Network Simulation Modeling Analysis Reserach Tools (N-SMART)
Telecommunication's Crucial Role
All critical infrastructures depend upon the core of Telecommunications, Energy, and Transportation for their successful operations. Understanding these critical interdependencies and the consequences of a communications disruption for each infrastructure is a high priority. In close technical collaboration, Lucent Technologies/Bell Laboratories and NISAC developed simulation models for the analysis of telecommunications networks. Telecommunication is vital to all industries and the economic health of the nation.
N-SMART Tools
These research tools are comprised of discrete event and/or flow-based models of telecommunications traffic across networks. N-SMART provides a first-of-its kind network-wide model that includes trunks and switch processor resources, network management controls, and customer behaviour in a single model with extensions to include mobile networks to show the interaction between wireline and wireless networks.
Components of the suite include:
- N-SMART-Voice - a geographically based point-to-point and call-by-call simulation for metropolitan voice networks.
- N-SMART-Data - a geographically based point-to-point and packet-by-packet simulation of packet data networks.
- N-SMART National Long Distance - geographically based point-to-point and flow-based aggregate call model for a national long distance network.
- N-SMART Analytic - provides the mathematical foundations for aggregation to higher level models,
- Operations Model - a quantitative analysis of the management environment supporting telecommunications networks
Another innovative element of N-SMART is a framework created to focus on the detailed behaviour of the individual users and the resulting effect on end-to-end network performance.
Simulation, Analysis and Insights
Using sophisticated algorithms and new techniques, N-SMART simulation results have contributed to a number of NISAC cross-infrastructure analytic efforts, revealing non-intuitive outcomes that could not otherwise have been predicted.
As an example, simulations of a one-week outage in the Port of Seattle's communications infrastructure predicted a container back-up in the port lasting 4 months, creating significant impacts to port operations and the local economy. The results from this analysis are being used to identify survivable network architectures, and improve business continuity plans for critical national infrastructures.
Insight: Communications networks in different metropolitan areas have different levels of robustness during failures and overloads. This result could not have been discovered with standard performance modeling or reliability tools that assume random failures of individual nodes due to software or hardware failures.
Insight: Overloads in one network would cause overloads in other networks; implies new engineering rules to increase the robustness of overall networks.
