A Case for a Coordinated Video Control Plane Xi Liu, Florin - - PowerPoint PPT Presentation

A Case for a Coordinated Video Control Plane

Xi Liu, Florin Dobrian, Henry Milner, Junchen Jiang, Vyas Sekar, Ion Stoica, Hui Zhang (Conviva, CMU, Intel, and UC Berkeley)

Video Is Dominating the Internet Traffic

Netflix traffic alone exceeds 20% of US traffic1 2011’s Cisco Visual Networking Index2

2011: video represents 51% of the Internet traffic 2016: all types of video will represent 86% of the Internet traffic

The Internet is becoming a Video Network

2http://web.cs.wpi.edu/~claypool/mmsys-2011/Keynote02.pdf 1http://blogs.cisco.com/sp/comments/cisco\_visual\_networking\_index\_forecast\_annual\_update

Video Ecosystem: Data-Plane

Video Source Encoders & Video Servers CMS and Hosting Content Delivery Networks (CDNs) ISP & Home Net Screen Video Player

Video Quality Matters [Sigcomm’11]

Quality has substantial impact on viewer engagement

Need to ensure uninterrupted streaming at high bitrates

Buffering ratio is most critical across video traffic types

Highest impact for live: 1% of buffering reduced play time by 3min 1% increase in buffering can lead to more than 60% loss in audience over one month

Our Argument

CDN performance varies widely in time, geography, and ISPs Opportunity for significantly improving video Quality by selecting best CDN (and bitrate) for each viewer Hence, we argue for a logically centralized control plane to dynamically select CDN and bitrate Assumptions:

• Content is encoded at multiple bitrates
• Content is delivered by multiple CDNs

How do We Collect Data?

Streaming Module UI Controller Content Manager Messaging & Serialization

To backend HTTPS

Automatic Monitoring Player Insight

Player Application

Automatic and continuous monitoring of video player

Flash: NetStream, VideoElement Silverlight: MediaElement, SmoothStreamMediaElement iOS: MPMoviePlayerElement

What Traffic do We See?

Close to two billions streams per month Mostly premium content providers (e.g., HBO, ESPN, Disney) but also User Generated Video sites (e.g., Ustream) Live events (e.g., NCAA March Madness, FIFA World Cup, MLB), short VoDs (e.g., MSNBC), and long VoDs (e.g., HBO, Hulu) Various streaming protocols (e.g., Flash, SmoothStreaming, HLS), and devices (e.g., PC, iOS devices, Roku, XBOX, …) Traffic from all major CDNs, including ISP CDNs (e.g., Verizon, AT&T)

CDN Performance Varies Widely

CDNs Vary in Performance over Geographies and Time

There is no single best CDN across geographies, network, and time

25% 50% 25%

CDN 1 CDN 2 CDN 3

• Metric: buffering ratio
• One month aggregated data-set

– Multiple Flash (RTMP) customers – Three major CDNs

• 31,744 DMA-ASN-hour with > 100

streams from each CDN

– DMA: Designated Market Area

• Percentage of DMA-ASN-hour

partitions a CDN has lowest buffering ratio

Washington DC (Hagerstown): ASN-CXA-ALL

10% 20% 100% 30% 40% 50% 60% 70% 80% 90%

Washington, DC viewer experience differed greatly…

Comcast viewers got the best streams from CDN 1 51% of the time and only 9% from CDN 2 Washington DC (Hagerstown): VZGNI-TRANSIT (19262) Verizon users got the best streams from CDN 1 only 17% of the time and 77% from CDN 2

There is no single best CDN in the same geographic region or over time

CDN Streaming Failures Are Common Events

% of stream failures: % of streams that failed to start Three months dataset (May-July, 2011) for a premium customer using Flash

CDN Streaming Failures Are Common Events

% of stream failures: % of streams that failed to start Three months dataset (May-July, 2011) for a premium customer using Flash

CDN Streaming Failures Are Common Events

% of stream failures: % of streams that failed to start Three months dataset (May-July, 2011) for a premium customer using Flash

CDN Streaming Failures Are Common Events

% of stream failures: % of streams that failed to start Three months dataset (May-July, 2011) for a premium customer using Flash

CDN Streaming Failures Are Common Events

% of stream failures: % of streams that failed to start Three months dataset (May-July, 2011) for a premium customer using Flash

CDN Streaming Failures Are Common Events

% of stream failures: % of streams that failed to start Three months dataset (May-July, 2011) for a premium customer using Flash CDN (relative) performance varies greatly over time

Opportunities for Improving Quality

Possible Actions to Improve Quality

Switch the bitrate

↓ Buffering, high frame drops, high start time, … ↑ High available bandwidth, …

Switch the CDN

↔ Connection error, missing content, buffering on low bitrate, ...

When to perform switching/selection?

Start time selection only Start time selection & midstream switching

Potential Improvement Example: CDN Switching Only

For each CDN partition clients by (ASN, DMA)

DMA: Designated Market Area

Potential Improvement Example: CDN Switching Only

For each CDN partition clients by (ASN, DMA)

DMA: Designated Market Area

For each partition compute:

Buffering ratio Failure ratio Start time ….

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN

Potential Improvement Example: CDN Switching Only

For each CDN partition clients by (ASN, DMA)

DMA: Designated Market Area

For each partition compute:

Buffering ratio Failure ratio Start time ….

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN

• Avg. buff ratio of users in

ASN[1]xDMA[1] streaming from CDN1

Potential Improvement Example: CDN Switching Only

For each CDN partition clients by (ASN, DMA)

DMA: Designated Market Area

For each partition compute:

Buffering ratio Failure ratio Start time ….

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN

• Avg. buff ratio of users in

ASN[1]xDMA[1] streaming from CDN1

• Avg. buff ratio of users in

ASN[1]xDMA[1] streaming from CDN2

Potential Improvement Example: CDN Switching Only

For each partition select best CDN and assume all clients in the partition selected that CDN

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN

Potential Improvement Example: CDN Switching Only

For each partition select best CDN and assume all clients in the partition selected that CDN

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN CDN1 >> CDN2

Potential Improvement Example: CDN Switching Only

For each partition select best CDN and assume all clients in the partition selected that CDN

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN

Potential Improvement Example: CDN Switching Only

For each partition select best CDN and assume all clients in the partition selected that CDN Essentially, pick partition with best quality across CDNs

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN Best CDN (buffering ratio) DMA ASN

Potential Improvements

Provider1: large UGV (User Generated Video) site Provider2: large premium VoD content provider Base-line: existing assignment of viewers (clients) to CDNs

Metric Provider1 (UGV) Provider2 (Premium) Base line Start- time Selectio n Mid- stream Switching Base line Start- time Selection Mid- stream Switching Buffering ratio (%) 6.8 2.5 1 1 0.3 0.1

Between x2.7 and x10 improvement in buffering ratio

Coordinated Control Plane for High Quality Video Delivery

Video Control Plane Architecture

Coordinator implementing a global optimization algorithm that dynamically select CDN & bitrate for each client based on

Individual client Aggregate statistics Content owner policies (CDN/ISP info)

Content owners (CMS & Origin)

CDN 1 CDN 2 CDN 3

Clients

Coordinator

Continuous measurements Business Policies control

Example: Local vs. Global Optimization

20 40 60 80 100

2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Bandwidth Fluctuation (%) Concurrent Viewers

Bandwidth fluctuation = (Max Bandwidth – Min Bandwidth)/(Average Bitrate)

Example: Local vs. Global Optimization

CDN1 DMA ASN DMA ASN DMA ASN CDN2 CDN3

20 40 60 80 100

2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Bandwidth Fluctuation (%) Concurrent Viewers

10 20 30 40

5000 10000 15000 20000 25000 30000 35000

Bandwidth Fluctuation (%) Concurrent Viewers

ASN/DMA saturated on all CDNs  Don’t switch CDN; cap bitrates, instead

Concluding Remarks (I)

Key transition of main-stream video to the Internet Video quality presents opportunity and challenge

Premium video on big screens  zero tolerance for poor quality

Video player continuous monitoring and global optimization has best chance of delivering high quality video Many challenges remain, e.g.,

Scalability How do multiple coordinators interact? …

Concluding Remarks (II)

The video traffic dominance in the Internet is growing

Over 51% Internet traffic today, will be more than 86% in the next 4 years

The Internet is becoming a Video Network Managing video delivery and maximizing video quality must be at the core of any future Internet architecture!

Backup Slides

Conviva Optimization in the Wild

… increased average bit-rate from 1.7 Mbps to 2.1 Mbps… Reduced views impacted by buffering from 16.13% to 5.56% … … and raised engagement by 36%

0,00% 5,00% 10,00% 15,00% 20,00% 25,00% 1600 1700 1800 1900 2000 2100 2200

Views Impacted by Buffering Average Bit Rate

Possible Coordinator Architecture

Continuous real-time measurements from every client

Inference Engine

Bit Rates CDNs

Decision Engine

Optimize viewer performance by selecting the best

• ption within the set
• f bit rates and

CDNs

Akamai

DMA ASN DMA ASN DMA ASN

Limelight Level3

Time

• f

day Localize issues by region, network, CDN, and time Real-time Global Data Aggregation and Correlation Historical Data Aggregation and Analysis

Global Inference, Decision & Policy Engine Real-time global

• ptimizations

Conviva Services Enhance the Viewer Experience and Lift Engagement by Lifting Bit Rate and Reducing Buffering

Increased average bit-rate from 1.6 Mbps to 2.1 Mbps …

1.5% 1.0% 0.5%

DMS LAUNCH EMS LAUNCH

… reduced buffering ratio from 1.5% to 0.5% … and raised engagement by 36%

Potential Improvements

Customer1: large UGV site Customer2: large premium content provider Note: * denotes improvements when using mid-stream switching Metric Customer1 Customer2 Current Projected Current Projected Buffering ratio (%) 6.8 2.5 / 1* 1 0.3 / 0.1* Start time (s) 6.41 2.91 1.36 0.9 Failure ratio (%) 16.57 2.4 1.1 0.7

Between x2.7 and x10 improvement in buffering ratio

Video Quality Matters [Sigcomm’11]

Quality has substantial impact on viewer engagement

Need to ensure uninterrupted streaming at high bitrates

Buffering ratio is most critical across genres

Highest impact for live: 1% of buffering reduced play time by 3min 1% increase in buffering leads to more than 60% loss in audience

1% difference in buffering between two ISPs 68% monthly loss in uniques for ISP with poor performance

Customer1: Start-time vs. Midstream CDN Switching

78% 84% 90%

Provider1: Oracle vs. Historical

Base-line Oracle Historic

Potential Improvement Example: CDN Switching Only

Oracle:

For each partition select best CDN and assume all clients in the partition selected that CDN Essentially, pick partition with best quality across CDNs

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN Best CDN (buffering ratio) DMA ASN

Potential Improvement Example: CDN Switching Only

Details

If a partition has not enough clients use a larger partition ?

CDN1 (buffering ratio) DMA ASN

Potential Improvement Example: CDN Switching Only

Details

If a partition has not enough clients use a larger partition Use quality metric distribution to predict quality of a client on new CDN

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN

CDNs Vary in Performance over Geographies and Time

0,0% 10,0% 20,0% 30,0% 40,0% 50,0% 60,0% 70,0% 80,0% 90,0% 100,0%

Washington DC (Hagerstown):CMCS(33657) Milwaukee:ROADRUNNER-CENTRAL(20231) Green Bay - Appleton:SCRR-7015(7017) Denver:ASN-QWEST(209) Charlotte:SCRR-11426(11426) Washington DC (Hagerstown):ASN-CXA-ALL-CCI-22773-… Philadelphia:VZGNI-TRANSIT(19262) San Diego:SBIS-AS(7132) Las Vegas:ASN-CXA-LV-13432-CBS(13432) Madison:CHARTER-NET-HKY-NC(20115) Indianapolis:SBIS-AS(7132) Providence - New Bedford:ASN-CXA-ALL-CCI-22773-… Washington DC (Hagerstown):VZGNI-TRANSIT(19262) Hartford & New Haven:SBIS-AS(7132) Houston:SBIS-AS(7132) Grand Rapids - Kalamazoo - Battle Creek:SBIS-AS(7132) Atlanta:BELLSOUTH-NET-BLK(6389) Honolulu:HAWAIIAN-TELCOM(36149) Atlanta:COMCAST-7725(7725) Washington DC (Hagerstown):SPCS(10507) Orlando - Daytona Beach - Melbourne:EMBARQ-… Denver:CMCS(33652) Norfolk - Portsmouth - Newport News:ASN-CXA-ALL-CCI-… Saint Louis:CHARTER-NET-HKY-NC(20115) Cincinnati:FUSE-NET(6181) Phoenix:ASN-QWEST(209) Dallas - Fort Worth:VZGNI-TRANSIT(19262) Pittsburgh:CCCH-3(7016) Saint Louis:SBIS-AS(7132) San Francisco - Oakland - San Jose:SBIS-AS(7132) San Diego:ROADRUNNER-WEST(20001) Greenville - Spartansburg - Asheville -… Kansas City:SBIS-AS(7132) Columbus - OH:SCRR-10796(10796) Louisville:INSIGHT-COMMUNICATIONS-CORP-AS1(36727) Chicago:ATT-INTERNET3(6478) Kansas City:ASN-CXA-ALL-CCI-22773-RDC(22773) Miami - Fort Lauderdale:BELLSOUTH-NET-BLK(6389) Cleveland:NEO-RR-COM(11060) Chicago:VZGNI-TRANSIT(19262) Columbia - SC:SCRR-11426(11426) Dallas - Fort Worth:SBIS-AS(7132) Detroit:SBIS-AS(7132) Kansas City:SCRR-11955(11955) Los Angeles:CHARTER-NET-HKY-NC(20115) Miami - Fort Lauderdale:COMCAST-20214(20214) Cleveland:SCRR-10796(10796) West Palm Beach - Fort Pierce:COMCAST-20214(20214) San Diego:ASN-CXA-ALL-CCI-22773-RDC(22773) Seattle - Tacoma:ASN-QWEST(209) New York:CABLE-NET-1(6128) Portland - OR:ASN-QWEST(209) Oklahoma City:SBIS-AS(7132) Phoenix:ASN-CXA-ALL-CCI-22773-RDC(22773) Chicago:SBIS-AS(7132) Greensboro - High Point - Winston-Salem:SCRR-… Albany - Schenectady - Troy:RR-NYSREGION-ASN-01(11351) Tyler - Longview (Lufkin & Nacogdoches):SUDDENLINK-… Grand Rapids - Kalamazoo - Battle Creek:CMCS(33668) Houston:CMCS(33662) Hartford & New Haven:COMCAST-7015(7015) Eugene:ASN-QWEST(209) San Francisco - Oakland - San Jose:CMCS(33651)

Potential Improvement Example: CDN Switching Only

Oracle:

For each partition select best CDN and assume all clients in the partition selected that CDN

Historical:

For each partition select best CDN in previous epoch, and assign clients to that CDN in next epoch

CDN1 (buffering ratio) DMA ASN CDN2 (buffering ratio) DMA ASN