Perfect Sampling of Generalized Jackson Network
Jose Blanchet, Xinyun Chen
TL;DR
This paper introduces the first perfect sampling algorithm for Generalized Jackson Networks with arbitrary topology and non-Markovian input assumptions, under certain stability and moment conditions.
Contribution
It develops a novel perfect sampling method applicable to complex queueing networks with general input distributions and arbitrary topology.
Findings
Successful implementation of perfect sampling for complex networks
Algorithm works under broad non-Markovian assumptions
Provides theoretical guarantees for sampling accuracy
Abstract
We provide the first perfect sampling algorithm for a Generalized Jackson Network of FIFO queues under arbitrary topology and non-Markovian assumptions on the input of the network. We assume (in addition to stability) that the interarrival and service times of customers have finite moment generating function in a neighborhood of the origin, and the interarrival times have unbounded support.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Mathematical Approximation and Integration · Graph theory and applications