Simulation concepts and architectures

1. Simulation concepts and architectures

2. Simulation Basics • System: a collecting of entities that act and interact together toward the accomplishment of some logical end. • State: the collection of variables necessary to describe a system at a particular time, relative to the objectives of a study • Model: a description about how a system behaves, that consist of a collection of assumptions.

3. System Experiment with the actual system Experiment with a model of the system Physical model Mathematical model Analytical solution Simulation Figure 1. Ways to study a system

4. Experiment with the actual system vs. Experiment with a Model of the system • Costly, disruptively. • “System” could be only a conceptual matter. • Validity : the accuracy of the model. • Physical model vs. Mathematical model • Cockpits, miniatures, etc. are iconic. Sometimes useful in operations research like scale models. • Analytical solution vs. Simulation • v = d/t • Large nonsparse matrix inversion, manufacturing system, the Matrix

5. Simulation models • Static vs. Dynamic simulation models • Whether time plays an role in the model • Deterministic vs. stochastic simulation models • Whether random elements are taken into account • One of the main disadvantage of simulation • Continuous vs. discrete simulation models • Whether state changes happen instantaneously in countable time points.

6. Time advance mechanisms for discrete-event models • Next-event time advance ….while ( !done) { System->next_Event(); Simulation_clock->update(); }….. • Fixed-increment time advance ….while (time <= end_time) { System->update_states(); time++; }….

7. Distributed simulation • Why distributed simulation? • Ever-complex system and simulation models require more computing power • Advance in hardware and software technologies • Lower cost of hardware • Network computing capabilities

8. Users don’t know where the resources are located • Resources can be moved without changing their names • Users don’t know the number of existing instances of the object • Many users can share the resources automatically • Some processes may execute in parallel on the network. • Distributed systems: the middle tier contains computers without shared memories. Acts as one single machine to users. • Dialogue between computers is make possible by a unique inter-process communication mechanism

9. Distributed simulation architecture Basic elements: • An object interface language : describe interface. • Distributed applications require more abstract level of communications than ordinary ones. • Provide interoperability between distributed objects • An object manager : • Passing references to requesting clients • Instantiating objects and marshalling(coordinating) object requests between different machines. • Objects therefore become indifferent to invokers. • A naming service • The mechanism by which a server informs clients about objects available for access. • Making the communications between objects can be delayed until runtime.

10. High level architecture

11. Common Object Request Broker Architecture

12. J2EE’s RMI Remote Method Invocation/Internet Inter-ORB Protocol (RMI/IIOP)—Protocol that enables Remote Method Invocation (RMI) • Programmers to combine the benefits of using the RMI APIs and robust • CORBA IIOP communications protocol to communicate with • CORBA-compliant clients that have been developed using any language • compliant with CORBA.

13. Comparison • Both CORBA and HLA are concerned with legacy applications, possibly in different languages. C++, Ada, Java, etc. • RMI keeps to single language, now has common interface with CORBA • HLA is more simulation specific, whereas CORBA and RMI are designed for general applications. • HLA’ s notion of transfer of object ownership is unique . • RMI uses TCP/IP, CORBA uses IIOP, HLA leaves the choice to RTI developers/vendors (which is considered a disadvantage to interoperability). • RMI has unique security Manager.