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Evaluation of Delivery Techniques for Dynamic Web Content

Evaluation of Delivery Techniques for Dynamic Web Content. Mor Naaman, Hector Garcia-Molina, Andreas Paepcke. Department of Computer Science Stanford University. {mor, hector, paepcke}@cs.stanford.edu http://www-db.stanford.edu/. Dynamic Web is Ubiquitous. Problems with Dynamic Pages.

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Evaluation of Delivery Techniques for Dynamic Web Content

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  1. Evaluation of Delivery Techniques for Dynamic Web Content Mor Naaman, Hector Garcia-Molina, Andreas Paepcke Department of Computer Science Stanford University {mor, hector, paepcke}@cs.stanford.edu http://www-db.stanford.edu/

  2. Dynamic Web is Ubiquitous

  3. Problems with Dynamic Pages • Generation of pages is resource-intensive • Pages are too dynamic, or too personalized, to be cached • Higher load on servers (page generation and delivery) • More network traffic

  4. We Evaluate Two Competing Solutions (Both address at least the network load) • ESI(Oracle, Akamai) • Enables assembly of pages from small fragments • Fragments can be cached on specialized network caches (edge servers) • Fragments are assembled on the edge server • Class Based Delta Encoding • Computes delta of generated page from a chosen base file • Base files can be cached on network caches • Client receives delta from the server and base file from cache; applies delta to base file to get final page

  5. A Page Content Model • Page composed from groups; groups include items. • Page construction modeled as two-phase selection (groups, then items) Groups Items

  6. Our Simulation Test-case web pages: Book pages in Amazon-style website MyYahoo-type personalized pages Personalized stock portfolio pages A simple personalized weather page

  7. =Arrival rate; TTL = item time-to-live; = constant Simulation of ESI • Assuming Zipf-like distribution for groups and items (popularityi=k/i) • Performance highly dependant on  (ranging from 0.7-1.5 in our simulations) • Hit rate estimates for items: Sample simulation results (bookstore-type resource, With “backend” servers) Hit-rate vs. value of Zipfian parameter Traffic vs. TTL

  8. Class-Based Delta Encoding Simulation • For some pages, client likely to be able to re-use base files Traffic vs. number of base files • For other pages, client-cache link traffic is higher than before. To minimize client traffic, use same base file owned by client if delta is larger than threshold Traffic vs. Same-Base threshold

  9. Sample Comparison Numbers MyYahoo-type pages Amazon-style Book pages

  10. Conclusions Excellent *, Good +, Bad -, Sometimes ~ All the details: http://dbpubs.stanford.edu/pub/2003-7

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