When CDN meets Software-defined NetworkingWhen CDN meets Software-defined Networking: Overview of Current Trends and New Agendas for Research 한동수 (KAIST) Video Traffic is Becoming

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

http://conferences.sigcomm.org/co-next/2012/eproceedings/conext/p97.pdf





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


13

PLAYER STATES

EVENTS


Buffer filled up

Buffer empty

Buffer filled up

Switch bitrate


14

PLAYER STATES

EVENTS


Buffer filled up

Buffer empty

Buffer filled up

Switch bitrate


15

PLAYER STATES

EVENTS


Buffer filled up

Buffer empty

Buffer filled up

Switch bitrate


16

PLAYER STATES

EVENTS


Buffer filled up

Buffer empty

Buffer filled up

Switch bitrate


17

PLAYER STATES

EVENTS


Buffer filled up

Buffer empty

Buffer filled up

Switch bitrate


18

PLAYER STATES

EVENTS


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


23



Engagement (e.g., Fraction of video viewed)


24


Join Time, Avg. bitrate, …?




25


Join Time, Avg. bitrate, …? f(Join Time, Avg. bitrate, …)




26


Join Time, Avg. bitrate, …? f(Join Time, Avg. bitrate, …)



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


Enga

gem

ent

Quality Metric

Non-monotonic

Average bitrate

E

nga

gem

ent

[Dobrian et al. Sigcomm 2011]

30


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


32

Join Time Bitrate

Rate of buffering

Rate of switching

Buffering Ratio


33

Join Time

Rate of buffering

Rate of switching

Buffering Ratio

Bitrate


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


40

However, user viewing behavior is very different


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]


New Agenda


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]


New Agenda


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

Documents

When CDN meets Software-defined NetworkingWhen CDN meets Software-defined Networking: Overview of Current Trends and New Agendas for Research 한동수 (KAIST) Video Traffic is Becoming