1 / 21

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 (zmao@eecs.berkeley.edu) C. Cranor, M. Rabinovich, O. Spatscheck, and J. Wang AT&T Labs-Research F. Douglis IBM Research. Origin servers. Clients. Clients. Motivation.

yahto
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluation of the Proximity between Web Clients and their Local DNS Servers Z. Morley Mao UC Berkeley (zmao@eecs.berkeley.edu) C. Cranor, M. Rabinovich, O. Spatscheck, and J. Wang AT&T Labs-Research F. Douglis IBM Research

  2. 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

  3. 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

  4. www.att.com Measurement setup • Three components • 1x1 pixel embedded transparent GIF image • <img src=http://xxx.rd.example.com/tr.gif height=1 width=1> • 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

  5. 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

  6. Measurement Data

  7. Measurement statistics

  8. Proximity metrics: • AS clustering • Network clustering • Traceroute divergence • Roundtrip time correlation

  9. AS clustering • Autonomous System (AS) • A single administrative entity with unified routing policy • Observes if client and LDNS belong to the same AS

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. Impact on commercial CDNsAS clustering

  19. Less than 10% of all clients Impact on commercial CDNsNetwork clustering

  20. 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

  21. 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

More Related