1. Content Distribution Networks
CPE 401 / 601 Computer Network Systems
Content Distribution Networks
Modified from Ravi Sundaram, Janardhan R. Iyengar, and others

2. Content and Internet Traffic
Shifts seismically ( FTPWebP2Pvideo)
Has many small/unpopular and few large/popular flows
Zipf popularity distribution, 1/k
Shows up as a line on log-log plot

3. Server Farms and Web Proxies (1)
Server farms enable large-scale Web servers:
Front-end load-balances requests over servers
Servers access the same backend database

4. Server Farms and Web Proxies (2)
Proxy caches help organizations to scale the Web
Caches server content over clients for performance
implements organization policies (e.g., access)

5. HTTP Caching Clients often cache documents
When should origin be checked for changes?
Every time? Every session? Date?
HTTP includes caching information in headers
HTTP 0.9/1.0 used: “Expires: <date>”; “Pragma: no-cache”
HTTP/1.1 has “Cache-Control”
“No-Cache”, “Private”, “Max-age: <seconds>”
“E-tag: <opaque value>”
If not expired, use cached copy
If expired, use condition GET request to origin
“If-Modified-Since: <date>”, “If-None-Match: <etag>”
304 (“Not Modified”) or 200 (“OK”) response
Ask: What’s the problem with just using date for IMS queries?
- Web servers may generate content per-client Examples: CGI scripts, things like google customized home,
vary with cookies, client IP, referrer, accept-encoding, etc.

6. Web Proxy Caches User configures browser: Web accesses via cache
Browser sends all HTTP requests to cache
Object in cache: cache returns object
Else: cache requests object from origin, then returns to client
origin
server
Proxy
server
HTTP request
HTTP request
client
HTTP response
HTTP response
HTTP request
HTTP response
client
origin
server

7. Caching Example (1) Assumptions Consequences
Average object size = 100K bits
Avg. request rate from browsers to origin servers = 20/sec
Delay from institutional router to any origin server and back to router = 2 sec
Consequences
Utilization on LAN = 20%
Utilization on access link = 100%
Total delay = Internet delay + access delay + LAN delay
= 2 sec + minutes + milliseconds
origin
servers
public
Internet
1.5 Mbps
access link
institutional
network
10 Mbps LAN

8. Caching Example (2) Possible Solution Consequences
Increase bandwidth of access link to, say, 10 Mbps
Often a costly upgrade
Consequences
Utilization on LAN = 20%
Utilization on access link = 20%
Total delay = Internet delay + access delay + LAN delay
= 2 sec + milliseconds
origin
servers
public
Internet
10 Mbps
access link
institutional
network
10 Mbps LAN

9. Caching Example (3) Install Cache Consequences Suppose hit rate is 60%
60% requests satisfied almost immediately (say 10 msec)
40% requests satisfied by origin
Utilization of access link down to 53%, yielding negligible delays
Weighted average of delays
= .6*2 s + .4*10 ms < 1.3 s
origin
servers
public
Internet
1.5 Mbps
access link
institutional
network
10 Mbps LAN
institutional
cache

10. When a single cache isn’t enough
What if the working set is > proxy disk?
Cooperation!
A static hierarchy
Check local
If miss, check siblings
If miss, fetch through parent
Internet Cache Protocol (ICP)
ICPv2 in RFC 2186 (& 2187)
UDP-based, short timeout
public
Internet
Parent
web cache

11. Problems with discussed approaches: Server farms and Caching proxies
Server farms do nothing about problems due to network congestion, or to improve latency issues due to the network
Caching proxies serve only their clients, not all users on the Internet
Content providers (say, Web servers) cannot rely on existence and correct implementation of caching proxies
Accounting issues with caching proxies.
For instance, needs to know the number of hits to the webpage for advertisements displayed on the webpage

12. Problems But…most dynamic content is small,
Significant fraction (>50% ) of HTTP objects un-cacheable
Sources of dynamism?
Dynamic data: Stock prices, scores, web cams
CGI scripts: results based on passed parameters
Cookies: results may be based on passed data
SSL: encrypted data is not cacheable
Advertising / analytics: owner wants to measure # hits
Random strings in content to ensure unique counting
But…most dynamic content is small,
while static content is large (images, video, .js, .css, etc.)

13. Content Distribution Networks (CDNs)
Content providers are CDN customers
Content replication
CDN company installs thousands of servers throughout Internet
In large datacenters
or, close to users
CDN replicates customers’ content
When provider updates content, CDN updates servers
origin server
in North America
CDN distribution node
CDN server
in S. America
CDN server
in Asia
CDN server
in Europe

14. The Web: Simple on the Outside…
Content Providers
End Users
Internet

15. …But Problematic on the Inside
Content Providers
Peering
Points
Network Providers
End
Users
UUNet
NAP
Qwest
NAP
AOL

16. Why does my click not work
Latency - Browser takes a long time to load the page
Packet Loss - Browser hangs, user needs to hit refresh
Jitter - Streams are jerky
Server load - Browser connects but does not fully load the page
Broken/missing content
Mention that in a nut-shell this is the problem you tried to solve and here you will talk about the ideas that go into the solution

17. The Akamai Solution Content Providers Servers at Network Edge
End Users
NAP
NAP

18. Benefits of CDNs Improved end-user experience
reduce latency
reduce loss
reduce jitter
Reduced network congestion
Increased scalability
Improved fault-tolerance
Reduced vulnerability
Reduced costs
Network congestion is reduced because traffic is moved to the edge
Scalability is improved because with more servers more requests can be served
Fault-tolerance improved because no single point of failure - distributed system
Vulnerability reduced because denial of service attacks are diffused
Reduced costs because of economies of scale, multiplexing and buying in bulk

19. CDN vs. Caching Proxies
Caches are used by ISPs to reduce bandwidth consumption, CDNs are used by content providers to improve quality of service to end users
Caches are reactive, CDNs are proactive
Caching proxies cater to their users (web clients) and not to content providers (web servers),
CDNs cater to the content providers (web servers) and clients
CDNs give control over the content to the content providers, caching proxies do not

20. CDNs – Content Delivery Networks (1)
CDNs scale Web servers by having clients get content from a nearby CDN node (cache)

21. Content Delivery Networks (2)
Directing clients to nearby CDN nodes with DNS:
Client query returns local CDN node as response
Local CDN node caches content for nearby clients and reduces load on the origin server

22. Content Delivery Networks (3)
Origin server rewrites pages to serve content via CDN
Traditional Web page on server
Page that distributes content via CDN

23. CDN Architecture Origin Server CDN Client Request Distribution Routing
Infrastructure
Distribution
and
Accounting
Infrastructure
Surrogate

24. CDN Components
Content Delivery Infrastructure: Delivering content to clients from surrogates
Request Routing Infrastructure: Steering or directing content request from a client to a suitable surrogate
Distribution Infrastructure: Moving or replicating content from content source (origin server, content provider) to surrogates
Accounting Infrastructure: Logging and reporting of distribution and delivery activities

25. Server Interaction with CDN
Origin
Server
Distribution
Infrastructure 1
Origin server pushes new content to CDN OR
CDN pulls content from origin server
Accounting
Infrastructure 2
2. Origin server requests logs and other accounting info from CDN OR
CDN provides logs and other accounting info to origin server

26. Client Interaction with CDN
Surrogate
(DE)
(CA)
CDN
delaware.unr.akamai.com
california.cnn.akamai.com 1
1. Hi! I need 2
Go to surrogate
california.unr.akamai.com
Request
Routing
Infrastructure 3
3. Hi! I need content /sochi Q:
How did the CDN choose the California surrogate over the Delaware surrogate ?

27. Content Distribution Networks & Server Selection
Replicate content on many servers
Challenges
How to replicate content
Where to replicate content
How to find replicated content
How to choose among known replicas
How to direct clients towards replicas

28. Server Selection Which server?
Lowest load: to balance load on servers
Best performance: to improve client performance
Based on Geography? RTT? Throughput? Load?
Any alive node: to provide fault tolerance
How to direct clients to a particular server?
As part of routing: anycast, cluster load balancing
As part of application: HTTP redirect
As part of naming: DNS
Ask: What things might you want to pick the server using?
How do you direct clients to a server? Anyone know how Akamai works?

29. Trade-offs between approaches
Routing based (IP anycast)
Pros: Transparent to clients, circumvents many routing issues
Cons: Little control, complex, scalability
Application based (HTTP redirects)
Pros: Application-level, fine-grained control
Cons: Additional load and RTTs, hard to cache
Naming based (DNS selection)
Pros: Well-suitable for caching, reduce RTTs
Cons: Request by resolver not client, request for domain not URL, hidden load factor of resolver’s population
Much of this data can be estimated “over time”

30. DNS based Request-Routing
Common due to the ubiquity of DNS as a directory service
Specialized DNS server inserted in DNS resolution process
DNS server is capable of returning a different set of A, NS or CNAME records based on policies/metrics

31. DNS based Request-Routing
How does the Akamai DNS know which surrogate is closest ?
Surrogate
Akamai
CDN
cis.ucdavis.edu
california.unr.akamai.com
delaware.cnn.akamai.com
Akamai DNS A
DNS response:
DNS query:
Session
local DNS server (louie.ucdavis.edu)
DNS query:
DNS response: A

32. DNS based Request-Routing
Akamai DNS
Surrogate
Akamai
CDN
cis.ucdavis.edu
Measurement results
Measure to
Client DNS
DNS response
DNS query
Measurements
Session
local DNS server
(louie. ucdavis.edu)
DNS query
DNS response

33. What is the Mapping Problem
Problem of directing requests to servers so as to optimize end-user experience
reduce latency
reduce loss
reduce jitter
Assumption - servers are fine
Applicable to 2 mirrors or 1000s of Akamai locations

34. Server Selection Metrics
Network Proximity (Surrogate to Client):
Network hops (traceroute)
Internet mapping services (NetGeo, IDMaps) …
Surrogate Load:
Number of active TCP connections
HTTP request arrival rate
Other OS metrics
Bandwidth Availability

35. Attempt Measure which is closer Measure frequently
Closeness changes over time
Measure frequently
Bothers people
Too many to do
If nobody asks why not use DNS queries then bring it up by myself - point out that % of nameservers are closed to the world, that their state tends to be variable, different size packets cannot be used and sysadmins tend to be sensitive about repeated queries.
Talk about as a naive attempt to do the impossible. Experts said it was not possible but say that we learnt important things. Never eliminate the obvious without trying it. Also known as the open design principle - need all the help we can get.
Scalability is the key problem. Say that 10s was set as a challenge
~500,000 unique nameservers on any given day
10 sec per measurement cycle

36. Idea Topology Congestion relatively static changes in BGP time
order of hours if not days
Congestion
dynamic
changes in round-trip time
order of milliseconds
Mention that the naive approach taught us a lot. To some extent we were theoreticians and by doing we learnt.

37. Set cover Let sets represent proxy points1 2 3 4
Let sets represent proxy points
Let elements represent nameservers
Find minimum collection of proxy points covering nameservers
X covers 1, 2, 3 and 4

38. Topology Discovery
At each mirror maintain list of partial paths to nameservers
At each epoch extend paths by 1, in randomized fashion, and exchange with other mirror
If the two (partial) paths to a namerver have intersected then declare that nameserver done.
If path has reached forbidden IP then wait
Use pair of proxies in case of failure

39. 90,000 proxy points (clusters)
Topology Discovery
500,000 nameservers
reduced to
90,000 proxy points (clusters)
Problem - Still too many measurements to do. 90,000 measurements every 10s with 32B packets requires a few Mbps per mirror.
State that fewer than 1% or 5,000 require proxy pairs.
Solution - Importance based sampling
7,000 account for 95% end-user load!
Maps built every 10s

40. CDN Types (Skeletal) CDNs Hosting CDN Relaying CDN
Partial Site Content Delivery
Full Site Content Delivery
Request Routing Techniques
DNS based
URL Rewriting

41. Value of a CDN Scale: Aggregate infrastructure size
Reach: Diversity of content locations
diverse placement of surrogates
Request routing efficiency, delivery techniques

42. How Akamai Works Clients fetch html document from primary server
E.g. fetch index.html from unr.edu
URLs for replicated content are replaced in HTML
E.g. <img src=“ replaced with
<img src=
Or, cache.unr.edu, and UNR adds CNAME (alias) for
cache.unr.edu  a73.g.akamai.net
Client resolves aXYZ.g.akamaitech.net hostname
Maps to a server in one of Akamai’s clusters

43. How Akamai Works Akamai only replicates static content
At least, simple version. Akamai also lets sites write code that run on their servers, but that’s a pretty different beast
Modified name contains original file name
Akamai server is asked for content
Checks local cache
Check other servers in local cluster
Otherwise, requests from primary server and cache file
CDN is a large-scale, distributed network
Akamai has ~25K servers spread over ~1K clusters world-wide
Why do you want servers in many different locations?
Why might video distribution architectures be different?

44. How Akamai Works Root server gives NS record for akamai.net
This nameserver returns NS record for g.akamai.net
Nameserver chosen to be in region of client’s name server
TTL is large
g.akamai.net nameserver chooses server in region
Should try to chose server that has file in cache
Uses aXYZ name and hash
TTL is small
Small modification to before:
CNAME cache.unr.edu  cache.unr.edu.akamaidns.net
CNAME cache.unr.edu.akamaidns.net  a73.g.akamai.net

45. How Akamai Works – Already Cached
unr.edu (content provider)
DNS root server
Akamai server
GET index.html
Akamai high-level DNS server 1 2
Akamai low-level DNS server 7 8
Nearby
hash-chosen Akamai
server 9 10
End-user
GET /unr.edu/foo.jpg

46. How Akamai Works unr.edu (content provider) DNS root server
Akamai server
GET foo.jpg 12
GET index.html 11
Akamai high-level DNS server 5 1 2 3 6 4
Akamai low-level DNS server 7 8
Nearby
hash-chosen Akamai
server 9 10
End-user
GET /unr.edu/foo.jpg