Dynamic Routing Structure for an RTI Taking an Evolutionary Approach Towards Optimization
Date of Award
Summer 2007
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computational Modeling & Simulation Engineering
Program/Concentration
Modeling and Simulation
Committee Director
John Sokolowski
Committee Member
Andreas Tolk
Committee Member
MinSong
Call Number for Print
Special Collections LD4331.E53 D38 2007
Abstract
The Runtime Infrastructure (RTI) is the common communication framework that High Level Architecture (HLA) simulations incorporate to exchange data. By abstracting the network communication layer from simulation, a common protocol for information exchange is achieved, allowing any RTI- based simulation to exchange data with any other. Such commonality can bring a limitation upon the network infrastructure, enforcing all federates to agree on the same communication policy. While this static network structure is not always the case, an optimal choice would be to adhere to the dynamic properties of a network. Static network configurations deny federates the ability to dynamically avoid network congestion, thus forcing some, if not all, federates to endure suboptimal communication performance. Simulation states can be dynamic, which further allows them to be defined by a set of more general states, based on the network's traffic pattern or signature. By taking advantage of these signatures, the RTI can incorporate a specific routing scheme, per signature, thus improving overall network performance. It is possible for an RTI to anticipate the next simulation state, and effectively predict the proper network routing configuration to achieve optimal network communication. RTI- s (a scalable RTI implementation) has been augmented to interpret the simulation state and dynamically reroute client federates based on optimal paths determined by taking an evolutionary approach to routing.
Rights
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DOI
10.25777/80ch-qw16
Recommended Citation
Davis, Matthew R..
"Dynamic Routing Structure for an RTI Taking an Evolutionary Approach Towards Optimization"
(2007). Master of Science (MS), Thesis, Computational Modeling & Simulation Engineering, Old Dominion University, DOI: 10.25777/80ch-qw16
https://digitalcommons.odu.edu/msve_etds/71