Evaluation of the Proximity between Web Clients and their Local DNS Servers

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Evaluation of the Proximity between Web Clients
and their Local DNS Servers

• Z. Morley Mao
• Chuck Cranor, Fred Douglis,
• Misha Rabinovich, Oliver Spatscheck, and Jia Wang

2
Motivation originator problem

• Originator problem
• CDNs assume that clients are close to their local
  DNS servers
• Content Distribution Networks (CDNs)
• Try to deliver content from servers close to
  users
• Current server selection mechanisms
• Uses Domain Name System (DNS)

Verify the assumption that clients are close to
their local DNS servers
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Measurement setup

• Three components
• 1x1 pixel embedded transparent GIF image
• ltimg srchttp//xxx.rd.example.com/tr.gif
  height1 width1gt
• A specialized authoritative DNS server
• Allows hostnames to be wild-carded
• An HTTP redirector
• Always responds with 302 Moved Temporarily
• Redirect to a URL with client IP address embedded

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Embedded image request sequence
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Measurement data/stats
Site Participant Hit count Duration
1 att.com 20,816,927 2 months
2,3 Personal Web pages (commercial domain) 1,743 3 months
4 Research lab 212,814 3 months
5-7 University site 4,367,076 3 months
8-19 Personal Web pages (university domain) 26,563 3 months
Data type Count
Client-LDNS associations 4,253,157
HTTP requests 25,425,123
Unique client IPs 3,234,449
Unique LDNS Ips 157,633
Client-LDNS associations with a common IP 56,086
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Proximity metrics

• AS clustering
• Observes if client and LDNS belong to the same AS
• Network clustering
• Network cluster based on BGP routing information
  using longest prefix match
• Observes if client and LDNS belong to the same
  network cluster
• Roundtrip time correlation
• Correlation between message roundtrip times from
  a probe site to the client and its LDNS server
• Probe site represents a potential cache server
  location
• A crude metric, highly dependent on the probe site

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Proximity metrictraceroute divergence (TD)
Probe machine

• Use the last
• point of divergence
• TDMax(3,4)4

• Sample Probe sites
• NJ(UUNET), NJ(ATT),
• Berkeley(calren), Columbus(calren)
• size 48,908 client-LDNS pairs
• Median divergence 4
• Mean divergence 5.8-6.2
• Ratio of common to disjoint path length
• About 66 pairs traced have common
• path at least as long as disjoint path

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Proximity analysis resultsAS, network clustering
Metrics Client IPs HTTP requests
AS cluster 64 (88) 69 (92)
Network cluster 16 (66) 24 (70)

• AS clustering coarse-grained
• Network clustering fine-grained
• Most clients not in same routing entity as their
  LDNS
• Clients with LDNS in same cluster slightly more
  active
• Numbers in red indicate improvement possible.

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Impact on commercial CDNs

• total clients 3,234,449
• Verifiable client
• A client with LDNS in cluster, responding to our
  request, and has at least one cache server in its
  cluster
• Majority of misdirected clients for NAC have
  LDNS nonlocal

CDN (using AS clustering) CDN X CDN Y CDN Z
Clients with CDN server in cluster 1,679,515 1,215,372 618,897
Verifiable clients 1,324,022 961,382 516,969
Misdirected clients ( verifiable clients) 809,683 (60) 752,822 (77) 434,905 (82)
Clients with LDNS not in clients cluster ( misdirected clients) 443,394 (55) 354,928 (47) 262,713 (60)
CDN (using network aware clustering) CDN X CDN Y CDN Z
Clients with CDN server in cluster 264,743 156,507 103,448
Verifiable clients 221,440 132,567 90,264
Misdirected clients ( verifiable clients) 154,198 (68) 125,449 (94) 87,486 (96)
Clients with LDNS not in clients cluster ( misdirected clients) 145,276 (94) 116,073 (93) 84,737 (97)
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Conclusion

• DNS based server selection works well for
  coarse-grained load-balancing
• Server selection can be inaccurate if cache
  server density is high
• Future work
• Study alternatives to DNS based server selection
• Improved proximity evaluation