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All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

CONFIG - Adapting qualitative modeling and discrete event simulation for design of fault management systems

CONFIG is a modeling and simulation tool prototype for analyzing the normal and faulty qualitative behaviors of engineered systems. Qualitative modeling and discrete-event simulation have been adapted and integrated, to support early development, during system design, of software and procedures for management of failures, especially in diagnostic expert systems. Qualitative component models are defined in terms of normal and faulty modes and processes, which are defined by invocation statements and effect statements with time delays. System models are constructed graphically by using instances of components and relations from object-oriented hierarchical model libraries. Extension and reuse of CONFIG models and analysis capabilities in hybrid rule- and model-based expert fault-management support systems are discussed.

Malin, Jane T.↗
NASA NTRS1987

Parallel discrete event simulation: A shared memory approach

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models.

Reed, Daniel A.↗
NASA NTRS1978

Discrete event simulation and the resultant data storage system response in the operational mission environment of Jupiter-Saturn /Voyager/ spacecraft

The Data Storage Subsystem Simulator (DSSSIM) simulating (by ground software) occurrence of discrete events in the Voyager mission is described. Functional requirements for Data Storage Subsystems (DSS) simulation are discussed, and discrete event simulation/DSSSIM processing is covered. Four types of outputs associated with a typical DSSSIM run are presented, and DSSSIM limitations and constraints are outlined.

Mukhopadhyay, A. K.↗
NASA NTRS1991

Program For Parallel Discrete-Event Simulation

User does not have to add any special logic to aid in synchronization. Time Warp Operating System (TWOS) computer program is special-purpose operating system designed to support parallel discrete-event simulation. Complete implementation of Time Warp mechanism. Supports only simulations and other computations designed for virtual time. Time Warp Simulator (TWSIM) subdirectory contains sequential simulation engine interface-compatible with TWOS. TWOS and TWSIM written in, and support simulations in, C programming language.

Beckman, Brian C.↗
NASA NTRS1996

A Framework for the Optimization of Discrete-Event Simulation Models

With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.

Joshi, B. D.↗
NASA NTRS2011

Performance Analysis of Cloud Computing Architectures Using Discrete Event Simulation

Cloud computing offers the economic benefit of on-demand resource allocation to meet changing enterprise computing needs. However, the flexibility of cloud computing is disadvantaged when compared to traditional hosting in providing predictable application and service performance. Cloud computing relies on resource scheduling in a virtualized network-centric server environment, which makes static performance analysis infeasible. We developed a discrete event simulation model to evaluate the overall effectiveness of organizations in executing their workflow in traditional and cloud computing architectures. The two part model framework characterizes both the demand using a probability distribution for each type of service request as well as enterprise computing resource constraints. Our simulations provide quantitative analysis to design and provision computing architectures that maximize overall mission effectiveness. We share our analysis of key resource constraints in cloud computing architectures and findings on the appropriateness of cloud computing in various applications.

Stocker, John C.↗
NASA NTRS2010

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

Kuhn, D. Richard↗
NASA NTRS2011

Examining Passenger Flow Choke Points at Airports Using Discrete Event Simulation

The movement of passengers through an airport quickly, safely, and efficiently is the main function of the various checkpoints (check-in, security. etc) found in airports. Human error combined with other breakdowns in the complex system of the airport can disrupt passenger flow through the airport leading to lengthy waiting times, missing luggage and missed flights. In this paper we present a model of passenger flow through an airport using discrete event simulation that will provide a closer look into the possible reasons for breakdowns and their implications for passenger flow. The simulation is based on data collected at Norfolk International Airport (ORF). The primary goal of this simulation is to present ways to optimize the work force to keep passenger flow smooth even during peak travel times and for emergency preparedness at ORF in case of adverse events. In this simulation we ran three different scenarios: real world, increased check-in stations, and multiple waiting lines. Increased check-in stations increased waiting time and instantaneous utilization. while the multiple waiting lines decreased both the waiting time and instantaneous utilization. This simulation was able to show how different changes affected the passenger flow through the airport.

Brown, Jeremy R.↗
NASA NTRS1988

Parallel discrete event simulation using shared memory

With traditional event-list techniques, evaluating a detailed discrete-event simulation-model can often require hours or even days of computation time. By eliminating the event list and maintaining only sufficient synchronization to ensure causality, parallel simulation can potentially provide speedups that are linear in the numbers of processors. A set of shared-memory experiments, using the Chandy-Misra distributed-simulation algorithm, to simulate networks of queues is presented. Parameters of the study include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential-simulation of most queueing network models.

Reed, Daniel A.↗
NASA NTRS1990

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons.

Malin, Jane T.↗
NASA NTRS1998

Synchronous Parallel Emulation and Discrete Event Simulation System with Self-Contained Simulation Objects and Active Event Objects

The present invention is embodied in a method of performing object-oriented simulation and a system having inter-connected processor nodes operating in parallel to simulate mutual interactions of a set of discrete simulation objects distributed among the nodes as a sequence of discrete events changing state variables of respective simulation objects so as to generate new event-defining messages addressed to respective ones of the nodes. The object-oriented simulation is performed at each one of the nodes by assigning passive self-contained simulation objects to each one of the nodes, responding to messages received at one node by generating corresponding active event objects having user-defined inherent capabilities and individual time stamps and corresponding to respective events affecting one of the passive self-contained simulation objects of the one node, restricting the respective passive self-contained simulation objects to only providing and receiving information from die respective active event objects, requesting information and changing variables within a passive self-contained simulation object by the active event object, and producing corresponding messages specifying events resulting therefrom by the active event objects.

Steinman, Jeffrey S.↗
NASA NTRS2010

Improving Customer Waiting Time at a DMV Center Using Discrete-Event Simulation

Virginia's Department of Motor Vehicles (DMV) serves a customer base of approximately 5.6 million licensed drivers and ID card holders and 7 million registered vehicle owners. DMV has more daily face-to-face contact with Virginia's citizens than any other state agency [1]. The DMV faces a major difficulty in keeping up with the excessively large customers' arrival rate. The consequences are queues building up, stretching out to the entrance doors (and sometimes even outside) and customers complaining. While the DMV state employees are trying to serve at their fastest pace, the remarkably large queues indicate that there is a serious problem that the DMV faces in its services, which must be dealt with rapidly. Simulation is considered as one of the best tools for evaluating and improving complex systems. In this paper, we use it to model one of the DMV centers located in Norfolk, VA. The simulation model is modeled in Arena 10.0 from Rockwell systems. The data used is collected from experts of the DMV Virginia headquarter located in Richmond. The model created was verified and validated. The intent of this study is to identify key problems causing the delays at the DMV centers and suggest possible solutions to minimize the customers' waiting time. In addition, two tentative hypotheses aiming to improve the model's design are tested and validated.

Arnaout, Georges M.↗
NASA NTRS2007

Simulating Operations at a Spaceport

SPACESIM is a computer program for detailed simulation of operations at a spaceport. SPACESIM is being developed to greatly improve existing spaceports and to aid in designing, building, and operating future spaceports, given that there is a worldwide trend in spaceport operations from very expensive, research- oriented launches to more frequent commercial launches. From an operational perspective, future spaceports are expected to resemble current airports and seaports, for which it is necessary to resolve issues of safety, security, efficient movement of machinery and people, cost effectiveness, timeliness, and maximizing effectiveness in utilization of resources. Simulations can be performed, for example, to (1) simultaneously analyze launches of reusable and expendable rockets and identify bottlenecks arising from competition for limited resources or (2) perform what-if scenario analyses to identify optimal scenarios prior to making large capital investments. SPACESIM includes an object-oriented discrete-event-simulation engine. (Discrete- event simulation has been used to assess processes at modern seaports.) The simulation engine is built upon the Java programming language for maximum portability. Extensible Markup Language (XML) is used for storage of data to enable industry-standard interchange of data with other software. A graphical user interface facilitates creation of scenarios and analysis of data.

Nevins, Michael R.↗
NASA NTRS2002

Predicting System Accidents with Model Analysis During Hybrid Simulation

Standard discrete event simulation is commonly used to identify system bottlenecks and starving and blocking conditions in resources and services. The CONFIG hybrid discrete/continuous simulation tool can simulate such conditions in combination with inputs external to the simulation. This provides a means for evaluating the vulnerability to system accidents of a system's design, operating procedures, and control software. System accidents are brought about by complex unexpected interactions among multiple system failures , faulty or misleading sensor data, and inappropriate responses of human operators or software. The flows of resource and product materials play a central role in the hazardous situations that may arise in fluid transport and processing systems. We describe the capabilities of CONFIG for simulation-time linear circuit analysis of fluid flows in the context of model-based hazard analysis. We focus on how CONFIG simulates the static stresses in systems of flow. Unlike other flow-related properties, static stresses (or static potentials) cannot be represented by a set of state equations. The distribution of static stresses is dependent on the specific history of operations performed on a system. We discuss the use of this type of information in hazard analysis of system designs.

Malin, Jane T.↗
NASA NTRS2024

Discrete Event Simulation-Based Timeline Validation Using R2U2

The Gateway Vehicle Systems Manager (VSM), the top-level software control system in a distributed, hierarchical Autonomous System Management Architecture is, like most modern spacecraft software control systems, heavily data-driven. For example, schedules (timelines) will be developed on the ground and, due to the high degree of autonomy, contain complex procedures involving conditional branching, variable timing, and resource contention resolution. In order to verify that an uploaded timeline will function correctly, it is necessary to explore the feasible set of possible executions. While it is possible to test a timeline using a mission simulation, the complexity of the system and duration of a timeline limits the number of trials and therefore the test coverage. To address this problem, the VSM team is using a discrete event system model that can rapidly generate from a timeline sets of event sequences using Monte Carlo techniques. To achieve rapid and trustworthy checking of the event sequences, we use an offline version of the runtime model checking tool R2U2. This presentation describes the approach the VSM team is using to implement the discrete event simulation and evaluate event sequences using R2U2. The presentation will discuss: 1. Description of the timelines by VSM in the context of VSM operations 2. Expansion of a timeline into a sequence of atomic events 3. Adjustment, in the Monte Carlo environment, of an event sequence to account for uncertainty, external events, and failures 4. Definition of R2U2 input and mission-time linear temporal logic files 5. Generation and use of R2U2 verdict sequences 6. Lessons learned and future work

Verification↗
NASA NTRS1987

Implementing system simulation of C3 systems using autonomous objects

The basis of all conflict recognition in simulation is a common frame of reference. Synchronous discrete-event simulation relies on the fixed points in time as the basic frame of reference. Asynchronous discrete-event simulation relies on fixed-points in the model space as the basic frame of reference. Neither approach provides sufficient support for autonomous objects. The use of a spatial template as a frame of reference is proposed to address these insufficiencies. The concept of a spatial template is defined and an implementation approach offered. Discussed are the uses of this approach to analyze the integration of sensor data associated with Command, Control, and Communication systems.

Rogers, Ralph V.↗
NASA NTRS2010

Model for Simulating a Spiral Software-Development Process

A discrete-event simulation model, and a computer program that implements the model, have been developed as means of analyzing a spiral software-development process. This model can be tailored to specific development environments for use by software project managers in making quantitative cases for deciding among different software-development processes, courses of action, and cost estimates. A spiral process can be contrasted with a waterfall process, which is a traditional process that consists of a sequence of activities that include analysis of requirements, design, coding, testing, and support. A spiral process is an iterative process that can be regarded as a repeating modified waterfall process. Each iteration includes assessment of risk, analysis of requirements, design, coding, testing, delivery, and evaluation. A key difference between a spiral and a waterfall process is that a spiral process can accommodate changes in requirements at each iteration, whereas in a waterfall process, requirements are considered to be fixed from the beginning and, therefore, a waterfall process is not flexible enough for some projects, especially those in which requirements are not known at the beginning or may change during development. For a given project, a spiral process may cost more and take more time than does a waterfall process, but may better satisfy a customer's expectations and needs. Models for simulating various waterfall processes have been developed previously, but until now, there have been no models for simulating spiral processes. The present spiral-process-simulating model and the software that implements it were developed by extending a discrete-event simulation process model of the IEEE 12207 Software Development Process, which was built using commercially available software known as the Process Analysis Tradeoff Tool (PATT). Typical inputs to PATT models include industry-average values of product size (expressed as number of lines of code), productivity (number of lines of code per hour), and number of defects per source line of code. The user provides the number of resources, the overall percent of effort that should be allocated to each process step, and the number of desired staff members for each step. The output of PATT includes the size of the product, a measure of effort, a measure of rework effort, the duration of the entire process, and the numbers of injected, detected, and corrected defects as well as a number of other interesting features. In the development of the present model, steps were added to the IEEE 12207 waterfall process, and this model and its implementing software were made to run repeatedly through the sequence of steps, each repetition representing an iteration in a spiral process. Because the IEEE 12207 model is founded on a waterfall paradigm, it enables direct comparison of spiral and waterfall processes. The model can be used throughout a software-development project to analyze the project as more information becomes available. For instance, data from early iterations can be used as inputs to the model, and the model can be used to estimate the time and cost of carrying the project to completion.

Mizell, Carolyn↗