TED—a language for modeling telecommunication networks
ACM SIGMETRICS Performance Evaluation Review - Special issue on the telecommunications description language
Computing in Science and Engineering
Advances in Network Simulation
Computer
A Generic Framework for Parallelization of Network Simulations
MASCOTS '99 Proceedings of the 7th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
A Critique of the Telecommunications Description Language (TeD)
A Critique of the Telecommunications Description Language (TeD)
BRITE: An Approach to Universal Topology Generation
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Large-scale network simulation techniques: examples of TCP and OSPF models
ACM SIGCOMM Computer Communication Review
A federated approach to distributed network simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Kronecker Graphs: An Approach to Modeling Networks
The Journal of Machine Learning Research
How Low Can You Go? Spherical Routing for Scalable Network Simulations
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
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This paper presents the model splitting method for large-scale interactive network simulation, which addresses the separation of concerns between network researchers, who focus on developing complex network models and conducting large-scale network experiments, and simulator developers, who are concerned with developing efficient simulation engines to achieve the best performance on parallel platforms. Modeling splitting divides the system into an interactive model to support user interaction, and an execution model to facilitate parallel processing. We describe techniques to maintain consistency and real-time synchronization between the two models. We also provide solutions to reduce the memory complexity of large network models and to ensure data persistency and access efficiency for out-of-core processing.