Back-Office Web Traffic on
The Internet
E. Pujol, P. Richter, B. Chandrasekaran, G. Smaragdakis, A
Feldmann, B. Maggs, and K-C. Ng
Presentation by: James Newman
Introduction
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Front-Office Traffic
○ End User
Back-Office Traffic
○ ex. CDNs
Vantage Points
○ 2 major IXPs
○ Major CDN
○ Major ISP
Main Contributions
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Identify and classify types of back-office traffic
○ Front-Office vs Back-Office
Identify implications on:
○ network protocol design
○ co-location strategies
Confirm that
○ CDNs have a sophisticated back-office
○ Back-Office Web traffic is significant
Data Sets
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Active Measurements
○ IP addresses
○ DNS reverse lookups
Identifying Back-Office Traffic
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Classify all IPs based on involvement in Web activity
Classify activities as
○ Client
■ Auctioneers
■ Crawlers
○ Server
■ Bidders
○ Both
■ Proxies
Back-Office Communication
Identifying Back-Office Traffic: Web Servers
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Two Caveats:
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Dynamically assigned IP addresses
Complex Dual behavior
Identifying Back-Office Traffic: IP Classification
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Most IPs are clients
11% of Web servers also act as clients
Closer Look
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Auctioneers
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Bidders
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282 IPs
Many co-located with Auctioneers
Crawlers
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> 300 IPs
2 search engines, an online social network, and a Web portal
> 3,000 IPs from 120 ASes
2 ASes host 72%
Content Delivery Proxies
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> 30,000 IPs
Estimating Back-Office Traffic
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Combine different data
sets
Backbone links more
variable than IXPs
Back-Office Traffic Characteristics
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Temporal Behavior
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Spatial Behavior
Back-Office Traffic Communication Patterns
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L-IXP trace
Auctioneers are most active
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232 million bid requests/hour on average
CDN Perspective
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Front-office vs. Back-office CDN Traffic
at least 25% of traffic is back-office
CDN Perspective cont.
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Intra-CDN/Public traffic
Substantial fraction of
traffic travels a short
distance
End User Perspective
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Improving Experience leads to increases in
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revenues
user engagement
Leads to an increase of back-office traffic
Implications
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Researchers
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Must differentiate between front and back-office traffic
Back-office infrastructure could lead to new protocols
■ different paradigms: Software Defined Networking
Operators
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Back-office links/traffic can influence front-office operations
May have different requirements than front-office traffic
■ customized services