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Modellering > Inför tenta > Flashcards

Inför tenta Flashcards

1
Q

What is performance evaluation?

A

Performance evaluation is the process of evaluating the performance of computer systems and networks under various conditions

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2
Q

What are the parameters assessed in performance evaluation?

A

The parameters assessed in performance evaluation include response time, throughput, resource utilization, and reliability

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3
Q

What are some approaches to performance evaluation mentioned on this page?

A

Some approaches to performance evaluation mentioned on this page include queuing models, analytic models, and simulation

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4
Q

What are some advantages of simulation?

A

Some advantages of simulation include better graphic quality, increased safety and efficiency, avoidance of danger and loss of life, the ability to study behavior more closely, and the ability to emulate components for video games and post-processing effects.

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5
Q

What is ANOVA?

A

ANOVA stands for Analysis of Variance, which is a statistical method for comparing means between two or more groups.

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6
Q

What is the procedure for ANOVA?

A

The procedure for ANOVA involves calculating the sum of squares, degrees of freedom, mean square, the F-value, and the p-value, which are used to determine if the means between groups are statistically significant

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7
Q

What are the assumptions for ANOVA

A

The assumptions for ANOVA mentioned on this page include the normality of sample distributions, homogeneity of variances, independence of observations, and random sampling

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8
Q

What is the difference between discrete and continuous states?

A

Discrete states refer to states that can only take on a limited number of values, while continuous states can take on any value within a continuous range

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9
Q

What are some common mistakes in simulation discussed

A

Some common mistakes in simulation discussed on this page include inappropriate level of detail, improper language, unverified models, invalid models, and improperly handled initial conditions.

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10
Q

What is the importance of validating simulation models?

A

Validating simulation models is important to ensure that the model accurately represents the system being studied and that the results are credible and applicable to the real system.

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11
Q

What is the impact of initial conditions on simulations?

A

The impact of initial conditions on simulations can be significant, as the initial trajectory is often not representative of steady-state behavior and including it can lead to inaccurate results

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12
Q

What is an incomplete mix of essential skills in a simulation team?

A

An incomplete mix of essential skills in a simulation team means that the team does not have all of the necessary skills to develop and implement the simulation effectively

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13
Q

What are some items in the simulation checklist

A

Some items in the simulation checklist mentioned on this page include checking if the goal is properly specified, ensuring the detail in the model is appropriate for the goal, verifying and validating the model, and having the right mix of individuals in the simulation team

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14
Q

What is the difference between verification and validation in simulation?

A

Verification in simulation involves ensuring the correctness of the model implementation, while validation involves testing whether the model represents the real system being studied

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15
Q

What does the distribution of measurements refer to?

A

What does the distribution of measurements refer to?

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16
Q

What is simulation modeling?

A

Simulation modeling is a methodology used to develop computer models that imitate the behavior of a real-world system and can be used to predict how it will behave under different conditions

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17
Q

What are some common types of variables in simulation modeling?

A

Some common types of variables in simulation modeling include input variables, output variables, internal variables, decision variables, and random variables

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18
Q

What is the purpose of validation in simulation modeling?

A

What is the purpose of validation in simulation modeling?

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19
Q

What is the difference between a model and a simulation

A

A model represents a simplified version of a real-world system, while a simulation is the execution of a model to estimate the behavior of the real-world system.

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20
Q

What are some common types of simulation models?

A

Some common types of simulation models include deterministic, stochastic, continuous, discrete, time-driven, and event-driven models

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21
Q

What is the importance of sensitivity analysis in simulation modeling?

A

Sensitivity analysis is important in simulation modeling as it can identify how changes in input variables affect the output of the model and can help to determine which inputs should be optimized or adjusted to achieve the desired results

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22
Q

What is the difference between a static and dynamic model?

A

A static model represents a system as it exists at a single point in time, while a dynamic model represents how a system changes over time as a result of its interactions with other systems and its environment

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23
Q

What are some common types of dynamic models?

A

Some common types of dynamic models include stock and flow models, system dynamics models, microsimulation models, agent-based models, and discrete-event models

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24
Q

What is the importance of calibration in dynamic modeling?

A

Calibration is important in dynamic modeling as it adjusts the model parameters to match the behavior of the real-world system being modeled

25
Q

What is the difference between discrete-event and continuous simulation?

A

Discrete-event simulation models systems in which events occur at discrete points in time, while continuous simulation models systems in which events occur continuously over a range of time.

26
Q

What is the role of a clock in simulation?

A

The clock in a simulation is used to keep track of time, to schedule events in the future, and to control the pacing of the simulation.

27
Q

What are some common types of events in discrete-event simulation?

A

Some common types of events in discrete-event simulation include arrivals, departures, failures, completions, and observations

28
Q

What are some common inputs to a simulation model?

A

Some common inputs to a simulation model include the arrival rates of customers, service times, processing rates, queue lengths, machine downtimes, repair times, and demand forecasts

29
Q

What are some common outputs from a simulation model?

A

Some common outputs from a simulation model include queue lengths, cycle times, lead times, processing times, resource utilization, inventory levels, and customer satisfaction

30
Q

What is the importance of random number generation in simulation?

A

Random number generation is important in simulation as it can introduce randomness into the model and allow the model to simulate stochastic processes and random events.

31
Q

What is the purpose of statistical analysis in simulation modeling?

A

The purpose of statistical analysis in simulation modeling is to analyze the output of the model and determine if the results are statistically significant or not

32
Q

What are some common statistical analysis methods used in simulation?

A

Some common statistical analysis methods used in simulation include hypothesis testing, confidence intervals, regression analysis, variance analysis, ANOVA, and regression trees

33
Q

What is the importance of statistically valid samples in simulation modeling?

A

Statistically valid samples are important in simulation modeling as they ensure that the analysis of the model output is representative of the population being modeled.

34
Q

What is the importance of replication in simulation modeling?

A

Replication in simulation modeling involves running the model multiple times with different random seeds to obtain a more accurate estimate of the model output. It is important in reducing the variance of the model output and improving the reliability of the analysis

35
Q

What are some common types of replication in simulation modeling?

A

Some common types of replication in simulation modeling include exact replication, repeated independent replication, partially correlated replication, and common random numbers

36
Q

What is the difference between variance reduction and antithetic variates in simulation modeling?

A

Variance reduction and antithetic variates are both techniques used to reduce the variance of the model output. Variance reduction involves selectively sampling input variables to improve the accuracy of the output, while antithetic variates involves generating pairs of random samples that are negatively correlated to reduce the variance of the output.

37
Q

What is the importance of experimentation in simulation modeling?

A

Experimentation in simulation modeling involves designing and conducting virtual experiments to evaluate the impact of different conditions on the system being modeled. It is important in improving the understanding of the system and identifying areas for improvement

38
Q

What are some common techniques used in experimentation in simulation modeling?

A

Some common techniques used in experimentation in simulation modeling include factorial design, response surface methods, optimization, genetic algorithms, and simulation-based optimization.

39
Q

What is the difference between discrete optimization and continuous optimization in simulation modeling

A

Discrete optimization involves identifying the best combination of discrete input variables to achieve the desired output, while continuous optimization involves identifying the best value of a continuous input variable to achieve the desired output

40
Q

What are the typical/needed components for discrete-event simulations

A

The typical/needed components for discrete-event simulations include an event scheduler, a simulation clock, and simulation entities

41
Q

What are some consistency tests

A

Some consistency tests discussed on this page include continuity tests, degeneracy tests, and seed independence tests.

42
Q

What is seed independence

A

Seed independence refers to the property that the random number generator starting value should not affect the final conclusions of the simulation model

43
Q

What are some main approaches to validate assumptions

A

Some main approaches to validate assumptions mentioned on this page include expert intuition, real system measurements, and theoretical results

44
Q

What is the importance of model validation in simulation?

A

Model validation is important in simulation to ensure that the models are realistic and that the conclusions drawn from the model output are valid and reliable

45
Q

What are some common model validation techniques

A

Some common model validation techniques include sensitivity analysis, data analysis, hypothesis testing, goodness-of-fit tests, and cross-validation

46
Q

What is a performance metric?

A

A performance metric is used to describe, measure, and extrapolate information from data. It includes counts of event occurrences, time duration, parameter sizes, and values derived from fundamental measurements

47
Q

What are the characteristics of a good metric?

A

A good metric allows accurate and detailed comparisons, leads to correct conclusions, is well understood by everyone, has a quantitative basis, and helps avoid erroneous conclusions.

48
Q

How is the SPECmark calculated?

A

SPECmark is calculated as the geometric mean of normalized values, where execution times are measured and normalized to a standard basis machine

49
Q

What is congestion collapse in complex systems?

A

Congestion collapse occurs when the offered load increases while work done decreases. It can happen due to factors like increased cost per job, impatience of jobs, or job rejection before completion

50
Q

How can congestion collapse be avoided in systems?

A

Congestion collapse can be avoided with TCP congestion control or admission control in web servers

51
Q

What are some common performance metrics?

A

Common performance metrics include response time (request to response elapsed time), throughput (jobs or operations per unit time), bandwidth (bits per second), jitter (variation in time delay), and more

52
Q

What is the purpose of a QQPlot in statistics?

A

A QQPlot is used in statistics to compare the distribution of a dataset to a theoretical distribution or another dataset. It helps assess whether data come from a known or assumed distribution, such as a normal distribution.

53
Q

What is the primary difference between congestion collapse and latent congestion collapse?

A

Congestion collapse occurs when the offered load increases while work done decreases, whereas latent congestion collapse is when a bottleneck initially prevents it, but adding resources reveals the congestion collapse

54
Q

How does standard deviation differ from variance in statistics?

A

Variance quantifies the spread or dispersion from the mean in a dataset, while standard deviation measures the dispersion relative to the mean and is the square root of the variance

55
Q

In performance metrics, what distinguishes ad hoc metrics from common metrics like response time and throughput?

A

Ad hoc metrics are specifically defined for particular needs or contexts, while common metrics are general measurements applicable across various scenarios

56
Q

What characteristics make a good performance metric, and how do they compare to poor metrics?

A

Good metrics allow accurate comparisons, lead to correct conclusions, are well understood, and have a quantitative basis. In contrast, poor metrics may lack these qualities and can lead to erroneous conclusions

57
Q

How do histograms and cumulative density function plots differ in representing data spread?

A

Histograms present data in equally sized “buckets” to visualize spread, while cumulative density function plots show the cumulative distribution of data and help understand how data is spread out over a range

58
Q

What role does the System Performance Evaluation Coop (SPEC) play in evaluating systems, and how does it standardize measurements?

A

SPEC is a non-profit consortium that establishes standardized benchmarks and tools for system evaluation. It measures execution times and normalizes them to a standard basis machine, allowing the calculation of SPECmark as the geometric mean of normalized values

Modellering (1 decks)

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