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Area of Science:

  • Computer Science
  • Distributed Systems
  • Information Retrieval

Background:

  • Maintaining local caches of frequently updated web pages is crucial for systems like web crawlers and proxy servers.
  • Limited resources necessitate efficient polling and fetching strategies to ensure cached data remains current.
  • Previous optimization models for this problem were computationally intractable for larger scales.

Purpose of the Study:

  • To develop an efficient and scalable policy for managing local caches of dynamic web pages.
  • To find an optimal randomized polling and fetching strategy that maximizes the utility of up-to-date served pages.
  • To provide a computationally tractable solution for the web page caching problem.

Main Methods:

  • Formulated the problem as an optimization task.
  • Derived an exact solution for the optimal randomized policy with a time complexity of O(n).
  • Developed a deterministic schedule based on optimal probabilities, computable in linear time.

Main Results:

  • The optimal randomized policy for polling and fetching updates can be found exactly in O(n) operations.
  • A deterministic schedule derived from optimal probabilities achieves 99% of the optimal utility in experimental settings.
  • The proposed policy offers a tractable and highly effective solution for dynamic web page caching.

Conclusions:

  • An efficient and exact method for determining optimal web page caching policies has been established.
  • The developed deterministic schedule provides a practical and near-optimal solution for real-world applications.
  • This research significantly advances the state-of-the-art in managing dynamic web content efficiently.