We consider the problem of polling web pages as a strategy for monitoring the world wide web. The problem consists of repeatedly polling a selection of web pages so that changes that occur over time are detected. In particular, we consider the case where we are constrained to poll a maximum number of web pages per unit of time. Thus, the issue at stake is one of determining which web pages are to be polled, and we attempt to do it in a manner that maximizes the number of changes detected. We solve the problem by first modelling it as a Stochastic Non-linear Fractional Knapsack Problem. We then present a completely new on-line Learning Automata (LA) system, namely, the Hierarchy of Twofold Resource Allocation Automata (H-TRAA), whose primitive component is a Twofold Resource Allocation Automaton (TRAA). Both the TRAA and the H-TRAA have been proven to be asymptotically optimal. Finally, we demonstrate empirically that H-TRAA provides orders of magnitude faster convergence compared to the LAKG which represents the state-of-the-art. Further, in contrast to the LAKG, H-TRAA scales sub-linearly. Based on these results, we believe that the H-TRAA has a tremendous potential to handle demanding real-world applications, particularly those which deal with the world wide web.

Additional Metadata
Keywords Learning Automata, Stochastic Optimization, Web Polling
Persistent URL dx.doi.org/10.1007/978-3-540-69052-8_37
Series Lecture Notes in Computer Science
Granmo, O.-C. (Ole-Christoffer), & Oommen, J. (2008). A hierarchy of twofold resource allocation automata supporting optimal web polling. In Lecture Notes in Computer Science. doi:10.1007/978-3-540-69052-8_37