Random numbers are the nuts and bolts of simulation. Typically, all the randomness required by the model is simulated by a random number generator whose output is assumed to be a sequence of independent and identically distributed (IID) U(0, 1) random variables (i.e., continuous random variables distributed uniformly over the interval

Thispapersummarizesthecurrentstate-of-the-art onuniformrandomnumbergenerationforstochasticsimulation. Itrecallsthebasicideas,discusses somelinearmethodsandtheirtheoreticalanalysis, andprovidespointerstofurtherdetailsandtorecommendedimplementations. 1 WHATISAGOODRNG? Withoutagoodrandomnumbergenerator(RNG), simulationresultsareoftenmeaningless.Andquestionablegeneratorsarestillallovertheplace, somany experimentsrestonshakyfoundations.Whythis problemwasnotsolvedlongago?Becauseitisnot soeasy.Aso-calledRNGactuallyproducesatotally deterministicandperiodicsequenceofnumbers,once itsinitialstate(orseed)ischosen.Thisisintotal contradictionwiththeassumptionofasequenceofindependentandidenticallydistributed (i.i.d.)random variables,andthereisnocleanwaytocompletely reconcilethesetwooppositeaspects.Therefore,everythingwedointhiscontextisheuristic. Thisbeingsaid, theheuristicargumentsleadtocriteriathat needtheorytobeanalyzed.

this paper, we present and analyze HAVEGE (HArdware Volatile Entropy Gathering and Expansion), a new user-level software heuristic to generate practically strong random numbers on general-purpose computers. The hardware clock cycle counter of the processor can be used to gather part of the entropy/uncertainty introduced by operating system interrupts in the internal states of the processor. Then, we show how this entropy gathering technique can be combined with pseudo-random number generation in HAVEGE. Since the internal state of HAVEGE includes thousands of internal volatile hardware states, it seems impossible even for the user itself to reproduce the generated sequences