Asymptotically Optimal Importance Sampling for Jackson Networks with a Tree Topology

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

20 pages, 2 figures

Scientific paper

Importance sampling (IS) is a variance reduction method for simulating rare events. A recent paper by Dupuis, Wang and Sezer (Ann. App. Probab. 17(4):1306- 1346, 2007) exploits connections between IS and stochastic games and optimal control problems to show how to design and analyze simple and efficient IS algorithms for various overflow events for tandem Jackson networks. The present paper uses the same approach to build asymptotically optimal IS schemes for stable open Jackson networks with a tree topology. Customers arrive at the single root of the tree. The rare overflow event we consider is the following: given that initially the network is empty, the system experiences a buffer overflow before returning to the empty state. Two types of buffer structures are considered: 1) A single system-wide buffer of size $n$ shared by all nodes, 2) each node $i$ has its own buffer of size $\beta_i n$, $\beta_i \in (0,1)$.

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

Asymptotically Optimal Importance Sampling for Jackson Networks with a Tree Topology 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 Asymptotically Optimal Importance Sampling for Jackson Networks with a Tree Topology, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Asymptotically Optimal Importance Sampling for Jackson Networks with a Tree Topology will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-394829

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