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When CDN meets Software-defined Networking:
Overview of Current Trends and New Agendas for Research
한동수 (KAIST)
Video Traffic is Becoming Dominant [conext2012]
• 2011, 66+% of Internet traffic is video. [Akamai]
• 2016, 86% will be video traffic. [Cisco]
The Internet is becoming a Video Network
2
Slides borrowed from [conext2012] Junchen Jiang, Vyas Sekar, Hui Zhang. Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with FESTIVE. CoNEXT 2012
How is Video Delivered Today [conext2012]
3
HTTP Adaptive Player
Web browser Web server
HTTP
TCP
…
HTTP
TCP
…
A1 A1 A2
B1 B2
A1 B1
Cache
Client
Web server
…
…
A1 A2
B1 B2
Load balancer
HTTP GET A1
Server
B2 HTTP GET B2
Abstract Player Model [conext2012]
Chunk scheduling
Bitrate selection
B/W Estimation
time
Video Buffer
Chunks
Bitrate1 ….. BitrateN
HTTP Requests
HTTP Protocol
Throughput
When to request the next chunk?
Which bitrate is the next chunk?
4
Why is it Popular? [conext2012]
5
HTTP Adaptive Player
Web browser Web server
HTTP
TCP
…
HTTP
TCP
…
A1 A1 A2
B1 B2
B1
Cache
Client
Web server
…
…
A1 A2
B1 B2
Load balancer
CDN Infrastructure
Reuse the CDN infrastructure
Client-driven control enables server/CDN switch
HTTP as convergence datagram,
middlebox/firewall penetration
The Broader Video Ecosystem
• CDN is only part of it.
• Players in the ecosystem
–Video players
–CDNs
–Publishers
– ISPs
–Video analytics/monitoring companies
The Video Ecosystem [hotnets2012]
Goals/Challenges for Each Players
• Common goal:
–Maximize user Quality of Experience/ Engagement!
• ISP, Provider, CDN:
–Minimize cost in video delivery.
How do we improve QoE?
• What is the metric for users’ quality of experience (QoE)?
There is no unified metric for QoE.
“If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.”
[M.A. Cohen]
Talk Overview
Research Trends (Technology overview)
• A Quest for Internet Video Quality-of-Experience Metric [slides from hotnets 2012]
• Software-defined Networking [slides from nsdi 2007]
New Agenda
• Towards a Software-defined Content Distribution Network
Video Quality Metrics: The State of the Art [hotnets 2012]
11
Objective Score (e.g., Peak Signal to Noise Ratio)
Subjective Scores (e.g., Mean Opinion
Score)
Problem 1: New Effects, New Metrics [hotnets 2012]
12
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Problem 1: New Effects, New Metrics [hotnets 2012]
13
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Problem 1: New Effects, New Metrics [hotnets 2012]
14
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Problem 1: New Effects, New Metrics [hotnets 2012]
15
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Problem 1: New Effects, New Metrics [hotnets 2012]
16
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Problem 1: New Effects, New Metrics [hotnets 2012]
17
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Problem 1: New Effects, New Metrics [hotnets 2012]
18
PLAYER STATES
EVENTS
Joining Playing Buffering Playing
Buffer filled up
Buffer empty
Buffer filled up
Switch bitrate
Join Time Buffering Ratio Rate of buffering
Rate of switching Average bitrate
Decrease in Join Time Leads to Longer Play Time
Vid
eo
Pla
y ti
me
(m
in)
Join Time (s)
From results reported in [Dobrian et al., sigcomm 2011]
Increase in Buffering Ration Reduces Engagement.
Buffering Ratio (%)
Vid
eo P
lay
tim
e (m
in)
From results reported in [Dobrian et al., sigcomm 2011]
Problem 2: Opinion Scores Engagement [hotnets 2012]
Opinion Scores
- Not representative of “in the wild” experience
- Combinatorial explosion of parameters
Engagement as replacement for opinion score.
(e.g., Play time, customer return rate)
21
Internet Video QoE [hotnets 2012]
22
Objective Scores PSNR
Subjective Scores MOS
Internet Video QoE [hotnets 2012]
23
Objective Scores PSNR
Subjective Scores MOS
Engagement (e.g., Fraction of video viewed)
Internet Video QoE [hotnets 2012]
24
Objective Scores PSNR
Join Time, Avg. bitrate, …?
Subjective Scores MOS
Engagement (e.g., Fraction of video viewed)
Internet Video QoE [hotnets 2012]
25
Objective Scores PSNR
Join Time, Avg. bitrate, …? f(Join Time, Avg. bitrate, …)
Subjective Scores MOS
Engagement (e.g., Fraction of video viewed)
Internet Video QoE [hotnets 2012]
26
Objective Scores PSNR
Join Time, Avg. bitrate, …? f(Join Time, Avg. bitrate, …)
Subjective Scores MOS
Engagement (e.g., Fraction of video viewed)
Outline [hotnets 2012]
• Need for a unified QoE
• What makes this hard?
• Proposed approach
[hotnets 2012] Athula Balachandran, Vyas Sekar, Aditya Akella, Srinivasan Seshan, Ion Stoica, Hui Zhang.
A Quest for an Internet Video Quality-of-Experience Metric, HotNets 2012
27
28
Challenge: Complex Engagement-to-metric Relationships [hotnets 2012]
Enga
gem
ent
Performance Metric
[Dobrian et al. Sigcomm 2011]
29
Challenge: Complex Engagement-to-metric Relationships [hotnets 2012]
Enga
gem
ent
Quality Metric
Non-monotonic
Average bitrate
E
nga
gem
ent
[Dobrian et al. Sigcomm 2011]
30
Challenge: Complex Engagement-to-metric Relationships [hotnets 2012]
Enga
gem
ent
Performance Metric
En
gage
men
t
Rate of buffering
Threshold
Non-monotonic
Average bitrate
E
nga
gem
ent
Challenge: Complex Metric Interdependencies [hotnets 2012]
31
Join Time Bitrate
Rate of buffering
Rate of switching
Buffering Ratio
Challenge: Complex Metric Interdependencies [hotnets 2012]
32
Join Time Bitrate
Rate of buffering
Rate of switching
Buffering Ratio
Challenge: Complex Metric Interdependencies [hotnets 2012]
33
Join Time
Rate of buffering
Rate of switching
Buffering Ratio
Bitrate
Challenge: Complex Metric Interdependencies [hotnets 2012]
34
Join Time Avg. bitrate
Rate of buffering
Rate of switching
Buffering Ratio
35
Need to learn these complex engagement-to-metric relationships and metric-to-metric dependencies [hotnets 2012]
Casting as a Learning Problem [hotnets 2012]
36
MACHINE LEARNING
Engagement (normalized play time)
Performance Metrics
QoE Model
Need to learn these complex engagement-to-metric relationships and metric-to-metric dependencies
Quality Metrics Engagement Predictions
10 million video sessions
Impact of the ML algorithm [hotnets 2012]
37
• Divide normalized playtime into discrete regions (classes) • Accuracy = # of accurate predictions/ # of cases
1 0
Region 1 Region 2 … Region N
QoE Metric (Normalized Playtime/Fraction of Video Viewed)
Performance Metrics (Join time, buffering ratio, etc)
Impact of the ML algorithm [hotnets 2012]
38
• Divide normalized playtime into discrete regions (classes) • Accuracy = # of accurate predictions/ # of cases
ML algorithm must be expressive enough to handle the complex relationships and interdependencies
(N)
Challenge: Confounding Factors [hotnets 2012]
39
Live and VOD sessions experience similar quality
Challenge: Confounding Factors [hotnets 2012]
40
However, user viewing behavior is very different
Challenge: Confounding Factors [hotnets 2012]
41
Devices User Interest Connectivity
Need systematic approach to identify and handle confounding factors
Domain-specific Refinement [hotnets 2012]
42
MACHINE LEARNING
Engagement Quality Metrics
QoE Model
Domain-specific Refinement [hotnets 2012]
43
MACHINE LEARNING
Engagement Quality Metrics
QoE Model
Confounding Factors
Improved prediction accuracy [hotnets 2012]
44
Refined ML models can handle confounding factors
Summary [hotnets 2012]
• Internet video needs unified quantitative QoE
• What makes this hard?
– Complex relationships and confounding factors
• Promising approach:
– Machine learning + domain-specific refinements
• Emerging new metrics for QoE will unveil new opportunities in measurement and optimization.
45
The Video Ecosystem
Suppose the metric is there. 1. Where do we start the
optimization from? 2. How do we enable various
optimizations for different entities/players?
The Video Ecosystem [hotnets 2012]
Focus on CDN Internal Management
• Goal:
–Let’s design a CDN that allows various network-wide goals (e.g., min cost, max QoE) to be implemented.
• Approach:
–Apply ideas from Software-defined Networking
Talk Overview
Research Trends (Tutorial/Technology overview)
• A Quest for Internet Video Quality-of-Experience Metric [slides from hotnets 2012, sigcomm 2011]
• Software-defined Networking [slides from nsdi 2007]
New Agenda
• Towards a Software-defined Content Distribution Network
Limitation of Today’s Control & Management
• Hard to Achieve Network-wide Goals
– Lack of higher level specification of network wide goals • Load balancing objectives vs. per link OSFP weight
–Difficult to dynamically coordinate multiple mechanisms • Forwarding, access control, NAT, tunnel management
Slides borrowed from Hui Zhang 4D project.
Direct Control: A New World [nsdi 2007]
• Express goals explicitly – Do not bury goals in box-specific configuration
• Design network to provide timely and accurate information – Topology, traffic, resource limitations
• Decision maker computes and pushes desired network state – Add new functions without modifying/creating
protocols or upgrading routers
Slides borrowed from Hui Zhang 4D project.
D
How can we get there?
Routing Table Access Control Table NAT Table Tunnel Table
Decision Computation Service
Generating table entries
Data Plane
Modeled as a set of tables
Install table entries
Discovery
Dissemination Service D
D
D
4D [nsdi 2007]
Talk Overview
Research Trends (Tutorial/Technology overview)
• A Quest for Internet Video Quality-of-Experience Metric [slides from hotnets 2012, sigcomm 2011]
• Software-defined Networking [slides from nsdi 2007]
New Agenda
• Towards a Software-defined Content Distribution Network
Software-defined CDN
• 3 Unique properties/differences:
–Environment: Overlay (vs. physical)
–Workload: Content delivery (vs. general
packet forwarding)
–Data plane: Software (vs. Hardware)
Server Server
Server Server Server
System Overview
Decision Engine
Data Plane
Discovery
Dissemination Service D
D
D
Challenges in Discovery
• Must ensure freshness, correctness, and completeness of data. – Raw data (connectivity, throughput, capacity,
demand) needs to be discovered.
• It must provide an abstract network model for the decision plane. – Raw data must be processed to model constraints.
– Identify topology, expected throughput, constrained resources from raw data.
Challenges in Dissemination
• Must be robust to network and node failures.
• Must be scalable and low-latency.
• Must provide appropriate semantics for distributed execution of commands.
– E.g., failure notifications, synchronization barrier
– Need to provide a model for distributed execution
Challenges in Decision
• Fault tolerance!
–Need multiple decision engines
• Consistency model
Most consistent Inconsistent
Transactional consistency [ONIX]
Eventual consistency [RCP, ONIX]
No consistency
Design space for control plane consistency
Challenges in Data Plane
• Ensure robustness even when state is inconsistent across nodes.
• Approach:
– Leverage content delivery semantics.
– Leverage the flexibility of software in data plane.
Conclusion
• Techniques from various domains, including distributed systems, software-defined networking, algorithms, and machine learning, can be leveraged to improve Internet video delivery.
• Emerging new metrics for QoE unveil new opportunities in measurement and optimization.
• Software-defined CDN infrastructure can enable network-wide optimizations for video delivery.
Acknowledgements
A large part of this talk was intended to be a technology overview on Internet video delivery. Many thanks to Junchen Jiang [CMU] and Athula Balachandran [CMU] for letting me use their slides.
Bibliography
[hotnets2012] Athula Balachandran, Vyas Sekar, Aditya Akella, Srinivasan Seshan, Ion Stoica, Hui Zhang. A Quest for an Internet Video Quality-of-Experience Metric, HotNets 2012 [nsdi2007]Hong Yan , David A. Maltz , T. S. Eugene Ng , Hemant Gogineni , Hui Zhang , Zheng Cai. Tesseract: A 4D Network Control Plane, NSDI 2007 [conext2012] Junchen Jiang, Vyas Sekar, Hui Zhang. Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with FESTIVE. CoNEXT 2012 [sigcomm2011] Dobrian et al., Understanding the Impact of Video Quality on User Engagement Slides borrowed from [hotnets2012] [nsdi2007] [conext2012] [sigcomm2011]
Conclusion
• Techniques from various domains, including distributed systems, software-defined networking, algorithms, and machine learning, can be leveraged to improve Internet video delivery.
• Emerging new metrics for QoE unveil new opportunities in measurement and optimization.
• Software-defined CDN infrastructure can enable network-wide optimizations for video delivery.
Applications of Software-defined CDN
• Live streaming: QoE maximization, cost minimization
• VoD: QoE maximization, cost minimization, cache management
• Elastic CDN: Energy efficiency. Handling flash crowds or large load variations more efficiently.
Content Distribution within CDN
• Live streaming: Distribute streams to group of nodes that are interested in the content.
• Optimize “quality”
Origin
Server Server
Server Server Server
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