Analysis of stochastic fluid queues driven by local time processes

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

32 pages, 6 figures

Scientific paper

We consider a stochastic fluid queue served by a constant rate server and driven by a process which is the local time of a certain Markov process. Such a stochastic system can be used as a model in a priority service system, especially when the time scales involved are fast. The input (local time) in our model is always singular with respect to the Lebesgue measure which in many applications is ``close'' to reality. We first discuss how to rigorously construct the (necessarily) unique stationary version of the system under some natural stability conditions. We then consider the distribution of performance steady-state characteristics, namely, the buffer content, the idle period and the busy period. These derivations are much based on the fact that the inverse of the local time of a Markov process is a L\'evy process (a subordinator) hence making the theory of L\'evy processes applicable. Another important ingredient in our approach is the Palm calculus coming from the point process point of view.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Analysis of stochastic fluid queues driven by local time processes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Analysis of stochastic fluid queues driven by local time processes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Analysis of stochastic fluid queues driven by local time processes will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-656636

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.