Dynamic information-based scalable hashing on a cluster of web cache servers

Hukeun Kwak, Andrew Sohn, Kyusik Chung

Research output: Contribution to journalArticlepeer-review


Caching web pages is an important part of web infrastructures. Medium to large-scale infrastructures deploy a cluster of servers to solve the scalability and storage problems inherent in caching. In this paper we present dynamic information-based scalable hashing that evenly hashes client requests to a cluster of cache servers, resulting in performance scalability. Runtime information is used to determine when and how to cache pages. Cached pages are stored and retrieved mutually exclusively to/from all the servers to minimize the use of storage, resulting in storage scalability. We set up an experimental environment consisting of various machines, including client servers, a cluster of 16 cache servers, and a load balancer. We demonstrate through experimental results that dynamic information-based scalable hashing maximizes both performance scalability and storage scalability while the existing approaches do only either one of the two.

Original languageEnglish (US)
Pages (from-to)322-340
Number of pages19
JournalConcurrency Computation Practice and Experience
Issue number3
StatePublished - Mar 10 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computer Science Applications
  • Computational Theory and Mathematics


  • cluster utilization
  • scalability
  • storage
  • traffic management
  • web caching


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