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Optimal Prefetching in Random Trees

Author

Listed:
  • Kausthub Keshava

    (Deloitte India (Offices of the US), Hyderabad 500032, Telangana, India
    The work of this author was performed partly as a Master’s student at IISER Mohali, India, and as an intern at Inria.)

  • Alain Jean-Marie

    (Inria, University of Montpellier, 34095 Montpellier, France)

  • Sara Alouf

    (Inria, Université Côte d’Azur, 06902 Sophia Antipolis, France)

Abstract

We propose and analyze a model for optimizing the prefetching of documents, in the situation where the connection between documents is discovered progressively. A random surfer moves along the edges of a random tree representing possible sequences of documents, which is known to a controller only up to depth d . A quantity k of documents can be prefetched between two movements. The question is to determine which nodes of the known tree should be prefetched so as to minimize the probability of the surfer moving to a node not prefetched. We analyzed the model with the tools of Markov decision process theory. We formally identified the optimal policy in several situations, and we identified it numerically in others.

Suggested Citation

  • Kausthub Keshava & Alain Jean-Marie & Sara Alouf, 2021. "Optimal Prefetching in Random Trees," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2437-:d:648134
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