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Application- and network-cognizant proxies

This project focuses on designing middleware functions to adapt services to different policies, network conditions, and quality of service, with a particular emphasis on source adaptation. The aim is to improve efficiency and scalability in a heterogeneous network environment.

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Application- and network-cognizant proxies

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  1. Application- and network-cognizant proxies Antonio Ortega, Daniel Lee Department of EE-Systems University of Southern California

  2. Motivation • Heterogeneous network • Different service provision • Bandwidth • Reliability • Delay • Servers required to provide different application QoS to each end-user or subnet: -> Efficiency? Scalability? • Adaptation is best performed • close to the end user • with some knowledge of the application and network services available

  3. Goals • Designing tools (middleware functions) for • Service adaptation • To different service policies • To network status (performance) • Source adaptation • To differences in quality of service (delay, losses) • To time varying behavior

  4. Client Client Example:Video Rate Control and Caching • Increase reliability under variable channel conditions: • Local rate control • Selective Caching • Application knowledge at proxy is needed Video Server Client buffer Internet Unreliable Link Proxy Video Server Fast link Video Stream Client buffer

  5. Service adaptation and selection • Service modeling and categorization • Design and analysis of service selection functions • Dynamic adaptation • Experimental middleware implementation • Object-oriented framework design • Service announcement and discovery protocol and module • Service adaptation module

  6. Source Adaptation • Collection of tools • Transcoding • Local error control • Rate control • Addition/removal of redundancy • Addition/removal of layers • Mapping of layers to service classes

  7. Key Technical Innovations to produce • Proxy functions to adapt to network conditions both in terms of • Service selection • Source contents • Joint transport technology that combines the source coding and network service selection

  8. Suggestions for testbed projects • Implement differentiated service architecture • Standard Per-Hop-Behavior • e.g, Expedited Forwarding and Assured Forwarding • Possibility of testing applications • e.g., End-to-end video streaming over networks with different provisioning

  9. Client Client Video Rate Control and Caching • Rate control • Selective Caching Video Server Client buffer Internet Unreliable Link Proxy Video Server Fast link Video Stream Client buffer

  10. Decoder Buffer Underflow time t Channel Encoder ? time t+DT Encoder buffer Decoder ? Decoder buffer Decoder buffer underflow is the more general constraint

  11. Rate Control for Unreliable Channels Wireless Channel Video output Baseline layer Encoder Buffer Video input Enhanced layer #1 Decoder ACK ... Transmitter Receiver Enhanced layer #N A priori channel model Rate-Control

  12. Results - PSNR Wireless Uplink

  13. Selective Caching • Two alternatives: • Prefix Caching only • Selective Caching Select frames to be cached Required initial segment Required initial segment Continue caching the following frames Start Frames Start Frames Additional prefix Intermediate frames Approach I Approach II

  14. Frame Selection Strategy B(t) Buffer trace: number of frames in the decoder buffer during playback High robustness 0 Time Low robustness Fewer frames in decoder buffer, increased likelihood of jitter if network congestion happens at this time.

  15. Results: Selective Caching

  16. Summary • Designing tools (middleware functions) for • Service adaptation • To different service policies • To network status (performance) • Source adaptation • To differences in quality of service (delay, losses) • To time varying behavior

  17. Milestones • Modeling and analysis of network services (Year 1) • Design of service selection functions (Year 2-3) • Analysis and simulation of service selection functions (Year 2-3) • Design and optimization of source-related adaptation tools (Year 1-2) • Joint optimization of service and source adaptation (Year 3)

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