Page 246 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling

Notes Experimental Results

To analyze the capabilities of the proposed framework, we used two illustrative applications: a
manipulator arm controller and a /_converter.
To evaluate the performances of simulation models generated in CODIS, we measured the
overhead given by the simulation interfaces. The overhead caused by the Simulink integration
step adjustment when detecting a SystemC event has been measured in a maximum of 10% of
total simulation time. The overhead caused by IPC (Inter Process Communication) used for the
context switch and the communication layers has been measured in order of maximum 20% of
the total simulation time.

Notes The cost of the added synchronization functionality in the case of SystemC is
negligible and does not exceed 0.02% of the total simulation time.
13.1.2 Discrete System Simulation

Discrete Event Simulation (DES) concerns the modelling of a system as it evolves over time by
representing the changes as separate events. This is the opposite of Continuous Simulation
where the system evolves as a continuous function (differential).
In discrete-event simulation, the operation of a system is represented as a chronological sequence
of events. Each event occurs at an instant in time and marks a change of state in the system. For
example, if an elevator is simulated, an event could be “level 6 button pressed”, with the
resulting system state of “lift moving” and eventually (unless one chooses to simulate the
failure of the lift) “lift at level 6”.
A common exercise in learning how to build discrete-event simulations is to model a queue,
such as customers arriving at a bank to be served by a teller. In this example, the system entities
are CUSTOMER-QUEUE and TELLERS. The system events are CUSTOMER-ARRIVAL and
CUSTOMER-DEPARTURE. (The event of TELLER-BEGINS-SERVICE can be part of the logic of
the arrival and departure events.) The system states, which are changed by these events, are
NUMBER-OF-CUSTOMERS-IN-THE-QUEUE (an integer from 0 to n) and TELLER-STATUS (busy
or idle). The random variables that need to be characterized to model this system stochastically
are CUSTOMER-INTERARRIVAL-TIME and TELLER-SERVICE-TIME.
A number of mechanisms have been proposed for carrying out discrete-event simulation; among
them are the event-based, activity-based, process-based and three-phase approaches (Pidd, 1998).
The three-phase approach is used by a number of commercial simulation software packages, but
from the user’s point of view, the specifics of the underlying simulation method are generally
hidden.

Task Analyze in discrete-event simulation, the operation of a system is represented as a
chronological sequence of events?

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