Scaling a video on demand server
Early performance estimation is key to successful implementation
Illia Cremer, CoFluent Design
EETimes (4/27/2011 9:51 AM EDT)
Abstract
In a growing and more competitive video on demand (VoD) market, system designers face new challenges in VoD server infrastructures definition and sizing. Early performance estimation thanks to abstract modeling is a key enabler for providing best quality of service and compelling user experience.
This article illustrates how to model and simulate an example model of a RTP/RTSP video on demand server using the method, notations and tools provided by CoFluent Design.
The objective is to determine the client’s frame rate deviation and the average power consumption for different server configurations. The frame rate deviation is the difference between the expected theoretical frame rate and the actual frame rate of the video stream. It directly impacts the user’s watching experience and should be kept as much as possible close to zero.
The impact of different hardware elements of the server such as HDD type and server buffering is studied. The example also illustrates how to model multiple instances of the same function, and how to define an abstract network of computers.
Related Semiconductor IP
- AES GCM IP Core
- High Speed Ethernet Quad 10G to 100G PCS
- High Speed Ethernet Gen-2 Quad 100G PCS IP
- High Speed Ethernet 4/2/1-Lane 100G PCS
- High Speed Ethernet 2/4/8-Lane 200G/400G PCS
Related White Papers
- Using vector processing for HD video scaling, de-interlacing, and image customization
- Polyphase Video Scaling in FPGAs
- Revisiting the analogue video decoder: Brushing up on your comb filters
- Thoughts on Streaming Video Securely
Latest White Papers
- New Realities Demand a New Approach to System Verification and Validation
- How silicon and circuit optimizations help FPGAs offer lower size, power and cost in video bridging applications
- Sustainable Hardware Specialization
- PCIe IP With Enhanced Security For The Automotive Market
- Top 5 Reasons why CPU is the Best Processor for AI Inference