA) streaming media server cluster architecture
NOSSDAV '02 Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video
Selecting among replicated batching video-on-demand servers
NOSSDAV '02 Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video
Introduction to Algorithms
Globe: A Wide-Area Distributed System
IEEE Concurrency
Hierarchical Storage Management in a Distributed VOD System
IEEE MultiMedia
Measuring the capacity of a streaming media server in a Utility Data Center environment
Proceedings of the tenth ACM international conference on Multimedia
MediSyn: a synthetic streaming media service workload generator
NOSSDAV '03 Proceedings of the 13th international workshop on Network and operating systems support for digital audio and video
Extending the VoDKA architecture to improve resource modelling
Proceedings of the 2003 ACM SIGPLAN workshop on Erlang
Extending and enhancing GT-ITM
MoMeTools '03 Proceedings of the ACM SIGCOMM workshop on Models, methods and tools for reproducible network research
EmuNET: a real-time network emulator
Proceedings of the 2004 ACM symposium on Applied computing
An SLA-Oriented Capacity Planning Tool for Streaming Media Services
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Optimizing the Reliable Distribution of Large Files within CDNs
ISCC '05 Proceedings of the 10th IEEE Symposium on Computers and Communications
Designing and Scaling Proactive, Self-Organizing Video Servers
Designing and Scaling Proactive, Self-Organizing Video Servers
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Planning Video-on-Demand (VoD) services based on the server architecture and the available equipment is always a challenging task. We created a formal model to support the design of distributed video servers that adapt dynamically and automatically to the changing client demands, network and host parameters. The model makes giving estimations about the available throughput possible, and defines evaluation criteria for VoD services relating to utilization and load balance, video usage, client satisfaction and costs. The dynamism of the frame model originates from the possible state transitions which have to be defined in a core model. The core model is responsible for configuration recommendation which determines how clients are served depending on the properties of their requests, system configuration and system load. Furthermore, it decides on the optimal placement of the server components in the network. The usability of the model is illustrated on examples.