Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
Introduction to queueing networks
Introduction to queueing networks
The QNET method for two-moment analysis of open queueing networks
Queueing Systems: Theory and Applications
Decomposability, instabilities, and saturation in multiprogramming systems
Communications of the ACM
Fluid models and solutions for large-scale IP networks
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Simulation of Ultra-large Communication Networks
MASCOTS '99 Proceedings of the 7th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Simulation of large scale networks I: simulation of large-scale networks using SSF
Proceedings of the 35th conference on Winter simulation: driving innovation
Simulation of large scale networks II: large-scale network simulations with GTNetS
Proceedings of the 35th conference on Winter simulation: driving innovation
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
Advanced concepts in large-scale network simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Reduction of closed queueing networks for efficient simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Reduction of closed queueing networks for efficient simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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This article gives several methods for approximating a closed queueing network with a smaller one. The objective is to reduce the simulation time of the network. We consider Jackson-like networks with Markovian routing and with general service distributions. The basic idea is to first divide the network into two parts—the core nodes of interest and the remaining nodes. We suppose that only metrics at the core nodes are of interest. The remaining nodes are collapsed into a reduced set of nodes, in an effort to approximate the flows into and out of the set of core nodes. The core nodes and their interactions are preserved in the reduced network. We test the network reductions for accuracy and speed. By randomly generating sample networks, we test the reductions on a large variety of test networks, rather than on a few specific cases. The main conclusion is that the reductions work well when the squared coefficients of variation of the service distributions are not all small (that is, the network is not close to being deterministic) and for nodes where the utilization is not too high or too low.