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4. Definition of probability laws

# Definition of probability laws

When setting up your simulation, you must fill in a probability law defining the task execution time.

The different laws proposed in the Iterop simulation tool are :

## Constant Law

This law represents a constant turnaround time. The time it takes to complete the task will always be the same.

For this law, you need to enter only one value.

Example: the task always takes 5 minutes to complete.

## Uniform Law

This law requires two time values min and max. The task will then take a random time between these two values and the probability of a value being drawn is the same regardless of the values.

Example: you define in value min 2 minutes and in value max 20 minutes. During the simulation, the task will take a random value between 2 and 20 minutes.

## Normal Law

This law requires two parameters: average and standard deviation. The standard deviation represents the drilldown of your data.

To simplify the setting, it is good to know that 99% of the times generated by this law will be between [ mean – 3 standard deviations , mean + 3 standard deviations ].

## Triangular Law

This law requires three parameters: min, max and mod. The task will most often take the mod time with min and max extremes.

## Laws specific to appearances

For instance, two other laws are available:

## Dirac’s Law

This law requires two parameters: number of instances and frequency.

It consists in starting simultaneously a number of instances of the process, every x frequency. This law is particularly adapted to simulate situations like “A new command is performed every 25 days“.

## Law by Ramp

This law requires two parameters: initial frequency and final frequency.

Instances will then be created initially every x initial frequency, and this frequency will gradually change until x final frequency is reached.

This is useful for observing how your process reacts to increasing workload, i.e. whether new process instances are launched more and more often.

## Act ramps per instance

This law requires three parameters:

• the frequency
• the initial number of executions
• delta.

This law will bring new instances all frequencies. Initially, it will show “initial number of executions” instances, then will show delta more each time.

For example : you have frequency=5min, initial number = 3 and delta = 5, you will have in your simulation 3 instances at t=0, then 8 ( 3+5 ) instances at t=5min, then 13 ( 3+5+5 ) instances at t=10min .

This law also allows you to analyze the scalability of your process, but this time observing the behavior if you have more and more instances.

## All together

This law will start as many instances as you want to simulate in the first minute of simulation. If you have chosen to run your simulation over a period of time, you must enter the number of instances to be initiated.

Updated on 13 November 2020