Flow coupling and stochastic ordering of throughputs in linear networks
Journal Title: EAI Endorsed Transactions on Internet of Things - Year 2015, Vol 1, Issue 3
Abstract
Robust estimates for the performance of complicated queueing networks can be obtained by showing that the number of jobs in the network is stochastically comparable to a simpler, analytically tractable reference network. Classical coupling results on stochastic ordering of network populations require strong monotonicity assumptions which are often violated in practice. However, in most real-world applications we care more about what goes through a network than what sits inside it. This paper describes a new approach for ordering flows instead of populations by augmenting network states with their associated flow counting processes and deriving Markov couplings of the augmented state-flow processes.
Authors and Affiliations
Lasse Leskelä
Keywords
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- EP ID EP46463
- DOI http://dx.doi.org/10.4108/icst.valuetools.2014.258210
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