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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 Antonio Ortega, Daniel Lee Department of EE-Systems University of Southern California
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
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
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
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
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
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
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
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
Decoder Buffer Underflow time t Channel Encoder ? time t+DT Encoder buffer Decoder ? Decoder buffer Decoder buffer underflow is the more general constraint
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
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
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.
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
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)