Asymptotically optimal importance sampling for Jackson networks with a tree topology
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This note describes an importance sampling (IS) algorithm to estimate buffer overflows of stable Jackson networks with a tree topology. Three new measures of service capacity and traffic in Jackson networks are introduced and the algorithm is defined in their terms. These measures are effective service rate, effective utilization and effective service-to-arrival ratio of a node. They depend on the nonempty/empty states of the queues of the network. For a node with a nonempty queue, the effective service rate equals the node’s nominal service rate. For a node i with an empty queue, it is either a weighted sum of the effective service rates of the nodes receiving traffic directly from node i, or the nominal service rate, whichever smaller. The effective utilization is the ratio of arrival rate to the effective service rate and the effective service-to-arrival ratio is its reciprocal. The rare overflow event of interest 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 shared by all nodes, and (2) each node has its own fixed size buffer. The constructed IS algorithm is asymptotically optimal, i.e., the variance of the associated estimator decays exponentially in the buffer size at the maximum possible rate. This is proved using methods from (Dupuis et al. in Ann. Appl. Probab. 17(4):1306–1346, 2007), which are based on a limit Hamilton–Jacobi–Bellman equation and its boundary conditions and their smooth subsolutions. Numerical examples involving networks with as many as eight nodes are provided.
KeywordsDynamic importance sampling Rare event simulation Queuing networks Jackson networks Overflow probability Large deviations Hamilton–Jacobi–Bellmann equation Optimal control
Mathematics Subject Classification (2000)93E20 60F10 60K25 68M20 90B22 49L20 65C05 68U20
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- 4.Dupuis, P., Wang, H.: Importance sampling, large deviations and differential games. Stoch. Stoch. Rep. 76(6), 481–508 (2004) Google Scholar
- 5.Dupuis, P., Wang, H.: Subsolutions of an Isaacs equation and efficient schemes for importance sampling: Examples and numerics (2005). Preprint available at http://www.dam.brown.edu/lcds/publications
- 7.Dupuis, P., Wang, H.: Subsolutions of an Isaacs equation and efficient schemes for importance sampling: Convergence analysis. (2005). Preprint available at http://www.dam.brown.edu/people/huiwang
- 15.Sezer, A.D.: Dynamic importance sampling for queueing networks, Ph.D. thesis, Brown University Division of Applied Mathematics (2005). Preprint available at http://www.dam.brown.edu/people/sezer
- 16.Sezer, A.D.: Asymptotically optimal importance sampling for Jackson networks with a tree topology (2007). Preprint, available at arXiv:0708.3260
- 18.Wei, L., Qi, H.: An efficient importance sampling method for rare event simulation in large scale tandem networks. In: Proceedings of the 2002 Winter Simulation Conference, pp. 580–587 (2002) Google Scholar