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Cycle based simulation and event based simulation dating, cycle based vs Event based Simulators

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Single-threaded simulation engines based on instantaneous events have just one current event. In contrast, multi-threaded simulation engines and simulation engines supporting an interval-based event model may have multiple current events. Formal verification can also be explored as an alternative to simulation, although a formal proof is not always possible.

This is sometimes called the pending event set because it lists events that are pending as a result of previously simulated event but have yet to be simulated themselves. In this example, the system entities are Customer-queue and Tellers. An event is described by the time at which it occurs and a type, indicating the code that will be used to simulate that event. This is accomplished by one or more Pseudorandom number generators. How fast an event simulation runs depends on the number of events to be processed the amount of activity in the model.

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Cycle based vs Event based Simulators

The state trajectory over time S t can be mathematically represented by a step function whose value can change whenever an event occurs. Events are sorted by the time when they will occur, and when all events for a particular time have been handled, the simulated time is advanced to the time of the next scheduled event. This use of the term bootstrapping can be contrasted with its use in both statistics and computing. The use of pseudo-random numbers as opposed to true random numbers is a benefit should a simulation need a rerun with exactly the same behavior. This problem is typically solved by bootstrapping the simulation model.

Optimized implementations may take advantage of low model activity to speed up simulation by skipping evaluation of gates whose inputs didn't change. When events are instantaneous, activities that extend over time are modeled as sequences of events. It is common for the event code to be parametrized, in which case, the event description also contains parameters to the event code. Some simulation frameworks allow the time of an event to be specified as an interval, giving the start time and the end time of each event. These events, however, schedule additional events, and with time, the distribution of event times approaches its steady state.

Discrete event simulation

This is called bootstrapping the simulation model. One of the problems with the random number distributions used in discrete-event simulation is that the steady-state distributions of event times may not be known in advance. The system events are Customer-Arrival and Customer-Departure.

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In cycle simulation, it is not possible to specify delays. Cycle simulation therefore runs at a constant speed, regardless of activity in the model. In these cases, since event simulation only simulates necessary events, anastasia brow duo asian dating performance may no longer be a disadvantage over cycle simulation. An agent-based framework for performance modeling of an optimistic parallel discrete event simulator is another example for a discrete event simulation.

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As a result, the initial set of events placed into the pending event set will not have arrival times representative of the steady-state distribution. In the bank example, it is of interest to track the mean waiting times.

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