Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Difficulties in simulating the internet
IEEE/ACM Transactions on Networking (TON)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
BGP4: Inter-Domain Routing in the Internet
BGP4: Inter-Domain Routing in the Internet
A recursive random search algorithm for large-scale network parameter configuration
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Design and Analysis of Experiments
Design and Analysis of Experiments
A Case Study in Understanding OSPF and BGP Interactions Using Efficient Experiment Design
Proceedings of the 20th Workshop on Principles of Advanced and Distributed Simulation
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Simulation and emulation techniques are fundamental to aid the process of large-scale protocol design and network operations. However, the results from these techniques are often view with a great deal of skepticism from the networking community. Criticisms come in two flavors: (i) the study presents isolated and potentially random feature interactions, and (ii) the parameters used in the study may not be representative of real-world conditions. In this paper, we explore both issues by applying large-scale experiment design and black-box optimization techniques to analyze convergence of network routes in the Open Shortest Path First protocol over a realistic network topology. By using these techniques, we show that: (i) the needed number of simulation experiments can be reduced by an order of magnitude compared to traditional full-factorial experiment design (FFED) approach, (ii) unnecessary parameters can easily be eliminated, and (iii) rapid understanding of key parameter interactions can be achieved.