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Evaluation of the Proximity between Web Clients and their Local DNS Servers. Z. Morley Mao UC Berkeley ([email protected]) C. Cranor, M. Rabinovich, O. Spatscheck, and J. Wang AT&T Labs-Research F. Douglis IBM Research. Origin servers. Clients. Clients. Motivation.

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evaluation of the proximity between web clients and their local dns servers

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

Z. Morley Mao

UC Berkeley ([email protected])

C. Cranor, M. Rabinovich, O. Spatscheck, and J. Wang

AT&T Labs-Research

F. Douglis

IBM Research

motivation
Origin servers

Clients

Clients

Motivation
  • Content Distribution Networks (CDNs)
    • Attempt to deliver content from servers close to users

Internet

Cache server

Cache server

Cache server

dns based server selection
www.service.com?

Server IP address

www.service.com?

www.service.com?

Server IP address

ns.service.com

Client.myisp.net

DNS based server selection
  • Originator problem
    • Assumes that clients are close to their local DNS servers

Authoritative DNS server

ns.service.com

Local DNS Server

ns.myisp.net

A.GTLD-SERVERS.NET

Verify the assumption that clients are close to

their local DNS servers

measurement setup
www.att.comMeasurement setup
  • Three components
    • 1x1 pixel embedded transparent GIF image
    • 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

1x1 transparent GIF

embedded image request sequence
1. HTTP GET request for the image

2. HTTP redirect to

IP10-0-0-1.cs.example.com

Client

[10.0.0.1]

Redirector for

xxx.rd.example.com

7. HTTP GET request for the image

8. HTTP response

6. Reply: content server

IP address

3. Request to resolve

IP10-0-0-1.cs.example.com

Content server for the image

4. Request to resolve IP10-0-0-1.cs.example.com

5. Reply: IP address of content server

Name server for

*.cs.example.com

Local DNS server

Embedded image request sequence
proximity metrics
Proximity metrics:
  • AS clustering
  • Network clustering
  • Traceroute divergence
  • Roundtrip time correlation
as clustering
AS clustering
  • Autonomous System (AS)
    • A single administrative entity with unified routing policy
  • Observes if client and LDNS belong to the same AS
network clustering
Network clustering
  • [Krishnamurthy,Wang sigcomm00]
  • Based on BGP routing information using the longest prefix match
  • Each prefix identifies a network cluster
  • Observes if client and LDNS belong to the same network cluster
traceroute divergence
client

Local DNS server

Traceroute divergence

Probe machine

a

  • [Shaikh et al. infocom00]
  • Use the last point of
  • divergence
  • Traceroute divergence:
  • Max(3,4)=4

b

1

1

2

2

3

3

4

roundtrip time correlation
Roundtrip time correlation
  • Correlation between message roundtrip times from a probe site to the client and its LDNS server
  • The probe site represents a potential cache server location
  • A crude metric, highly dependent on the probe site
aggregate statistics of as network clustering
Aggregate statistics of AS/network clustering
  • More than 13,000 ASes
    • Close to 75% total ASes
  • 440,000 unique prefixes
    • Close to 25% of all possible network clusters

 We have a representative data set

proximity analysis as network clustering
Proximity analysis:AS, network clustering
  • AS clustering: coarse-grained
  • Network clustering: fine-grained
  • Most clients not in the same routing entity as their LDNS
  • Clients with LDNS in the same cluster slightly more active
proximity analysis traceroute divergence
Proximity analysis:Traceroute divergence
  • Probe sites:
    • NJ(UUNET), NJ(AT&T), Berkeley(Calren), Columbus(Calren)
    • Sampled from top half of busy network clusters
  • Median divergence: 4
  • Mean divergence: 5.8-6.2
  • Ratio of common to disjoint path length
    • 72%-80% pairs traced have common path at least as long as disjoint path
improved local dns configuration
Improved local DNS configuration
  • For client-LDNS associations not in the same cluster, do we know a LDNS in the client’s cluster?

Client IPs

HTTP requests

impact on commercial cdns
Data set

Client-LDNS associations

LDNS-CDN associations

Available CDN servers

Verifiable clients:

w/ responsive

LDNS

Misdirected clients:

directed to a cache

not in client’s cluster

Clients with LDNS

not in same cluster

Impact on commercial CDNs

Client w/ CDN server

in cluster

conclusion
Conclusion
  • Novel technique for finding client and local DNS associations
    • Fast, non-intrusive, and accurate
  • DNS based server selection works well for coarse-grained load-balancing
    • 64% associations in the same AS
    • 16% associations in the same network cluster
  • Server selection can be inaccurate if server density is high
related work
Related work
  • Measurement methodology
    • IBM (Shaikh et al.)
      • Time correlation of DNS and HTTP requests from DNS and Web server logs
    • Univ of Boston (Bestavros et al.)
      • Assigning multiple IP addresses to a Web server
    • Differences from our work:
      • Our methodology: efficient, accurate, nonintrusive
    • Web bugs
  • Proximity metrics
    • Cisco’s Boomerang protocol: uses latency from cache servers to the LDNS
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