The National Infrastructure Simulations and Analysis Center (NISAC) has developed a nationwide model of the Internet to study the potential impact of the loss of physical facilities on the network and on other infrastructures that depend on the Internet for services. The model looks at the Internet from the perspective of Internet Service Providers (ISPs) and their connectivity and can be used to determine how the network connectivity could be modified to assist in mitigating an event. In addition the model could be used to explore how portions of the network could be made more resilient to disruptive events.
This report gives introductory guidance on the level of effort required to create a data warehouse for mining data. Numerous tutorials have been provided to demonstrate the process of downloading raw data, processing the raw data, and importing the data into a PostgreSQL database. Additional information and tutorial has been provided on setting up a Hadoop cluster for storing vasts amounts of data. This report has been generated as a deliverable for a New Mexico Small Business Assistance (NMSBA) project.
Research into modeling of the quantification and prioritization of resources used in the recovery of lifeline critical infrastructure following disruptive incidents, such as hurricanes and earthquakes, has shown several factors to be important. Among these are population density and infrastructure density, event effects on infrastructure, and existence of an emergency response plan. The social sciences literature has a long history of correlating the population density and infrastructure density at a national scale, at a country-to-country level, mainly focused on transportation networks. This effort examines whether these correlations can be repeated at smaller geographic scales, for a variety of infrastructure types, so as to be able to use population data as a proxy for infrastructure data where infrastructure data is either incomplete or insufficiently granular. Using the best data available, this effort shows that strong correlations between infrastructure density for multiple types of infrastructure (e.g. miles of roads, hospital beds, miles of electric power transmission lines, and number of petroleum terminals) and population density do exist at known geographic boundaries (e.g. counties, service area boundaries) with exceptions that are explainable within the social sciences literature. The correlations identified provide a useful basis for ongoing research into the larger resource utilization problem.
Enterprise level logistics and prognostics and health management (PHM) modeling efforts use reliability focused failure distributions to characterize the probability of failure over the lifetime of a component. This research characterized the Sandia National Laboratories developed combined lifecycle (CMBL) distribution and explored methods for updating this distribution as systems age and new failure data becomes available. The initial results obtained in applying a Bayesian sequential updating methodology to the CMBL distribution shows promise. This research also resulted in the development of a closed-form full life cycle (CFLC) distribution similar to the CMBL distribution but with slightly different, yet commonly recognized, input parameters. Further research is warranted to provide additional theoretical validation of the distributions, complete the updating methods for the CMBL distribution, evaluate a Bayesian updating methodology for the CFLC distribution, and determine which updating methods would be most appropriate for enterprise level logistics and PHM modeling.
Analyzing the performance of a complex System of Systems (SoS) requires a systems engineering approach. Many such SoS exist in the Military domain. Examples include the Army's next generation Future Combat Systems 'Unit of Action' or the Navy's Aircraft Carrier Battle Group. In the case of a Unit of Action, a system of combat vehicles, support vehicles and equipment are organized in an efficient configuration that minimizes logistics footprint while still maintaining the required performance characteristics (e.g., operational availability). In this context, systems engineering means developing a global model of the entire SoS and all component systems and interrelationships. This global model supports analyses that result in an understanding of the interdependencies and emergent behaviors of the SoS. Sandia National Laboratories will present a robust toolset that includes methodologies for developing a SoS model, defining state models and simulating a system of state models over time. This toolset is currently used to perform logistics supportability and performance assessments of the set of Future Combat Systems (FCS) for the U.S. Army's Program Manager Unit of Action.
This report documents the results of an LDRD program entitled 'System of Systems Modeling and Analysis' that was conducted during FY 2003 and FY 2004. Systems that themselves consist of multiple systems (referred to here as System of Systems or SoS) introduce a level of complexity to systems performance analysis and optimization that is not readily addressable by existing capabilities. The objective of the 'System of Systems Modeling and Analysis' project was to develop an integrated modeling and simulation environment that addresses the complex SoS modeling and analysis needs. The approach to meeting this objective involved two key efforts. First, a static analysis approach, called state modeling, has been developed that is useful for analyzing the average performance of systems over defined use conditions. The state modeling capability supports analysis and optimization of multiple systems and multiple performance measures or measures of effectiveness. The second effort involves time simulation which represents every system in the simulation using an encapsulated state model (State Model Object or SMO). The time simulation can analyze any number of systems including cross-platform dependencies and a detailed treatment of the logistics required to support the systems in a defined mission.