Transcript Multimedia Applications - CSE Labs User Home Pages

A Quick Primer on
CDN & Multimedia Networking
• Web Caching and CDN
• Multimedia vs. (conventional) Data Applications
– analog “continuous” media: encoding, decoding & playback
– service requirements
• Classifying multimedia applications
– Streaming (stored) multimedia
– Live multimedia broadcasting
– Interactive multimedia applications
• Making the best of best effort service: streaming Stored
Multimedia over “Best-Effort “Internet (optional)
– client buffering, rate adaption, etc.
• Large-scale video delivery over the Internet: YouTube
and Netflix case studies (optional)
Required Readings:
csci4221 Textbook, sections 2.2.5, 2.6, 9.1-9.2; optional 9.4-9.6
1
Web Performance:
impact of bottleneck link
assumptions:
 avg object size: 100K bits
 avg request rate from browsers to
origin servers:15/sec
 avg data rate to browsers: 1.50 Mbps
 RTT from gateway router to any
origin server: 2 sec
 access link rate: 1.54 Mbps
consequences:
 LAN utilization: 15% problem!
 access link utilization = 99%
 total delay = Internet delay + access
delay + LAN delay
= 2 sec + minutes + usecs
origin
servers
public
Internet
home or
institutional
network
1.54 Mbps
access link
100Mbps LAN
or
1 Gbps LAN
2
Web performance: fatter access link
assumptions:





avg object size: 100K bits
avg request rate from browsers to origin
servers:15/sec
avg data rate to browsers: 1.50 Mbps
RTT from institutional router to any
origin server: 2 sec
access link rate: 1.54 Mbps
154 Mbps
origin
servers
public
Internet
consequences:



LAN utilization: 15%
access link utilization = 99%
9.9%
total delay = Internet delay + access
delay + LAN delay
= 2 sec + minutes + usecs
msecs
home or
institutional
network
1.54 Mbps
154 Mbps
access link
100 Mbps LAN
or 1 Gbps LAN
Cost: increased access link speed
(not cheap!)
3
Improving Web Performance
via (local) browser cache
assumptions:





avg object size: 100K bits
avg request rate from browsers to origin
servers:15/sec
avg data rate to browsers: 1.50 Mbps
RTT from gateway router to any origin
server: 2 sec
access link rate: 1.54 Mbps
consequences:



origin
servers
public
Internet
problem!
LAN utilization: 15%
access link utilization = 99%
total delay = Internet delay + access
delay + LAN delay
= 2 sec + minutes + usecs
home or
institutional
network
1.54 Mbps
access link
100Mbps LAN
or
1 Gbps LAN
(local) browser cache:
when & how much it can help?
4
Conditional GET
• Goal: don’t send object if
cache has up-to-date cached
version
– no object transmission
delay
– lower link utilization
• cache: specify date of cached
copy in HTTP request
If-modified-since:
<date>
• server: response contains no
object if cached copy is up-todate:
HTTP/1.0 304 Not
Modified
client
server
HTTP request msg
If-modified-since: <date>
HTTP response
HTTP/1.0
304 Not Modified
object
not
modified
before
<date>
HTTP request msg
If-modified-since: <date>
HTTP response
HTTP/1.0 200 OK
object
modified
after
<date>
<data>
5
Web caches (proxy server)
goal: satisfy client request without involving origin server
• user sets browser: Web
accesses via cache
• browser sends all HTTP
requests to cache
– object in cache: cache
returns object
– else cache requests
object from origin
server, then returns
object to client
proxy
server
client
client
origin
server
origin
server
6
More about Web caching
• cache acts as both
client and server
– server for original
requesting client
– client to origin server
• typically cache is
installed by ISP
(university, company,
residential ISP)
why Web caching?
• reduce response time for
client request
• reduce traffic on an
institution’s access link
• Internet dense with caches:
enables “poor” content
providers to effectively
deliver content (so too does
P2P file sharing)
7
Caching example: install local cache
assumptions:





avg object size: 100K bits
avg request rate from browsers to origin
servers:15/sec
avg data rate to browsers: 1.50 Mbps
RTT from institutional router to any
origin server: 2 sec
access link rate: 1.54 Mbps
origin
servers
public
Internet
consequences:



LAN utilization: 15%
access link utilization = ?
total delay = ?
How to compute link
utilization, delay?
Cost: web cache (cheap!)
1.54 Mbps
access link
institutional
network
1 Gbps LAN
local web
cache
8
Caching example: install local cache
Calculating access link utilization,
delay with cache:
• suppose cache hit rate is 0.4
origin
servers
– 40% requests satisfied at cache,
60% requests satisfied at origin
public
Internet
access link utilization:
 60% of requests use access link
 data rate to browsers over access link
= 0.6*1.50 Mbps = .9 Mbps
 utilization = 0.9/1.54 = .58

 total delay
 = 0.6 * (delay from origin servers) +0.4
* (delay when satisfied at cache)
 = 0.6 (2.01) + 0.4 (~msecs) = ~ 1.2 secs
 less than with 154 Mbps link (and
cheaper too!)
All good!
1.54 Mbps
access link
institutional
network
1 Gbps LAN
local web
cache
Potential issues?
CSci4211:
Application Layer
9
Dealing with (Internet) Scale : CDNs
Challenges: one single “mega-server” can’t possibly
handle all requests for popular service
DNS
not
enough bandwidth: Netflix video streaming at 2
Mbps per connection
 only 5000 connections over fastest possible
(10Gbs) connection to Internet at one server
 30 Million Netflix customers
too far from some users: halfway around the globe to
someone
reliability: single point of failure
A single server
doesn’t “scale”
10
Content distribution networks
• challenge: how to stream content (selected
from millions of videos) to hundreds of
thousands of simultaneous users?
• option 1: single, large “mega-server”
–
–
–
–
single point of failure
point of network congestion
long path to distant clients
multiple copies of video sent over outgoing link
….quite simply: this solution doesn’t scale
11
Content distribution networks
• challenge: how to stream content (selected from
millions of videos) to hundreds of thousands of
simultaneous users?
• option 2: store/serve multiple copies of videos at
multiple geographically distributed sites (CDN)
– enter deep: push CDN servers deep into many access
networks
• close to users
• used by Akamai, 1700 locations
– bring home: smaller number (10’s) of larger clusters in
POPs near (but not within) access networks
• used by Limelight
12
CDN: “simple” content access scenario
Bob (client) requests video http://netcinema.com/6Y7B23V
 video stored in CDN at http://KingCDN.com/NetC6y&B23V
1. Bob gets URL for for video
http://netcinema.com/6Y7B23V
from netcinema.com
web page
1
2
6. request video from
KINGCDN server,
streamed via HTTP
netcinema.com
5
3. netcinema’s DNS returns URL
http://KingCDN.com/NetC6y&B23V
3
netcinema’s
authorative DNS
2. resolve http://netcinema.com/6Y7B23V
via Bob’s local DNS
KingCDN.com
4
4&5. Resolve
http://KingCDN.com/NetC6y&B23
via KingCDN’s authoritative DNS,
returns IP address of KIingCDN
server with video
which
KingCDN
authoritative DNS
13
CDN cluster selection strategy
• challenge: how does CDN DNS select
“good” CDN node to stream to client
– pick CDN node geographically closest to client
– pick CDN node with shortest delay (or min # hops) to
client (CDN nodes periodically ping access ISPs,
reporting results to CDN DNS)
• alternative: let client decide - give client a
list of several CDN servers
– client pings servers, picks “best”
3-14
Akamai CDN: quickie
• pioneered creation of CDNs circa 2000
• now: 61,000 servers in 1,000 networks in
70 countries
• delivers est 15-20% of all Internet traffic
• runs its own DNS service (alternative to
public root, TLD, hierarchy)
• hundreds of billions of Internet
interactions daily
• more shortly….
3-15
Multimedia and Quality of Service
Multimedia applications:
network audio and video
(“continuous media”)
QoS
network provides
application with level of
performance needed for
application to function.
16
Digital Audio
• Sampling the analog signal
– Sample at some fixed rate
– Each sample is an arbitrary real number
• Quantizing each sample
– Round each sample to one of a finite number of values
– Represent each sample in a fixed number of bits
4 bit representation
(values 0-15)
17
Audio Examples
• Speech
– Sampling rate: 8000 samples/second
– Sample size: 8 bits per sample
– Rate: 64 kbps
• Compact Disc (CD)
– Sampling rate: 44,100 samples/second
– Sample size: 16 bits per sample
– Rate: 705.6 kbps for mono,
1.411 Mbps for stereo
18
Why Audio Compression
• Audio data requires too much bandwidth
– Speech: 64 kbps is too high for a dial-up modem user
– Stereo music: 1.411 Mbps exceeds most access rates
• Compression to reduce the size
– Remove redundancy
– Remove details that human tend not to perceive
• Example audio formats
– Speech: GSM (13 kbps), G.729 (8 kbps), and G.723.3 (6.4
and 5.3 kbps)
– Stereo music: MPEG 1 layer 3 (MP3) at 96 kbps, 128
kbps, and 160 kbps
19
A few words about audio compression
• Analog signal sampled at
constant rate
– telephone: 8,000
samples/sec
– CD music: 44,100
samples/sec
• Each sample quantized, i.e.,
rounded
– e.g., 28=256 possible
quantized values
• Each quantized value
represented by bits
– 8 bits for 256 values
• Example: 8,000
samples/sec, 256 quantized
values --> 64,000 bps
• Receiver converts it back
to analog signal:
– some quality reduction
Example rates
• CD: 1.411 Mbps
• MP3: 96, 128, 160 kbps
• Internet telephony: 5.3 13 kbps
20
Digital Video
• Sampling the analog signal
– Sample at some fixed rate (e.g., 24 or 30 times per sec)
– Each sample is an image
• Quantizing each sample
– Representing an image as an array of picture elements
– Each pixel is a mixture of colors (red, green, and blue)
– E.g., 24 bits, with 8 bits per color
21
The
2272 x 1704
hand
CSci5221:
The
320 x 240
hand
Multimedia
22
A few words about video compression
• Video is sequence of
images displayed at
constant rate
Examples:
• MPEG 1 (CD-ROM) 1.5
Mbps
– e.g. 24 images/sec
• MPEG2 (DVD) 3-6 Mbps
• Digital image is array of
• MPEG4 (often used in
pixels
Internet, < 1 Mbps)
• Each pixel represented
Research:
by bits
• Layered (scalable) video
• Redundancy
– spatial
– temporal
– adapt layers to available
bandwidth
23
Video Compression: Within an Image
• Image compression
– Exploit spatial redundancy (e.g., regions of same color)
– Exploit aspects humans tend not to notice
• Common image compression formats
– Joint Pictures Expert Group (JPEG)
– Graphical Interchange Format (GIF)
Uncompressed: 167 KB
Good quality: 46 KB
Poor quality: 9 KB
24
Video Compression: Across Images
• Compression across images
– Exploit temporal redundancy across images
• Common video compression formats
– MPEG 1: CD-ROM quality video (1.5 Mbps)
– MPEG 2: high-quality DVD video (3-6 Mbps)
– Proprietary protocols like QuickTime and RealNetworks
25
MM Networking Applications
Classes of MM applications:
1) Streaming stored audio
and video
2) Streaming live audio and
video
3) Real-time interactive
audio and video
Jitter is the variability
of packet delays within
the same packet stream
CSci4211:
Fundamental
characteristics:
• Typically delay sensitive
– end-to-end delay
– delay jitter
• But loss tolerant:
infrequent losses cause
minor glitches
• Antithesis of data,
which are loss intolerant
but delay tolerant.
Multimedia Networking
26
Application Classes
• Streaming
– Clients request audio/video files from servers
and pipeline reception over the network and
display
– Interactive: user can control operation (similar
to VCR: pause, resume, fast forward, rewind,
etc.)
– Delay: from client request until display start
can be 1 to 10 seconds
27
Application Classes (more)
• Unidirectional Real-Time:
– E.g., real-time video broadcasting of a sport event
– similar to existing TV and radio stations, but delivery on
the network
– Non-interactive, just listen/view
• Interactive Real-Time :
– Phone conversation or video conference
– E.g., skype, Google handout, VoIP & SIP, …
– More stringent delay requirement than Streaming and
Unidirectional because of real-time nature
– Video: < 150 msec acceptable
– Audio: < 150 msec good, <400 msec acceptable
28
Multimedia Over Today’s Internet
TCP/UDP/IP: “best-effort service”
• no guarantees on delay, loss
?
?
?
?
?
?
But you said multimedia apps requires ?
QoS and level of performance to be
?
? effective!
?
?
Today’s Internet multimedia applications
use application-level techniques to mitigate
(as best possible) effects of delay, loss
29
Challenges
• TCP/UDP/IP suite provides best-effort, no
guarantees on expectation or variance of packet
delay
• Streaming applications delay of 5 to 10 seconds is
typical and has been acceptable, but performance
deteriorate if links are congested (transoceanic)
• Real-Time Interactive requirements on delay and
its jitter have been satisfied by over-provisioning
(providing plenty of bandwidth), what will happen
when the load increases?...
30
Internet (Stored) Multimedia:
Simplest Approach
• audio or video stored in file
• files transferred as HTTP object
– received in entirety at client
– then passed to player
audio, video not streamed:
• no, “pipelining,” long delays until playout!
31
Streaming Stored Multimedia
Streaming:
• media stored at source
• transmitted to client
• streaming: client playout begins
before all data has arrived
• timing constraint for still-to-be
transmitted data: in time for playout
32
Streaming Stored Multimedia:
What is it?
1. Video
pre-recorded
2. video
sent
network
delay
3. video received,
played out at client
time
streaming: at this time, client
playing out early part of video,
while server still sending later
part of video
33
Internet multimedia: Streaming Approach
• browser GETs metafile
• browser launches player, passing metafile
• player contacts server
• server streams audio/video to player
34
Streaming from a streaming server
• This architecture allows for non-HTTP protocol
between server and media player
• Can also use UDP instead of TCP.
35
Streaming Stored Multimedia
Application-level streaming
techniques for making the
best out of best effort
service:
– client side buffering
– use of UDP versus TCP
– multiple encodings of
multimedia
Media Player
•
•
•
•
jitter removal
decompression
error concealment
graphical user interface
w/ controls for
interactivity
36
Streaming Multimedia: UDP or TCP?
UDP
• server sends at rate appropriate for client (oblivious to
network congestion !)
– often send rate = encoding rate = constant rate
– then, fill rate = constant rate - packet loss
• short playout delay (2-5 seconds) to compensate for network
delay jitter
• error recover: time permitting
TCP
•
•
•
•
send at maximum possible rate under TCP
fill rate fluctuates due to TCP congestion control
larger playout delay: smooth TCP delivery rate
HTTP/TCP passes more easily through firewalls
37
Streaming Multimedia: Client Buffering
video sent at
Certain bit rates
variable
network
delay
constant bit
rate video
playout at client
buffered
video
client video
reception
client playout
delay
time
• Client-side buffering, playout delay compensate
for network-added delay, delay jitter
38
Streaming Multimedia: Client Buffering
“constant
”
drain
rate,
d
variable
fill
rate, x(t)
buffered
video
• Client-side buffering, playout delay compensate
for network-added delay, delay jitter
39
Streaming Stored Multimedia:
Interactivity
• VCR-like functionality: client can
pause, rewind, FF, push slider bar
– 10 sec initial delay OK
– 1-2 sec until command effect OK
• timing constraint for still-to-be transmitted data:
in time for playout
40
Streaming Live Multimedia
Examples:
• Internet radio talk show
• Live sporting event
Streaming
• playback buffer
• playback can lag tens of seconds after
transmission
• still have timing constraint
Interactivity
• fast forward impossible
• rewind, pause possible!
41
Interactive, Real-Time Multimedia
• applications: IP telephony,
video conference, distributed
interactive worlds
• end-end delay requirements:
– audio: < 150 msec good, < 400 msec OK
• includes application-level (packetization) and network
delays
• higher delays noticeable, impair interactivity
• session initialization
– how does callee advertise its IP address, port number,
encoding algorithms?
42
Large-scale Internet Video Delivery:
YouTube & Netflix Case Studies
Based on two active measurement studies we have
conducted
•Reverse-engineering YouTube Delivery Cloud
– Google’s New YouTube Architectural Design
•Unreeling Netflix Video Streaming Service
– Cloud-sourcing: Amazon Cloud Services & CDNs
43
YouTube Video Delivery Basics
Front end
web-servers
2. HTTP reply
containing html to
construct the web page 4. HTTP reply
and a link to stream the FLV stream
FLV file
Video-servers
(front end)
Internet
1. HTTP GET request
for video URL
3. HTTP GET
request
for FLV stream
User
44
www.youtube.com
45
Embedded Flash Video
46
Google’s New YouTube
Video Delivery Architecture
Three components
• Videos and video id space
• Physical cache hierarchy
•
 three tiers: primary,
secondary, & tertiary
 primary caches:
“Google locations” vs.
“ISP locations”
Implications:
Layered organization of
a) YouTube videos are not replicated at all locations!
namespaces
b) only replicated at (5) tertiary cache locations
 representing “logical”
c) Google
likely utilizes some form of location-aware
video servers
load-balancing
(among primary cache locations)
 five
“anycast” namespaces
 two “unicast” namespaces
47
YouTube Video Id Space
• Each YouTube video is assigned a unique id
e.g., http://www.youtube.com/watch?v=tObjCw_WgKs
• Each video id is 11 char string
• first 10 chars can be any alpha-numeric values [0-9, a-z,
A-Z] plus “-” and “_”
• last char can be one of the 16 chars {0, 4, 8, ..., A, E, ...}


Video id space size: 6411
Video id’s are randomly
distributed in the id space
CSci4211:
Multimedia Networking
48
Physical Cache Hierarchy & Locations
~ 50 cache locations
• ~40 primary locations
• including ~10 non-
Google ISP locations
• 8 secondary locations
• 5 tertiary locations
Geo-locations using
• city codes in unicast hostnames,
e.g., r1.sjc01g01.c.youtube.com
• low latency from PLnodes (< 3ms)
• clustering of IP addresses using
latency matrix
P: primary
S: secondary
T: Tertiary
CSci4211:
Multimedia Networking
49
Layered Namespace Organization
Two types of namespaces
– Five “anycast” namespaces
• lscache: “visible” primary ns
• each ns representing fixed #
of “logical” servers
• logical servers mapped to
physical servers via DNS
– 2 “unicast” namespaces
• rhost: google locations
• rhostisp: ISP locations
• mapped to a single server
Examples:
50
YouTube Video Delivery Dynamics:
Summary
• Locality-aware DNS resolution
• Handling load balancing & hotspots
– DNS change
– Dynamic HTTP redirection
– local vs. higher cache tier
• Handling cache misses
– Background fetch
– Dynamic HTTP redirection
51
What Makes Netflix Interesting?
• Commercial, feature-length movies and TV shows
 and not free; subscription-based
• Nonetheless, Netflix is huge!


25 million subscribers and ~20,000 titles (and growing)
consumes 30% of peak-time downstream bandwidth in North
America
• A prime example of cloud-sourced architecture
 Maintains only a small “in-house” facility for key functions




e.g., subscriber management (account creation, payment, …)

user authentication, video search, video storage, …

Akamai, Level 3 and Limelight
Majority of functions are sourced to Amazon cloud
(EC2/S3)
DNS service is sourced to UltraDNS
Leverage multiple CDNs (content-distribution networks) for
video delivery
Netflix Architecture
• Netflix has its own “data center” for certain crucial operations
(e.g., user registration, billing, …)
• Most web-based user-video interaction, computation/storage
operations are cloud-sourced to Amazon AWS
• Video delivery used to out/cloud-sourced to 3 CDNs; now
increasingly is delivered via its “own” OpenConnect boxes
placed at participating ISPs
• Users need to use MS
Silverlight or other
video player for video
streaming
Netflix Videos and Video Chunks
• Netflix uses a numeric ID to identify each movie
– IDs are variable length (6-8 digits): 213530, 1001192,
70221086
– video IDs do not seem to be evenly distributed in the
ID space
– these video IDs are not used in playback operations
• Each movie is encoded in multiple quality levels, each is
identified by a numeric ID (9 digits)
– various numeric IDs associated with the same movie appear to
have no obvious relations
54
Netflix Videos and Video Chunks
• Videos are divided in “chunks” (of roughly 4 secs),
specified using (byte) “range/xxx-xxx?” in the URL path:
Limelight:
http://netflix-094.vo.llnwd.net/s/stor3/384/534975384.ismv/range/057689?p=58&e=1311456547&h=2caca6fb4cc2c522e657006cf69d4ace
Akamai:
http://netflix094.as.nflximg.com.edgesuite.net/sa53/384/534975384.ismv/range/0
-57689?token=1311456547_411862e41a33dc93ee71e2e3b3fd8534
Level3:
http://nflx.i.ad483241.x.lcdn.nflximg.com/384/534975384.ismv/range/057689?etime=20110723212907&movieHash=094&encoded=06847414df0656e6
97cbd
• Netflix uses a version of (MPEG-)DASH for video streaming
55
DASH: dynamic adaptive streaming over HTTP
• Not really a protocol; it provides formats to enable efficient
and high-quality delivery of streaming services over the
Internet
– Enable HTTP-CDNs; reuse of existing technology (codec, DRM,…)
– Move “intelligence” to client: device capability, bandwidth
adaptation, …
• In particular, it specifies Media Presentation Description (MPD)
56
Ack & ©: Thomas Stockhammer
DASH Data Model and Manifest Files
• DASH MPD:
Segment Info
Initialization Segment
http://www.e.com/ahs-5.3gp
Media Presentation
Period, start=0s
Media Segment 1
Period,
•start=100
•baseURL=http://www.e.com/
…
…
Period, start=100s
Representation 1
Representation 2
Period, start=295s
…
100kbit/s
•bandwidth=500kbit/s
•width 640, height 480
…
Segment Info
500kbit/s
…
Representation 1
…
duration=10s
Template:
./ahs-5-$Index$.3gs
start=0s
http://www.e.com/ahs-5-1.3gs
Media Segment 2
start=10s
http://www.e.com/ahs-5-2.3gs
Media Segment 3
start=20s
http://www.e.com/ahs-5-3.3gh
…
• Segment Indexing: MPD only; MPD+segment; segment only
Media Segment 20
start=190s
Segment Index in MPD only
<MPD>
...
<URL
sourceURL="seg1.mp4"/>
<URL
sourceURL="seg2.mp4"/>
<MPD>
</MPD>
...
<URL sourceURL="seg.mp4" range="0-499"/>
<URL sourceURL="seg.mp4" range="500999"/>
</MPD>
seg1.mp4
http://www.e.com/ahs-5-20.3gs
seg2.mp4
...
seg.mp4
57
Ack & ©: Thomas Stockhammer
Netflix Manifest Files
• A manifest file contains metadata
• Netflix manifest files contain a lot of information
o
o
o
Available bitrates for audio, video and trickplay
MPD and URLs pointing to CDNs
CDNs and their "rankings"
<nccp:cdn>
<nccp:name>level3</nccp:name>
<nccp:cdnid>6</nccp:cdnid>
<nccp:rank>1</nccp:rank>
<nccp:weight>140</nccp:weight>
</nccp:cdn>
<nccp:cdn>
<nccp:name>limelight</nccp:name>
<nccp:cdnid>4</nccp:cdnid>
<nccp:rank>2</nccp:rank>
<nccp:weight>120</nccp:weight>
</nccp:cdn>
<nccp:cdn>
<nccp:name>akamai</nccp:name>
<nccp:cdnid>9</nccp:cdnid>
<nccp:rank>3</nccp:rank>
<nccp:weight>100</nccp:weight>
</nccp:cdn>
58
Netflix Manifest Files …
A section of the manifest containing the base URLs, pointing to
CDNs
<nccp:downloadurls>
<nccp:downloadurl>
<nccp:expiration>1311456547</nccp:expiration>
<nccp:cdnid>9</nccp:cdnid>
<nccp:url>http://netflix094.as.nflximg.com.edgesuite.net/sa73/531/943233531.ismv?token=1311456547_e329d42
71a7ff72019a550dec8ce3840</nccp:url>
</nccp:downloadurl>
<nccp:downloadurl>
<nccp:expiration>1311456547</nccp:expiration>
<nccp:cdnid>4</nccp:cdnid>
<nccp:url>http://netflix094.vo.llnwd.net/s/stor3/531/943233531.ismv?p=58&amp;e=1311456547&amp;h=8adaa52cd06db9219790bbdb323
fc6b8</nccp:url>
</nccp:downloadurl>
<nccp:downloadurl>
<nccp:expiration>1311456547</nccp:expiration>
<nccp:cdnid>6</nccp:cdnid>
<nccp:url>http://nflx.i.ad483241.x.lcdn.nflximg.com/531/943233531.ismv?etime=20110723212907&amp;movieHash
=094&amp;encoded=0473c433ff6dc2f7f2f4a</nccp:url>
</nccp:downloadurl>
</nccp:downloadurls>
59
Netflix: Adapting to Bandwidth Changes
• Two possible approaches


Increase/decrease quality level using DASH
Switch CDNs
• Experiments


Play a movie and systematically throttle available
bandwidth
Observe server addresses and video quality
• Bandwidth throttling using the “dummynet” tool


Throttling done on the client side by limiting how fast it
can download from any given CDN server
First throttle the most preferred CDN server, keep
throttling other servers as they get selected
60
Adapting to Bandwidth Changes
• Lower quality levels in response to lower bandwidth
• Switch CDN only when minimum quality level cannot be
supported
• Netflix seems to use multiple CDNs only for failover
purposes!
61
CDN Bandwidth Measurement
• Use both local residential hosts and PlanetLab nodes
 13 residential hosts and 100s PlanetLab nodes are used
 Each host downloads small chunks of Netflix videos from all
three CDN servers by replaying URLs from the manifest files
• Experiments are done for several hours every day for about
3 weeks
 total experiment duration was divided into 16 second intervals
 the clients downloaded chunks from CDN 1, 2 and 3 at the
beginning of seconds 0, 4 and 8.
 at the beginning of the 12th second, the clients tried to
download the chunks from all three CDNs simultaneously
• Measure bandwidth to 3 CDNs separately as well as the
combined bandwidth
• Perform analysis at three time-scales



average over the entire period
daily averages
instantaneous bandwidth
CSci4211:
Multimedia Networking
62
There is no Single Best CDN
63
Video on Demand: OTT
Over-the-top: VoD provider uses public Internet to
deliver content (augmented by CDNs)
 use Internet best effort service (no
guarantees)
 ISPs (AT&T, Comcast, Version) relegated to
role of “bit pipes” - carrying traffic but not
offering “services”
 unicast HTTP (e.g., DASH)
 minimal infrastructure costs
 user subscription fee or advertising revenue
64
Video on Demand: in-network
in-network: access network owner (Comcast, Verizon)
provides VoD service to its customers
 high-quality user experience (QoE)
because ISP manages network
 servers in same edge network as viewers
 efficient network use: multicast possible
(one packet to many receivers)
 ISP pays infrastructure cost
OTT versus in-network: who will win, and why?
65