Center for Network-Based Systems (CNBS)

A Partnership for Innovation

The Center for Network-Based Systems (CNBS) works to solve practical performance, economic, and resilience challenges faced by planned and implemented complex systems that employ networks as a central architectural component. The Center is a collaborative initiative between Noblis and George Mason University (GMU).

Five Areas of Research

The CNBS is involved in five areas of research: rare events simulation, network congestion, network resiliency, economics of network congestion, and heavy tails distribution modeling.

Rare Events Simulation

Our research in rare events simulation is to develop new approaches for simulating rare events and to efficiently determine via simulation the best choice among a number of alternatives, where the simulation of each alternative required the evaluation of a rare-event probability.

We are developing a new rare-event simulation methodology for decision making. The key idea is to integrate the notions of optimal budget allocation and splitting to optimally allocate the limited computing budget so that the overall efficiency is maximized. For simulation analysis of a single system, we are determining the optimal numbers of simulation runs among a number of splitting levels so that the variance of the rare-event estimator is minimized. This rare-event simulation method will enable decision makers to efficiently analyze the impact of disruption and quickly identify the best mitigation decision.

Network Congestion

Our efforts in network congestion research are focusing on finding solutions to packet loss, Quality of Service (QoS), latency, and jitter problems introduced when Internet Protocol (IP) networks become overloaded.

Industry is moving towards IP technology for all its telecommunications applications. When IP networks become overloaded, packets get dropped and other QoS measures like packet latency and jitter are significantly degraded. At some point, the quality of service for voice and video packets becomes poor enough that their communication is lost.

Our research focuses developing modeling and simulation capabilities to determine the performance of voice, video, data, and proposed emergency traffic streams under the low latency queueing (LLQ) discipline.

Network Resiliency

We are investigating network resiliency to determine how much widespread damage a network can absorb before significant mission effectiveness is lost.

One of the shortcomings of risk analysis as applied to complex, network-oriented infrastructures is determining the consequences of the loss of individual facilities. In a poorly designed network, the loss of one or two key facilities can cause massive loss of mission effectiveness. There is currently no methodology to measure the resilience of such networks, be they telecommunications networks, electricity grids, oil or gas pipelines, roads, etc.  Without such a measure, it is difficult for industry to justify investments in infrastructure resilience without knowing the resulting benefit.

Economics of Network Congestion

Our investigations surrounding the economics of network congestion are focused on assessing likely congestion conditions and toll revenue projections when travel reliability is established as the foundation for financial transactions between transportation system user and transportation system operator.

The implementation of congestion-based tolling on urban surface transportation systems (combined auto, bus and rail modes) represents an opportunity to address the root causes of the interlinked congestion-mobility-productivity problem. Research and analysis at the CNBS is focused on assessing likely congestion conditions and toll revenue projections when travel reliability is established as the foundation for financial transactions between the transportation system user and transportation system operator. This approach provides a money-back guarantee of reliable travel to transportation users in exchange for user fees collected through ubiquitous, system-wide congestion pricing on all high-capacity facilities. Center researchers conduct large-scale simulation and modeling of integrated surface transportation systems to estimate congestion impacts and projected revenue streams to compare alternative pricing and refund strategies.

Heavy Tails Distributions Modeling

Our research in heavy tails distributions modeling investigates the feasibility of various approaches to modeling queues with heavy-tailed interarrival and service distributions.

We are researching the feasibility of approaches to modeling queues with heavy-tailed interarrival and service distributions. This research involves developing procedures for modeling queues with heavy-tailed distributions for interarrival and/or service times. These types of probability distributions decay much more slowly than exponential. Distributions of this type make queueing analyses very difficult, in that the Laplace-Stieltjes transforms (LSTs) of interarrival and/or service times, which play such a crucial role in analytical queueing theory, often do not have closed form. The approaches avoid the problems and pitfalls of finding approximating distributions by using the actual heavy-tailed distributions themselves.


View CNBS Publications 

Center Researchers
  • Chun-Hung Chen received his Ph.D. from Harvard University and is currently a professor of Systems Engineering & Operations Research at George Mason University. Dr. Chen has lead research projects in stochastic simulation and optimization, systems design under uncertainty, and air traffic management, which are sponsored by NSF, FAA, and NASA. Dr. Chen received the Kayamori Best Automation Paper Award from the 2003 IEEE International Conference on Robotics and Automation, 1994 Eliahu I. Jury Award from Harvard University, and the 1992 MasPar Parallel Computer Challenge Award. He is serving on the editorial boards of IEEE Transactions on Automatic Control, IIE Transactions, Journal of Simulation Modeling Practice and Theory, and International Journal of Simulation and Process Modeling. 

  • David A. Garbin is a senior fellow at Noblis where his interests include telecommunications technology, networking, network design and optimization, economic analysis, voice communications, and data communications. He received an MSEE from the Massachusetts Institute of Technology.

  • Andrew M. Girard is a fellow at Noblis where his experience includes network design and optimization problems within telecommunications. His research interests include optimization problems in telecommunications and finance, applications of geometry and mechanics to biological and mechanical systems as well as control theory. He received a doctorate in applied mathematics from the University of Maryland

  • Karla Hoffman is a professor in the Systems Engineering and Operations Research Department of the School of Information Technology and Engineering of George Mason University. Dr. Hoffman’s primary area of research is combinatorial optimization. She consults to the FCC on auction design and testing for package-bidding auctions and is responsible for the design of a real-time scheduling algorithm for the concrete industry. She has developed scheduling algorithms for the airline industry, consults to the military on a variety of routing and scheduling problems and has advised the telecommunications industry on capital budgeting. Her research focuses on the development of new algorithms for solving large modeling problems arising in industry. Resume: 

  • Denise M. Bevilacqua Masi is a fellow engineer at Noblis. Her research interests include queueing theory and simulation applied to telecommunications networks. She received a doctorate in information technology and engineering from George Mason University.

  • John F. Shortle, Ph.D., is an associate professor of Systems Engineering at George Mason University. His experience includes developing stochastic, queueing, and simulation models to optimize networks and operations. His research interests include simulation and queueing applications in telecommunications and air transportation. He received his doctorate degree in operations research from UC Berkeley.

  • James R. Soltys is a senior manager at Noblis where his experience includes pattern classification, neural network design, and telecommunications systems design. His research interests include genetic algorithms, neural networks, and mathematical programming as applied to pattern classification and telecommunications network design. He received his doctorate degree in systems engineering from the University of Virginia.

  • Karl E. Wunderlich is a fellow at Noblis where his experience includes simulation analysis, dynamic programming/shortest path techniques, and operations research. He received a doctorate in industrial and operations engineering from the University of Michigan.

Noblis Experts

Denise Masi

Denise is an expert in operations research, simulation, and telecommunications networks.
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Karl Wunderlich

Karl is an expert in transportation analysis, traffic simulation, traveler information, and travel reliability.
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