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An End-to-end Architecture for Quality-Adaptive Streaming Applications in Best-effort Networks. Reza Rejaie [email protected] USC/ISI http://netweb.usc.edu/reza April 13, 1999. Motivation. Rapid growth in deployment of realtime streams(audio/video) over the Internet - PowerPoint PPT Presentation
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1USC INFORMATION SCIENCES INSTITUTE
An End-to-end Architecture for Quality-Adaptive Streaming Applications in Best-effort Networks
Reza [email protected]
USC/ISI
http://netweb.usc.edu/reza
April 13, 1999
2USC INFORMATION SCIENCES INSTITUTE
Motivation Rapid growth in deployment of realtime
streams(audio/video) over the Internet TCP is inappropriate for realtime streams
The Internet requires end-system to react to congestion properly and promptly
Streaming applications require sustained consumption rate to deliver acceptable and stable quality
3USC INFORMATION SCIENCES INSTITUTE
Best-effort Networks (The Internet)
Shared environment Bandwidth is not known a prior Bandwidth changes during a session Seemingly-random losses
TCP-based traffic dominates End-to-end congestion control is crucial for
stability, fairness & high utilization
End-to-end congestion control in a TCP-friendly fashion is the main requirement in the Internet
4USC INFORMATION SCIENCES INSTITUTE
Streaming Applications
Delay-sensitive Semi-reliable Rate-based
Require QoS from the end-to-end point of view
Internet
Adaptation
Buffer
Decoder
TCP TCP
Server
Display
Encoder
Source
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Designing an end-to-end congestion control mechanism
Delivering acceptable and stable quality while performing congestion control
The Problem
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Outline
The End-to-end Architecture Congestion Control (The RAP protocol) Quality Adaptation
Extending the Architecture Multimedia Proxy Caching
Contributions Future Directions
7USC INFORMATION SCIENCES INSTITUTE
Buffer Manager
Archive
ErrorControl
QualityAdaptation
Transmission Buffer
Cong.Control Acker
Decoder
PlaybackBuffer
Inte
rnet
Server ClientAdaptation Buffer
Data pathControl path
The End-to-end Architecture
Buffer Manager
8USC INFORMATION SCIENCES INSTITUTE
Outline
The End-to-end Architecture Congestion Control (The RAP Protocol) Quality Adaptation
Extending the Architecture Multimedia Proxy Caching
Contributions Future Directions
9USC INFORMATION SCIENCES INSTITUTE
Previous works on Congestion Ctrl.
Modified TCP [Jacob et al. 97], SCP[Cen et al. 98]
TCP equation [Mathis et al. 97], [Padhye et al. 98]
Additive Inc., Multiplicative Dec. LDA[Sisalem et al. 98]
NETBLT[Lixia!] Challenge: TCP is a moving target
10USC INFORMATION SCIENCES INSTITUTE
Overview of RAP
Decision Function Increase/Decrease
Algorithm Decision Frequency
Goal: to be TCP-friendlyTime
Rat
e
Decision Frequency
Decision Function
+ --
Increase/Decrease Algorithm
11USC INFORMATION SCIENCES INSTITUTE
Congestion Control Mechanism
Adjust the rate once per round-trip-time (RTT)
Increase the rate periodically if no congestion
Decrease the rate when congestion occurs Packet loss signals congestion
Cluster Loss Grouping losses per congestion event
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Rate Adaptation Algorithm
Coarse-grain rate adaptation Additive Increase, Multiplicative Decrease (AIMD)
Extensive simulations revealed: TCP’s behavior substantially varies with network
conditions, e.g. retransmission timeout, bursty TCP is responsive to a transient congestion
AIMD only emulates window adjustment in TCP
13USC INFORMATION SCIENCES INSTITUTE
Rate Adaptation Algorithm(cont’d)
Fine-grain rate adaptation The ratio of short-term to long-term average
RTT
Emulates ACK-clocking in TCP Increase responsiveness to transient
congestion
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Coarse vs fine grain RAP figImpact of fine-grain rate adaptation
15USC INFORMATION SCIENCES INSTITUTE
RAP against Tahoe, Reno, NewReno & SACK
Inter-dependency among parameters
Config. parameters: Bandwidth per flow RTT Number of flows
RA
P S
inksT
CP
Sinks
RAP Traffic
SW
TCPTraffic
SW
RA
P S
ourcesT
CP
Sources
RAP Simulation
Fairness Ratio = Avg. RAP BW
Avg. TCP BW
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Fairness ratio across the parameter space without F.G. adaptation
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Fairness ratio across the parameter space with F.G. adaptation
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Impact of RED switches on Fairness ratio
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Summary of RAP Simulations
RAP achieves TCP-friendliness over a wide range Fine grain rate adaptation extends inter-protocol fairness to a
wider range Occasional unfairness against TCP traffic is mainly
due to divergence of TCP congestion control from AIMD Pronounced more clearly for Reno and Tahoe The bigger TCP’s congestion window, the closer its behavior
to AIMD RED gateways can improve inter-protocol sharing
Depending on how well RED is configured RAP is a TCP-friendly congestion controlled UDP
20USC INFORMATION SCIENCES INSTITUTE
Outline
The End-to-end Architecture Congestion Control (The RAP protocol) Quality Adaptation
Extending the Architecture Multimedia Proxy Caching
Contributions Future Directions
21USC INFORMATION SCIENCES INSTITUTE
Buffer Manager
Archive
ErrorControl
QualityAdaptation
Transmission Buffer
Cong.Control Acker
Decoder
PlaybackBuffer
Inte
rnet
Server ClientAdaptation Buffer
Data pathControl path
Quality Adaptation
Buffer Manager
22USC INFORMATION SCIENCES INSTITUTE
The Problem
Delivering acceptable and stable quality while performing congestion control
Seemingly random losses result in random & potentially wide variations in bandwidth
Streaming applications are rate-based
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Role of Quality Adaptation
Buffering only absorb short-term variations
Long-lived session could result in buffer overflow or underflow
Quality Adaptation is complementary for buffering
Adjust the quality with long-term variations in bandwidth
BW(t)
Time
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Adaptive encoding [Ortega 95, Tan 98] CPU-intensive
Switching between multiple encoding High storage requirement
Layered encoding[McCanne 96, Lee 98] Inter-layer decoding dependency
When/How much to adjust the quality?
Mechanisms to Adjust Quality
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Assumptions AIMD variations in bandwidth(rate) Linear layered encoding
Constraint Obeying congestion controlled rate
limit Goal
To control the level of smoothing
Assumptions & Goals
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Layered Quality Adaptation
Layer 2
Layer 1
Layer 0
+bw (t)2
bw (t)1
bw (t)0
Inte
rnet
Decoder
Time(sec)
BW
(t)
bw (t)1
bw (t)0
C
C
C Display
Linear layered stream
buf 0
buf 1
buf 2
bw (t)2
QualityAdaptation
Con
sum
ptio
n
ra
te
C
Time(msec)
BW
(t)
BW(t) BW(t)
a c
b
Filling Phase
Draining Phase
C
C
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Buffering Tradeoff Each buffering layer can only
contribute at most C(bps) Buffering for more layers
provides higher stability
bw (t)1
bw (t)0
C
C
Cbuf 0
buf 1
bw (t)2
Time
BW
(t)
BW(t)
nC
buf 2
C
C
C
C
Buffered data for a dropped layer is useless for recovery Buffering for lower layers is
more efficient What is the optimal buffer distribution for a single back-off scenario?
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Optimal buffer state depends on time of the back-off
Draining pattern depends on the buffer state
Back-off occurs randomly
Keep the buffer state as close to the optimal as possible during the filling phase
Time
BW
(t) Draining
Phase
4CCC
Buf. data &Buf. data &
Filling Phase
Optimal Inter-layer Buffer Allocation
Buf. data &
BW share of L0BW share of L1BW share of L2
29USC INFORMATION SCIENCES INSTITUTE
Add a layer when buffering is sufficient for a single back-off
Drop a layer when buffering is insufficient for recovery
Random losses could result in frequent add and drop unstable quality
Conservative adding results in smooth changes in quality
Time
Adding & Dropping
BW
(t)
Draining Phase
nC
Draining Phase
(n-1)C
Buf. data for L0Buf. data for L1Buf. data for L2
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Conservative adding When average bandwidth is sufficient When sufficient buffering for K back-offs
Buffer constraint is preferred and sufficient Directly relate time of adding to the buffer state Effectively utilizes the available bandwidth
K is a smoothing factor Short-term quality vs long-term smoothing
Smoothing
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ProperBuf. State recovery from 1 backoff
ProperBuf. State recovery from 2 backoffs
ProperBuf. State recovery from K backoffs
Add aLayer
Drop aLayer
Filling
Smooth Filling & Draining
Draining
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KB/s
C = 10
Buf. L3(KB)9.5
9.5
9.5
9.5
Buf. L2(KB)
Buf. L1(KB)
Buf. L0(KB)
17.5 Buf. L3(KB)
Buf. L2(KB)
Buf. L1(KB)
Buf. L0(KB)
17.5
17.5
17.5
KB/s
C = 10
40 Time(sec)
40 Time(sec)
Effect of smoothing factor
(K = 2)
(K = 4)40 Time(sec)
40 Time(sec)
TX rate & Quality
TX rate & Quality
33USC INFORMATION SCIENCES INSTITUTE
Buf. L3(KB)
Buf. L3(KB)
Buf. L3(KB)
Buf. L3(KB)
C = 10
17.5
17.5
17.5
17.5
KB
90 Time(sec)30 60
(K = 4)
90 Time(sec)30 60
Adapting to network load
KB/s
TX rate & Quality
34USC INFORMATION SCIENCES INSTITUTE
No of Dropped Layers
Buffering efficiency
949596979899
100101
2 3 4 5 8
K(smoothing factor)
% u
sed
bu
ffer
No of drops
0
20
40
60
80
2 3 4 5 8
K (smoothing factor)
#dro
ps
T1
T2
35USC INFORMATION SCIENCES INSTITUTE
Summary of the QA results
Quality adaptation mechanism can efficiently control the quality
Smoothing factor allows the server to trade short-term improvement with long-term smoothing
Buffer requirement is low Deploying for live but non-interactive
sessions!
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Delivered quality is limited to the average bandwidth between the server and client
Solutions: Mirror servers Multimedia proxy
caching
Limitation of the E2E Approach
Server
Client
Internet
ClientClient
TimeL0L1L2L3
L4
Qua
lity(
laye
r)
37USC INFORMATION SCIENCES INSTITUTE
Outline
The End-to-end Architecture Congestion Control (The RAP protocol) Quality Adaptation
Extending the Architecture Multimedia Proxy Caching
Contributions Future Directions
38USC INFORMATION SCIENCES INSTITUTE
Server
Assumptions Proxy can perform:
– End-to-end congestion ctrl– Quality Adaptation
Goals Improve delivered
quality Low-latency VCR-
functions Natural benefits of
caching
Proxy
Internet
Multimedia Proxy Caching
Client Client Client
39USC INFORMATION SCIENCES INSTITUTE
Challenge
Cached streams have variable quality
Layered organization provides opportunity for adjusting the quality
Time
L0
L1
L2
L3
L4
Qua
lity
(lay
er)
Stored stream
Played back streamPlayed back stream
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Issues
Delivery procedure Relaying on a cache miss Pre-fetching on a cache hit
Replacement algorithm Determining popularity Replacement pattern
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Cache Miss Scenario
Stream is located at the original server
Playback from the server through the proxy
Proxy intercepts and caches the stream
No benefit in a miss scenario
Server
Internet
Proxy
Client Client Client
42USC INFORMATION SCIENCES INSTITUTE
Cache Hit Scenario
Playback from the proxy cache Lower latency May have better
quality! Available bandwidth
allows: Lower quality playback Higher quality playback
Server
Proxy
Internet
Client Client Client
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Lower quality playback
Missing pieces of the active layers are pre-fetched on-demand
Required pieces are identified by QA
Results in smoothing
Time
L0
L1
L2
L3
L4
Qua
lity
(no.
act
ive
laye
rs)
Pre-fetched data
Stored stream
Played back stream
44USC INFORMATION SCIENCES INSTITUTE
Pre-fetch higher layers on-demand
Pre-fetched data is always cached
Must pre-fetch a missing piece before its playback time
Tradeoff Time
L0
L1
L2
L3
L4
Qua
lity
(no.
act
ive
laye
rs)
Pre-fetched data
Stored stream
Played back Stream
Higher quality playback
45USC INFORMATION SCIENCES INSTITUTE
Replacement Algorithm
Goal: converge the cache state to optimal Average quality of a cached
stream depends on – popularity – average bandwidth between
proxy and recent interested clients
Variation in quality inversely depends on
– popularity
Server
Proxy
Internet
Client Client Client
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Number of hits during an interval User’s level of interest (including VCR-functions)
Potential value of a layer for quality adaptation Calculate whit on a per-layer basis
Layered encoding guarantees monotonically decrease in popularity of layers
Popularity
whit = PlaybackTime(sec)/StreamLength(sec)
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Multi-valued replacement decision for multimedia object
Coarse-grain flushing on a per-layer basis
Fine-grain flushing on a per-segment basis
Fine-grain Coarse-grain
Cached segment
Replacement Pattern
Time
Quality(Layer)
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Summary of Multimedia Caching
Exploited characteristics of multimedia objs
Proxy caching mechanism for multimedia streams Pre-fetching Replacement algorithm
Adaptively converges state of the cache to the optimal
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Contributions
End-to-end architecture for delivery of quality-adaptive multimedia streams
RAP, a TCP-friendly cong. ctrl mechanism over a wide range of network conditions
Quality adaptation mechanism that adjusts the delivered quality with a desired degree of smoothing
Proxy caching mechanism for multimedia streams to effectively improve the delivered quality of popular streams
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Future Directions
End-to-end Congestion Control RAP’s behavior in the presence web-like traffic Emulating timer-driven regime TCP Bi-directional RAP connections, Reverse ns
forward path congestion control Experiments over CAIRN & the Internet Integration of RAP and congestion manager Adopting RAP into class-based QoS Using RAP for multicast congestion control Congestion control over wireless networks
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Future Directions(cont’d)
Quality Adaptation Extending to other rate adaptation
mechanisms Multimedia Proxy Caching
Other replacement patterns & popularity functions(e.g. chunk-based)
Traffic Measurement and Characterization Imiprical evaluation of streaming applications
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An End-to-end Architecture for Quality-Adaptive Streaming Applications in Best-effort Networks
Reza [email protected]
USC/ISI
http://netweb.usc.edu/reza
April 7, 1999
54USC INFORMATION SCIENCES INSTITUTE
Archive
MediaServer Internet
TCPTraffic
TCPTraffic
Target Environment
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Optimal Buffer Allocation
Scenario 1 Scenario 2 Scenario 3
Backoff 2
Optimal buffer state is not uniqueS1 and S2 are extreme cases
S1 requires more buffering layersS2 requires more buffer share per layer
Buffer allocation for S1 can recover from S2 but not vice versa