I am doing a scheduling simulation in python which is full determinstic. So, when I have the same input and parameters I always get the same output.
Now I want to randomize the initial starting state of the simulation and compare the output of two (or more) different simulation parameters. To compare the "same randomized initial starting state" I want to set the random.seed()
with an initial value, which should stay the same for all comparisions of different schedulers. Furthermore I want to see the behaviour for one scheduler on different initial states so I have to change the random.seed()
. This I have to do of course for all schedulers.
Now my question is, what impact has the seed on the "randomness" of the random generator? For example does it matter if I choose as a seed 1 or 100? And because I want to use different seeds for the same scheduler and compare it to the other ones, can I simply use e.g. the seeds 1 to 10 or must my seeds be "more random"?
For clarification, I use the random generator for distributing tasks initial on different cores and compare the output to "my optimal (deterministic) initial distribution". I want to get a wide-spread of different distributions with my choosen seeds.
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