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Network Aware Forward Caching
Presenter: Alexandre Gerber
Jeffrey Erman, Mohammad T. Hajiaghayi, Dan Pei, Oliver Spatscheck
AT&T Labs Research
April 24th , 2009
Outline
What’s happening on the Internet?
• Traffic characterization
• Efficiency of existing delivery mechanisms
• Cacheability of HTTP traffic
Let’s revisit forward caching with a new twist!
• Network Aware Forward caching
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Data available to understand Internet traffic
Application Mix and HTTP requests (2007-2008):
100,000 broadband subscribers:
• US only: California, Texas, Illinois
• DSL not Cable
• Monitoring at the edge: BRAS (aggregation point)
• Application classification based on application header (e.g. MIME
type)
Efficiency of delivery mechanisms (October 2008):
Complete Netflow based PoP to PoP Traffic matrix of a US
broadband ISP
Air Miles between Points of Presence (PoP)
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Aggregate downstream traffic growth per subscriber
Stable over the last 8 years in aggregate!
20-30% per subscriber per year
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Let’s drill down: Application Mix over the last 2 years
HTTP is back and it is growing fast!
Thanks in part to Multimedia streams over HTTP!
Normalized Traffic Volumes
1.8
HTTP Web
1.6
HTTP Multimedia
1.4
Multimedia Other
File Sharing
1.2
TCP Other
1
UDP Other
Net News
0.8
0.6
0.4
0.2
Time
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Dec-08
Oct-08
Aug-08
Jun-08
Apr-08
Feb-08
Dec-07
Oct-07
Aug-07
Jun-07
Apr-07
Feb-07
0
Growing 3
times faster
than the
historical
aggregate
growth rate
Application Mix: January 2008
89% of potentially “reusable” content
Average
Busy Hour
Downstream
Multmedia Other-UDP
3%
Other
6%
Upstream
Other
8%
Other
5%
HTTP Web
28%
Other-UDP
13%
FileSharing
7%
Multmedia
Other
1%
Other-TCP
9%
HTTP Web
55%
HTTP
Multimedia
1%
FileSharing
22%
HTTP
Multimedia
15%
Other-UDP
Multmedia
3%
Other
7%
Other-TCP
27%
Other
6%
Other
9%
Other-UDP
10%
HTTP Web
39%
FileSharing
11%
Multmedia
Other
2%
HTTP Web
21%
HTTP
Multimedia
2%
Other-TCP
8%
HTTP
Multimedia
26%
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Other-TCP
22%
FileSharing
34%
Let’s keep drilling down: HTTP & Multimedia
It’s no longer about text and images, HTTP is the
workhorse for data delivery (streaming or direct downloads)
/w eb/flash
3%
/w eb/other
6%
/multimedia/xbox
3%
/multimedia/rtsp
3%
/w eb/audio
3%
/web/video
/w eb/https
4%
/web/text-image
/w eb/video
41%
/w eb/javascript
5%
/web/download
/w eb/audio
6%
/multimedia/rtp
1%
/multimedia/msstreaming
0%
/multimedia/shoutcas
t
0%
/web/video
/multimedia/rtmp
/web/javascript
/web/audio
/web/https
/web/audio
/web/flash
/w eb/dow nload
15%
/multimedia/rtsp
/multimedia/rtmp
15%
/multimedia/xbox
/multimedia/rtp
/web/other
/multimedia/ms-streaming
/multimedia/shoutcast
/w eb/text-image
23%
/http/download accounts for
10.2% traffic of overall traffic
while FTP accounts for 0.3%
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/w eb/video
72%
http accounts for 80% of
the multimedia content
delivery (flash)
Is this content delivered efficiently?
CDNs are doing a good job! P2P protocols are not!
2
1.8
Relative Air Miles
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
P2P
Web
Content
Provider 3
Content
Provider 2
Content
Provider 1
CDN 3
CDN 2
CDN 1
All Traffic
0
On-Net
CDN traffic is traversing 60%
fewer air miles than the
average traffic
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P2P flows are traversing
more air miles than the
average traffic
Are CDNs big enough to have an impact?
Yes, because they carry a significant fraction of large
files.
Percentage of Bytes Delivered
100%
90%
Other
80%
CDN
70%
60%
50%
40%
30%
20%
10%
0%
<1MB
1-10MB
10-100MB
100MB +
Overall
Flow Size
46% of very large files are
distributed by the 3 large CDNs
studied
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Are all the Points of Presence equal in the US?
No, some are much closer/farther from content
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Are all the Autonomous Systems traversing
the same distance on an ISP’s backbone?
No, some are much closer/farther from content
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What have we learned so far?
• 89% of the content during the busy hour may be “reusable”
(HTTP content, Multimedia streams, P2P / File sharing)
• 68% of the content during the busy hour is coming from HTTP
and HTTP is growing fast: it is the workhorse for data delivery
and includes the strong growth of Multimedia streams
• There are big differences in the delivery of content when
comparing air miles on an ISP’s backbone:
CDNs are doing a good job but other web content providers and P2P
protocols are not
• Distance traversed to reach PoPs varies significantly
•
What if we only cached the HTTP traffic that has to traverse
the longest distance?
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Let’s first understand if HTTP content is really
cacheable: Yes, it is!
32% of HTTP bytes served from the cache (24% of total
downstream traffic during the busy hour)
Cache size is reasonable (TB)
Based on 20,000 subscribers in January 2008
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Network Aware Forward caching
• HTTP forward cache that is aware of the network:
Understand the “cost” of each bit:
Backbone cost: Air Miles traversed on ISP’s network by each bit
• Source of traffic: free peering vs. paid transit traffic
•
•
Let’s only cache the bits that need it most
•
•
Tradeoff between bandwidth cost vs. caching cost
Caching decision made for:
Each PoP: no caches? X caches?
• And each “entity” in each PoP: BGP prefix, AS, IP addresses, etc.
•
•
•
Tradeoff between amount of traffic generated vs. storage requirement
Combinatorial, knapsack like, NP-hard problem
Can be solved with dynamic programming in pseudo polynomial time
• Or greedy heuristic by sorting “entities” in decreasing order of benefits
•
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Solution sensitive to backbone costs and
caching costs
Base scenario: $4/Mbps/Month for transit and backbone costs,
$20K for caches (400 Mbps, 4TB)
Sensitivity to Backbone costs
Sensitivity to Caching costs
The optimal deployment is to cache 68% of the traffic. This
is 37% better than a simple cache all solution!
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Drilling down into the optimal solution:
25% of the PoPs don’t need it a cache
Only for 15% of the PoPs does it make sense to cache
all the cacheable content
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Conclusion & Future Work
Contribution:
• Characterization of Internet traffic & its distribution efficiency:
There is a strong growth of HTTP content , especially multimedia streams
• Significant differences in distribution efficiencies
•
•
Proposed and evaluated Network Aware Forward Caching for HTTP
Future work:
•
Explore other mechanisms to better distribute Internet traffic:
network aware P2P, Multicast, Anycast, etc.
•
Extend study to other environments: wireless IP networks
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