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Relative Network Positioning via CDN Redirections

Relative Network Positioning via CDN Redirections

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Relative Network Positioning via CDN Redirections

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  1. Relative Network Positioning via CDN Redirections Ao-Jan Su, David R. Choffnes,Fabián E. Bustamante and Aleksandar Kuzmanovic Department of EECS Northwestern University IEEE ICDCS 2008

  2. Network Positioning • Why do we need network positioning systems? • Emerging large scale distributed systems can benefit from selecting among alternative nodes • Example: select an on-line gaming server • All-to-all measurements are not scalable • Current approaches • Provide network positioning services in a scalable way (e.g. landmark based) • Clear tradeoffs • Precision vs. overhead • Precision vs. deployment • And others

  3. Observations • Content distribution networks (e.g., Akamai) improve web performance by • Performing extensive network measurements • Redirecting clients to their closest replica servers • Publishing the results through DNS Can we reuse those measurements collected by CDNs to build a network positioning system?

  4. CDNs Basics • Web client’s request redirected to ‘close’ by server • Client gets web site’s DNS CNAME entry with domain name in CDN network • Hierarchy of CDN’s DNS servers direct client to 2 nearby servers Hierarchy of CDN DNS servers Internet Customer DNS servers Multiple redirections to find nearby edge servers Web replica servers (3) (4) Client is given 2 nearby web replica servers (fault tolerance) (5) Client gets CNAME entry with domain name in Akamai (2) (6) Client requests translation for yahoo LDNS (1) Web client

  5. Our approach • CDN-based Relative Network Positioning (CRP) • Clients are redirected to currently closest replica servers in general • CDN’s redirections are primarily driven by network conditions (latency) [Su et al. 2006] • Inferring relative network distance by overlapping CDN replica servers R1 R2 A C B

  6. Uses of CRP • Closest node selection • Select the closest node (shortest latency) from a group of candidates (e.g. select the closest on-line gaming server) • Methodology • Encode redirection frequency from a node to its redirected replica servers by a vector • Compare similarity (cosine similarity) of nodes’ redirection vectors to estimate proximity R1 Replica servers Server A 0.8 0.2 R2 Server B R3 Client

  7. Uses of CRP (Cont.) • Clustering • Select a set of nodes that are close to each other (e.g. replicate content to a group of nodes) • Methodology • Select cluster centers • Assign strong mapping peers to the cluster centers

  8. Evaluation Goals • Comparing the performance of CRP’s closest node selection to • Ground truth – active measurements • A state of the art network positioning system – Meridian [Wong 2005] • With respect to • Accuracy • Scalability • Deployment • Overhead Meridian: Closest node selection

  9. Experiment Setup • Globally distributed nodes • 1000 DNS servers as clients • 240 Planet Lab nodes as candidate servers (on the same nodes as our reference system – Meridian) • Concurrent data collection • Monitoring CDN redirections by recursive DNS queries for CRP • Querying Meridian via its interface • Measuring end-to-end latencies by pings as the ground truth

  10. Selecting the closest node • Clients are not close to any servers due to • Limited Planet Lab nodes coverage (Meridian) • Located in areas not well served by CDNs (CRP) CRP outperforms Meridian by 25% of the nodes due to larger deployment of CDN replica servers CRP’s accuracy is comparable to its alternative without active measurements and dedicated infrastructure CRP’s recommendations for 65% of nodes differ from Meridian by < 7ms

  11. Selecting the closest node (Cont.) Relative Error: estimated latency – ground truth CRP’s is quite accurate comparing to ground truth, with virtually no measurement overhead 80% of CRP nodes have relative error < 50ms

  12. Load on CDN’s DNS System Rank: rank 0 is the closest server • Low probe frequency • Smaller overhead • Less accurate • Miss overlapping replica servers • 100 mins probe frequency • Appropriate for 95% of nodes • Much less than CDN’s DNS TTL (20 secs) • Overhead is too small to impact CDN’s operations • High probe frequency • Can Improve accuracy • Larger overhead

  13. Load on CRP Clients • Small history • Sufficient for CRP • Small overhead • Capture network dynamics • Large history • More refined results • Larger computation overhead

  14. You May Be Wondering • “Will CDNs be unhappy because of CRP?” • CRP nodes behaves as regular web clients • CRP’s overhead does not impact CDN’s daily operations • Could be an additional service provided by CDNs • “What if CDNs change their redirection policy?” • CRP’s goal aligns with CDNs • Our approach is not restricted to a specific CDN, CRP can reuse results from other measurement infrastructures

  15. Summary • CRP discovers relative positions of end hosts • Closest node selection • Clustering • Key features of CRP • Accurate • Light-weight • Reuse CDN’s network measurements • Scalable • No dedicated infrastructure is required

  16. Cosine Similarity