The potential costs and benefits of long-term prefetching for content distribution

  • Authors:

  • Arun Venkataramani;Praveen Yalagandula;Ravindranath Kokku;Sadia Sharif;Mike Dahlin

  • Affiliations:

  • Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USA;Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USA;Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USA;Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USA;Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USA

  • Venue:
  • Computer Communications
  • Year:
  • 2002

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Abstract

This paper examines the costs and potential benefits of long-term prefetching for content distribution. In traditional short-term prefetching, caches use recent access history to predict and prefetch objects likely to be referenced in the near future. In contrast, long-term prefetching uses long-term steady state object access rates and update frequencies to identify objects to replicate to content distribution locations. Compared to demand caching, long-term prefetching increases network bandwidth and disk space costs but may benefit a system by improving hit rates. Using analytic models and trace-based simulations, we examine algorithms for selecting objects for long-term prefetching. We find that although the Zipf-like popularity distribution of web objects makes it challenging to prefetch enough objects to significantly improve hit rates, systems can achieve significant benefits at modest costs by focusing on long-lived objects.