2014/12/08 by Lasse Leskelä, Leskelä, Lasse
Business, Management and Accounting · Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Queuing Theory Analysis #Age of Information Optimization #Algorithm #Applied mathematics #Computer network #Computer science #Coupling (piping) #Engineering #FOS: Mathematics #Flow (mathematics) #Flow network #Markov chain #Markov process #Mathematical optimization #Mathematics #Monotonic function #Probability (math.PR) #Queueing theory #Simulation Techniques and Applications #State (computer science) #Throughput #math.PR
paper · pdf · doi:10.48550/arxiv.1412.2540
published in arXiv (Cornell University) 1(3) (Cornell University) · 12 pages, 1 figure
arxiv created 2014/12/08 · openalex publication_date 2014/12/08 · arxiv updated 2014/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.