Optimal Prefetching in Random Trees - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Mathematics Year : 2021

Optimal Prefetching in Random Trees

Kausthub Keshava
  • Function : Author
  • PersonId : 1112013


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.
Fichier principal
Vignette du fichier
mathematics-09-02437-v4.pdf (428.31 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03361953 , version 1 (01-10-2021)
hal-03361953 , version 2 (15-10-2021)





Kausthub Keshava, Alain Jean-Marie, Sara Alouf. Optimal Prefetching in Random Trees. Mathematics , 2021, 9 (19), pp.2437. ⟨10.3390/math9192437⟩. ⟨hal-03361953v2⟩
66 View
75 Download



Gmail Facebook X LinkedIn More