Adaptation of the SimSo framework for probabilistic execution times

The original project can be found here: https://github.com/MaximeCheramy/simso/tree/master/simso

Probabilistic execution times

Probabilistic execution times can be simulated now with the ETM's pET and continuousET. The first one is for discrete distributions, the second one for any continuous distributions available in scipy : https://docs.scipy.org/doc/scipy/reference/stats.html#module-scipy.stats

• For discrete distributions, the inputs to pass to the configuration.add_task are modes for the values of execution times and proba for their associated probabilities.
• For continuous distributions, the input is distribution.

The function generator.generate_schedule returns an instance of the schedule. For example, for discrete distributions one can simulate response times with :

from simso.generator.generate_schedule import generate_schedule
execution_times = [([1, 2], [0.5, 0.5]), ([1, 2, 3], [1/3, 1/3, 1/3])]
periods = (4, 6)
schedule = generate_schedule(execution_times=execution_times, periods=periods, etm='pet')
for task in schedule.task_list:
    rt = task.response_times

and for continuous function, for example extreme value distributions:

from simso.generator.generate_schedule import generate_schedule
from scipy.stats import genextreme as gev

distributions = [gev(loc=10, scale=2), gev(loc=20, scale=1)]  
periods = (20, 36)
schedule = generate_schedule(distributions=distributions, periods=periods, etm='continuouset')
for task in schedule.task_list:
    rt = task.response_times

Modification of the execution time model ACET

In its original version, the ACET model generates Gaussian distribution for execution times, but many times it generates negative values, as Gaussian variables is not adapted to model execution times. Now the ACET model generated exponential variables, of mean acet.

This implementation is not stable !