Discrete Event Simulation - Ch. 1

1. Discrete Event Simulation - Ch. 1 Instructor: Giampiero Pecelli e-mail: giam@cs.uml.edu Office Phone: 978 - 934 -3639 Office: Olsen 225 Office Hours: Before Class and by Appointment

2. Discrete Event Simulation - 1 Why Discrete Event Simulation? How Discrete Event Simulation? What Discrete Event Simulation?

3. Discrete Event Simulation - 1 • WHY? We need to conduct experiments "on some reality" and the reality - although pre-existing - is not available for our experiments. Examples: a) a busy network of computers that cannot be taken over just for the experiment; b) a busy superhighway system on which we want to "change the rules of traffic"; c) a chemical plant whose production cannot be stopped so that "we can tinker with it"; etc..

4. Discrete Event Simulation - 1 • What characteristic do these example share? They simply have to do with our lack of access to an existing artifact: the simulation allows us to construct a useful model of the artifact, that we can then use as though it were the inaccessible artifact. The goal is to determine whether a planned change to the USE of the artifact can be implemented while producing the desired results and no undesired ones. A more specific example would be the introduction of the use of a "group productivity package", like Lotus Notes or a Configuration and Version Manager for a software producing organization. In both cases the traffic patterns - and bottlenecks - in a LAN might not be predictable without extensive testing, and any meaningful REAL testing will result in many lost productivity hours for the whole group or organization that is adopting the package.

5. Discrete Event Simulation - 1 • A second set of examples. These have to do with the absence of an appropriate artifact. Here are some examples: a) An automobile frame that must meet certain stiffness and crushability criteria, while also meeting geometry, materials, production method and weight constraints; b) An algorithm to manage certain types of (not yet available?) traffic in networks with as yet non-existent (but likely, or already possible) properties (e.g., 20 TH bandwidth); c) The design of drugs with special properties;

6. Discrete Event Simulation - 1 • What characteristic do these example share? There is NO artifact on which to perform experiments, and the construction of any such artifact is not feasible (too expensive - current technology is too immature - too dangerous) without knowledge that the finished artifact will behave (with high probability) as desired. There MAY exist earlier versions of similar artifacts, with different characteristics, that MIGHT be used as guides for the design of a simulation, but with no guarantee that the results of the simulation can be compared to "real" data in the regions of interest.

7. Discrete Event Simulation - 1 • Simulation (Shannon): The process of designing a computerized model of a system (or process) and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies for the operation of the system. System: an orderly collection of logically related principles, facts or objects. Process: a method of doing something involving multiple steps and operations.

8. Discrete Event Simulation - 1 • Some terminology. A) System Environment: the collection of external factors capable of causing a change in the system. B) State of a System: the minimal collection of information with which the future behavior of a system can be reliably (uniquely?) predicted. C) Activity: any events that causes a State Change. D) Endogenous Activity: one occurring inside the system. E) Exogenous Activity: one occurring outside the system.

9. Discrete Event Simulation - 1 F) Continuous System: one in which the quantities of interest are represented by continuous variables (e.g., distances between cars on a highway). G) Discrete System: one in which the quantities of interest are represented by integer-valued variables (e.g., number of cars on a highway). F) Hybrid System: one in which both integer and continuos variables appear (e. g., number of and distances between cars) and are of interest.

10. Discrete Event Simulation - 1 G) Deterministic System: one in which the next state is uniquely determined by the current state. Examples: Classical Mechanics; anything that can be adequately modeled via Newtonian Mechanics: hit the brakes of your car under exactly controlled conditions and the distance it takes for you to come to a stop can be exactly predicted. Deterministic Automata. H) Stochastic System: one in which the next state is only probabilistically determined by the current state - there are multiple possible next states that can occur subsequent to the same activity, each with a given probability. Examples: Quantum Mechanical phenomena. Non-deterministic automata.

11. Discrete Event Simulation - 1 The Stages of a Simulation Project. • Planning a) Problem Formulation: what is it and what do I want to do with it? b) Resource Estimation: time, people and money. c) System and Data Analysis • Modeling a) Model Building: find relationships. b) Data Acquisition: find and collect appropriate data. c) Model Translation: program and debug.

12. Discrete Event Simulation - 1 • Verification/Validation a) Verification: does the PROGRAM execute as intended? b) Validation: does the PROGRAM represent reality as intended? • Application a) Experimentation: run it! b) Analysis: how do I analyze and interpret the results? c) Implementation/Documentation: how do I implement the decisions resulting from the simulation, and how do I document the model and its use?

13. Discrete Event Simulation - 1 • Performance Measures. What is it that we are measuring? What (statistical) properties of the "measured" are we interested in? For example: maximum, minimum, totals, mean, variance, higher moments, specific frequency distribution, interarrival times, service times, lengths of queues, loss rates, error rates, etc.

14. Discrete Event Simulation - 1 Advantages of Simulation. a) it permits controlled experimentation. you KNOW what parameters are being changed. b) it permits time compression. e.g., weather forecasting... c) it permits sensitivity analysis (change input vars) d) it does not disturb the real system. which may not even exist, anyway. e) it is an effective training tool. you are not likely to crash a flight simulator, or a big chunk of the Internet.

15. Discrete Event Simulation - 1 My own interest: How do you experiment in a meaningful way with algorithms whose theoretical properties can be predicted? Most of these algorithms attempt to provide management for traffic which is, as yet, not well understood, in networks with characteristics that don't yet exist. Qualitative and quantitative mathematical predictions can be obtained only under considerably simplified assumptions on the system being studied. How well will these predictions compare with reality? Can simulation provide a reasonable answer? Many engineers construct an algorithm that will exhibit SOME desired behaviors, run a few simulations, call it quits and send a paper out. Is this a prescription for nonsense? Or worse?