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- Simulation in Computer Network Design and Modeling: Use and Analysis
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*Flows are here discretely modelled, i. All orders are placed anonymously. Perform data modelling using different discrete event simulation software's, and use optimization techniques, research aptitude.*

Computer simulation is the process of mathematical modelling , performed on a computer , which is designed to predict the behaviour of or the outcome of a real-world or physical system.

Since they allow to check the reliability of chosen mathematical models, computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics computational physics , astrophysics , climatology , chemistry , biology and manufacturing , as well as human systems in economics , psychology , social science , health care and engineering.

Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions. Computer simulations are realized by running computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers.

The scale of events being simulated by computer simulations has far exceeded anything possible or perhaps even imaginable using traditional paper-and-pencil mathematical modeling. In , a desert-battle simulation of one force invading another involved the modeling of 66, tanks, trucks and other vehicles on simulated terrain around Kuwait , using multiple supercomputers in the DoD High Performance Computer Modernization Program.

Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification. A computer model is the algorithms and equations used to capture the behavior of the system being modeled. By contrast, computer simulation is the actual running of the program that contains these equations or algorithms. Simulation, therefore, is the process of running a model.

Thus one would not "build a simulation"; instead, one would "build a model", and then either "run the model" or equivalently "run a simulation". Computer simulation developed hand-in-hand with the rapid growth of the computer, following its first large-scale deployment during the Manhattan Project in World War II to model the process of nuclear detonation.

It was a simulation of 12 hard spheres using a Monte Carlo algorithm. Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible. There are many types of computer simulations; their common feature is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states of the model would be prohibitive or impossible.

The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers for example, simulation of a waveform of AC electricity on a wire , while others might require terabytes of information such as weather and climate models. Because of this variety, and because diverse simulation systems have many common elements, there are a large number of specialized simulation languages.

The best-known may be Simula sometimes called Simula, after the year when it was proposed. There are now many others. Systems that accept data from external sources must be very careful in knowing what they are receiving.

While it is easy for computers to read in values from text or binary files, what is much harder is knowing what the accuracy compared to measurement resolution and precision of the values are.

Often they are expressed as "error bars", a minimum and maximum deviation from the value range within which the true value is expected to lie. Because digital computer mathematics is not perfect, rounding and truncation errors multiply this error, so it is useful to perform an "error analysis" [8] to confirm that values output by the simulation will still be usefully accurate.

Computer models can be classified according to several independent pairs of attributes, including:. Another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two main classes:. Equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted.

Formerly, the output data from a computer simulation was sometimes presented in a table or a matrix showing how data were affected by numerous changes in the simulation parameters.

The use of the matrix format was related to traditional use of the matrix concept in mathematical models. However, psychologists and others noted that humans could quickly perceive trends by looking at graphs or even moving-images or motion-pictures generated from the data, as displayed by computer-generated-imagery CGI animation.

Although observers could not necessarily read out numbers or quote math formulas, from observing a moving weather chart they might be able to predict events and "see that rain was headed their way" much faster than by scanning tables of rain-cloud coordinates.

Such intense graphical displays, which transcended the world of numbers and formulae, sometimes also led to output that lacked a coordinate grid or omitted timestamps, as if straying too far from numeric data displays. Similarly, CGI computer simulations of CAT scans can simulate how a tumor might shrink or change during an extended period of medical treatment, presenting the passage of time as a spinning view of the visible human head, as the tumor changes.

Other applications of CGI computer simulations are being developed to graphically display large amounts of data, in motion, as changes occur during a simulation run. Generic examples of types of computer simulations in science, which are derived from an underlying mathematical description:.

In social sciences, computer simulation is an integral component of the five angles of analysis fostered by the data percolation methodology, [12] which also includes qualitative and quantitative methods, reviews of the literature including scholarly , and interviews with experts, and which forms an extension of data triangulation.

Of course, similar to any other scientific method, replication is an important part of computational modeling [13]. The reliability and the trust people put in computer simulations depends on the validity of the simulation model , therefore verification and validation are of crucial importance in the development of computer simulations.

Another important aspect of computer simulations is that of reproducibility of the results, meaning that a simulation model should not provide a different answer for each execution. Although this might seem obvious, this is a special point of attention in stochastic simulations , where random numbers should actually be semi-random numbers. An exception to reproducibility are human-in-the-loop simulations such as flight simulations and computer games.

Here a human is part of the simulation and thus influences the outcome in a way that is hard, if not impossible, to reproduce exactly. Vehicle manufacturers make use of computer simulation to test safety features in new designs. By building a copy of the car in a physics simulation environment, they can save the hundreds of thousands of dollars that would otherwise be required to build and test a unique prototype. Engineers can step through the simulation milliseconds at a time to determine the exact stresses being put upon each section of the prototype.

Computer graphics can be used to display the results of a computer simulation. Animations can be used to experience a simulation in real-time, e. In some cases animations may also be useful in faster than real-time or even slower than real-time modes.

For example, faster than real-time animations can be useful in visualizing the buildup of queues in the simulation of humans evacuating a building. Furthermore, simulation results are often aggregated into static images using various ways of scientific visualization.

In debugging, simulating a program execution under test rather than executing natively can detect far more errors than the hardware itself can detect and, at the same time, log useful debugging information such as instruction trace, memory alterations and instruction counts. This technique can also detect buffer overflow and similar "hard to detect" errors as well as produce performance information and tuning data. Although sometimes ignored in computer simulations, it is very important to perform a sensitivity analysis to ensure that the accuracy of the results is properly understood.

For example, the probabilistic risk analysis of factors determining the success of an oilfield exploration program involves combining samples from a variety of statistical distributions using the Monte Carlo method. If, for instance, one of the key parameters e. The following three steps should be used to produce accurate simulation models: calibration, verification, and validation.

Computer simulations are good at portraying and comparing theoretical scenarios, but in order to accurately model actual case studies they have to match what is actually happening today. A base model should be created and calibrated so that it matches the area being studied.

The calibrated model should then be verified to ensure that the model is operating as expected based on the inputs. Once the model has been verified, the final step is to validate the model by comparing the outputs to historical data from the study area. This can be done by using statistical techniques and ensuring an adequate R-squared value. Unless these techniques are employed, the simulation model created will produce inaccurate results and not be a useful prediction tool.

Model calibration is achieved by adjusting any available parameters in order to adjust how the model operates and simulates the process. For example, in traffic simulation, typical parameters include look-ahead distance, car-following sensitivity, discharge headway, and start-up lost time. These parameters influence driver behavior such as when and how long it takes a driver to change lanes, how much distance a driver leaves between his car and the car in front of it, and how quickly a driver starts to accelerate through an intersection.

Adjusting these parameters has a direct effect on the amount of traffic volume that can traverse through the modeled roadway network by making the drivers more or less aggressive. These are examples of calibration parameters that can be fine-tuned to match characteristics observed in the field at the study location. Most traffic models have typical default values but they may need to be adjusted to better match the driver behavior at the specific location being studied.

Model verification is achieved by obtaining output data from the model and comparing them to what is expected from the input data. For example, in traffic simulation, traffic volume can be verified to ensure that actual volume throughput in the model is reasonably close to traffic volumes input into the model. Ten percent is a typical threshold used in traffic simulation to determine if output volumes are reasonably close to input volumes.

Simulation models handle model inputs in different ways so traffic that enters the network, for example, may or may not reach its desired destination. Additionally, traffic that wants to enter the network may not be able to, if congestion exists. This is why model verification is a very important part of the modeling process. The final step is to validate the model by comparing the results with what is expected based on historical data from the study area.

Ideally, the model should produce similar results to what has happened historically. This is typically verified by nothing more than quoting the R-squared statistic from the fit. This statistic measures the fraction of variability that is accounted for by the model.

A high R-squared value does not necessarily mean the model fits the data well. Another tool used to validate models is graphical residual analysis. If model output values drastically differ from historical values, it probably means there is an error in the model. Before using the model as a base to produce additional models, it is important to verify it for different scenarios to ensure that each one is accurate. If the outputs do not reasonably match historic values during the validation process, the model should be reviewed and updated to produce results more in line with expectations.

It is an iterative process that helps to produce more realistic models. Validating traffic simulation models requires comparing traffic estimated by the model to observed traffic on the roadway and transit systems. Initial comparisons are for trip interchanges between quadrants, sectors, or other large areas of interest. The next step is to compare traffic estimated by the models to traffic counts, including transit ridership, crossing contrived barriers in the study area.

These are typically called screenlines, cutlines, and cordon lines and may be imaginary or actual physical barriers. Cordon lines surround particular areas such as a city's central business district or other major activity centers. Transit ridership estimates are commonly validated by comparing them to actual patronage crossing cordon lines around the central business district.

Three sources of error can cause weak correlation during calibration: input error, model error, and parameter error. In general, input error and parameter error can be adjusted easily by the user. Model error however is caused by the methodology used in the model and may not be as easy to fix. Simulation models are typically built using several different modeling theories that can produce conflicting results.

Some models are more generalized while others are more detailed. If model error occurs as a result, in may be necessary to adjust the model methodology to make results more consistent.

Computer simulation is the process of mathematical modelling , performed on a computer , which is designed to predict the behaviour of or the outcome of a real-world or physical system. Since they allow to check the reliability of chosen mathematical models, computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics computational physics , astrophysics , climatology , chemistry , biology and manufacturing , as well as human systems in economics , psychology , social science , health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions. Computer simulations are realized by running computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers.

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Networking Project Report Pdf. Report PM2. For public services it has the potential to target support more effectively to those in need, or to tailor services to users. Media will be utilized to raise public awareness and focussed information packages will be produced to meet the objectives of this project. Taking basic steps to secure your home network will help protect your devices — and your information — from compromise. Plan your networking - know what you want - manage it. A report serves project managers and all those involved in a project in prioritising and organising all that has been done in a project up-to-date.

This site features information about discrete event system modeling and simulation. It includes discussions on descriptive simulation modeling, programming commands, techniques for sensitivity estimation, optimization and goal-seeking by simulation, and what-if analysis. Advancements in computing power, availability of PC-based modeling and simulation, and efficient computational methodology are allowing leading-edge of prescriptive simulation modeling such as optimization to pursue investigations in systems analysis, design, and control processes that were previously beyond reach of the modelers and decision makers. Enter a word or phrase in the dialogue box, e.

Computer networks have become essential to the survival of businesses, organizations, and educational institutions, as the number of network users, services, and applications has increased alongside advancements in information technology. Given this, efforts have been put forward by researchers, designers, managers, analysts, and professionals to optimize network performance and satisfy the varied groups that have an interest in network design and implementation. Simulation in Computer Network Design and Modeling: Use and Analysis reviews methodologies in computer network simulation and modeling, illustrates the benefits of simulation in computer networks design, modeling, and analysis, and identifies the main issues that face efficient and effective computer network simulation.

It is found that temperature has acute effects on product yield, especially hydrogen. In teaching, my objective is to prompt critical thinking in hopes of eliciting some innovative adjudication-model concepts that, in their application, might create actual, human improvement: a more humane justice system. This study aims at benchmarking current and past gasifier systems in order to create a com-prehensive database for computer simulation purposes.

A couple of examples showing how to find the theoretical probability. What percentage of your simulated classes had at least two people with the same birthday? How does this compare to your response to Q1? To address concerns about coverage bias related to the imputed degree cases being identified as ineligible i. The Monte Carlo Simulation is a technique that generates large volumes of probable performance outcomes based on the probability distribution of the schedule and cost of individual activities.

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