System Modeling and Simulation in .NET

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System Modeling and Simulation
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Apply various technologies and alternative architectural and design solutions for con guration items (CIs). Assess incremental capability and performance upgrades to system eld con gurations. Evolution of the Test Bed. Test beds evolve in a number of different ways. Test beds may be operated and maintained until the nal deliverable system completes SITE. At that point actual systems serve as the basis for incremental or evolutionary development. Every system is different. So assess the cost bene ts of maintaining the test bed. All or portions of the test bed may be dismantled, depending on the development needs as well as the utility and expense of maintenance. For some large complex systems, it may be impractical to conduct WHAT IF experiments on the ACTUAL systems in enclosed facilities due to: 1. Physical space requirements. 2. Environmental considerations. 3. Geographically dispersed development organizations. In these cases it may be practical to keep a test bed intact. This, in combination with the capabilities of high-speed Internet access, may allow geographically dispersed development organizations to conduct work with a test bed without having to be physically colocated with the actual system.
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51.6 MODELING AND SIMULATION CHALLENGES AND ISSUES
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Although modeling and simulation offer great opportunities for SEs to exploit technology to understand the problem and solution spaces, there are also a number of challenges and issues. Let s explore some of these.
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Challenge 1: Failure to Record Assumptions and Scenarios
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Modeling and simulation requires establishing a base set of assumptions, scenarios, and operating conditions. Reporting modeling and simulation results without recording and noting this information in technical reports and brie ngs diminishes the integrity and credibility of the results.
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Challenge 2: Improper Application of the Model
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Before applying a model to a speci c type of decision support task, the intended application of the model should be veri ed. There may be instances where models do not exist for the application. You may even be confronted with a model that has only a degree of relevance to the application. If this happens, you should take the relevancy into account and apply the results cautiously. The best approach may be to adapt the current model.
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Challenge 3: Poor Understanding of Model De ciencies and Flaws
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Models and simulations generally evolve because an organization has an operational need to satisfy or resolve. Where the need to resolve critical operational or technical issues (COIs/CTIs) is immediate, the investigator may only model a segment of an application or piece of the problem. Other Users with different needs may want to modify the model to satisfy their own segment needs. Before long, the model will evolve through a series of undocumented patches, and then documentation accuracy and con guration control become critical issues.
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51.7 Guiding Principles
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To a potential user, such a model may have risks due to potential de ciencies or shortcomings relative to the User s application. Additionally, undiscovered design aws and errors may exist because parts of the model have not been exercised. Beware of this problem. Thoroughly investigate the model before selecting it for usage. Locate the originator of the model, assuming they can be located or are available. ASK the developers WHAT you should know about the model s performance, de ciencies, and aws that may be untested and undocumented.
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Challenge 4: Model Portability
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Models tend to get passed around, patched, and adapted. As a result, con guration and version control becomes a critical issue. Maintenance and con guration management of models and simulations and their associated documentation is very expensive. Unless an organization has a need to use a model for the long term, the item may go onto a shelf. While the physics and logic of the model may remain constant over time, the execution of the model on newer computer platforms may be questionable. This often necessitates migration of the model to a new computer system at a signi cant cost.
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