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Two-node fluid network with a heavy-tailed random input: the strong stability case

Published online by Cambridge University Press:  30 March 2016

Sergey Foss
Affiliation:
Heriot Watt University and Novosibirsk State University, Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, UK. Email address: s.foss@hw.ac.uk.
Masakiyo Miyazawa
Affiliation:
Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan. Email address: miyazawa@rs.tus.ac.jp.
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Abstract

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We consider a two-node fluid network with batch arrivals of random size having a heavy-tailed distribution. We are interested in the tail asymptotics for the stationary distribution of a two-dimensional workload process. Tail asymptotics have been well studied for two-dimensional reflecting processes where jumps have either a bounded or an unbounded light-tailed distribution. However, the presence of heavy tails totally changes these asymptotics. Here we focus on the case of strong stability where both nodes release fluid at sufficiently high speeds to minimise their mutual influence. We show that, as in the one-dimensional case, big jumps provide the main cause for workloads to become large, but now they can have multidimensional features. We first find the weak tail asymptotics of an arbitrary directional marginal of the stationary distribution at Poisson arrival epochs. In this analysis, decomposition formulae for the stationary distribution play a key role. Then we employ sample-path arguments to find the exact tail asymptotics of a directional marginal at renewal arrival epochs assuming one-dimensional batch arrivals.

Type
Part 6. Heavy tails
Copyright
Copyright © Applied Probability Trust 2014 

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