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Autonomic QoE Optimization in the Access Node Knowledge Plane
Bart De Vleeschauwer, Pieter Simoens, Wim Van de Meerssche, Steven Latré, Filip De Turck, Bart Dhoedt, Piet Demeester
Steven Van Den Berghe, Edith Gilon, Tom Van Caenegem
BBEurope 2007 conference — 2 Alcatel KPlane WorkshopIBBT - IBCN – Ghent University
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Services & Challenges
> Novel services for residential users : VoIP, Broadcast IPTV, Video on Demand, Gaming
> High requirements: interactivity, quality, Zapping Time, Artifact-free Video
> Quality of Experience is essential
Need for QoS/QoE management
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Outline
> Three planes for access node QoE management
> Reasoning directions
> Use case & demo
> Conclusion
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A three plane approach
Element
EffectorsSensors
Knowledge Monitor
Analyze Plan
Execute
Gather monitor information Summarize data Make information available
Analyze monitor data Determine QoE optimizing actions Reason & learn
Provide interface to actions
IBM
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QoE Management end-to-end view
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Autonomic QoE management in the access node
> Problems often occur between access node and user
> Under control of access network provider
> Access node can be service-rich
BBEurope 2007 conference — 7 Alcatel KPlane WorkshopIBBT - IBCN – Ghent University
MPlane
> Gather monitor information
> Protocol monitoring for RTP/RTCP and TCP services
> Summarize monitor information
> Provide interface to KPlane
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APlane
> Provide interface to QoE enhancing actions
> Possible actions to enhance the quality of a connection• Choose a bit rate for a video• Send FEC packets• Change video stream GOP structure• Purposefully degrade other (less important) services
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Outline
> Three planes for access node QoE management
> Reasoning directions
> Use case & demo
> Conclusion
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KPlane
> Connects monitor data with the actions that should be executed
> One engine for each home network
> Implement learning behavior
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Reasoning directions
> Analytical reasoner• Mathematical equations determine actions• Simple to implement but difficult to extend
> Fuzzy logic• Captures vagueness in monitor information• A rule based approach can be used to determine actions
> Neural networks• Black box• Network is trained with input patterns
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Outline
> Three planes for access node QoE management
> Reasoning directions
> Use case & demo
> Conclusion
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Use case
Video Server
Client 1
Client 2
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Use case description
> Server sends out:• Video channels in different bit rates (700 kbit/s and 2 Mbit/s)• Stream of FEC packets for each channel and each bit rate
> Access node• MPlane: Uses information in RTP and RTCP packets to analyze
the QoE• KPlane: Selects the bit rate and the amount of FEC• APlane: Makes the necessary changes to enforce these actions
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Analytical Reasoner
> Probability that up to K FEC packets are lost for a stream of N data packets when there is Loss packet loss
> Bitrate that should be used when there is FreeBW available and K data packets are sent for every N data packets
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Simulation Results
0
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5Loss on the wireless link (%)
Me
as
ure
d l
os
s (
%) No actions
Analytical reasoner
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Demo management GUI
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Demo client screen
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Conclusion
> QoE management with three planes• MPlane• APlane• KPlane
> Access node can play pivotal role in enhancing the QoE
> QoE for BCTV services can be optimized• Applying FEC to solve packet loss• Selecting the correct bit rate to avoid congestion
> Demo
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Thank you for your attentionAny questions?
