Simulation - 01 Introduction
Computer Science, Informatik 4Communication and Distributed SystemsSimulationModelingg and Performance Analysisywith Discrete-Event SimulationDr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsChapter 1Introduction to Simulation
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction to Simulation Given a system, how do you evaluate its performance?SystemHow to evaluate?ExperimentsAnalysisSimulationDevelop a mathematicalDevelop a computer programUse existing instance of theabstraction of the system andwhich implements a model ofsystem to performderive formulas whichthe system. Performperformance measurements.describe the systemexperiments by running theperformance.computer program.Chapter 1. Introduction to Simulation3Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction to Simulation How to studyy a system?y Measurements on an existing system- What to do, if system does not exist in reality?- What to do, if changes are very expensive or time consuming? Mathematical analysis- Good solutions, but only feasible for simple systems.- Real world systems are too complex, e.g., factory, computer, network etc.Other course from Informatik 4Modeling and Evaluation of Communication Systems SimulationB ild thh i off a systemtithi a program- Buildthe bbehaviorwithin The content of this course is described in the subtitle Modeling and Performance Analysis of by means of Discrete-EventSimulationChapter 1. Introduction to Simulation4Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction to Simulation There are many open questions What is a system?What is a model?What is performance and how to measure it?On what does performance depend?Ho to bHowbuildild a model?How to numerically evaluate it?How to interpret such results?Chapter 1. Introduction to Simulation5Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction to SimulationSystemExperiment with the actual systemExperiment with a model of the systemPhysical ModelMathematical ModelAnalytical ModelChapter 1. Introduction to Simulation6SimulationDr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction to Simulation Simulation is used to imitate the realworldWooden mechanical horse simulatorduring WW1 It is not as new as we think ;-) According to Elmaghraby [1968] Aid to tionPredictingA soldier in a heavy-wheeled-vehicledriver simulator Entertainment (this is a new application)- Video gamesChapter 1. Introduction to Simulation7Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 1 A storehouse with n loadingg berthsThere are several 100 trucks daily to serveLoading time of a truck is 50 minutesGoalnTruckkTruckkTruckk Usually 2 customer types1Truckk Cost-effective loading and short waiting timeStorehouse Type 1: Full load with only one product Type 2: Load consisting of several products ProposalsTrucckTrucck ProblemTrucck Fast loading berth for Type 1 customers Special berth for Type 2 customers Cannot experiment, changes are expensive!Park SlotsChapter 1. Introduction to Simulation8Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 2 Experimentp Sliding of a leader on the wall A leader is at the wall WeW ddraw ththe bbottomttoff ththeleader and the top of theleader is leant on the wall andslides downdown.Top Question: Which shapep drawsthe center of the leader? Concave ConvexChapter 1. Introduction to Simulation9Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 2 Variant: The leader falls down from the wall The resulting shape is convex.TopExperiment 1: Leader falls down from the wallChapter 1. Introduction to Simulation10Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 2 One intuitively thinks the driven shape will be concave. However, the resulting shape is also convex. Astonished?TopExperiment 2: Leader slides down on the wallChapter 1. Introduction to Simulation11Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 3 Clients requestqsome service from a server over a network. Client User and web browserService web pageSServer webb serverNetwork local network,Internet, wireless networkClient 1 AnalysisNetwork(Internet) Performance of the server Performance of the networkServerClient k Attention In this examples theserver as well asthe network isdepicted very simple!Chapter 1. Introduction to Simulation12Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 4 Mobile multi-hopp ad-hoc network(MANET) Wireless network consisting ofmobile nodes No infrastructure, i.e. no AccessPoints or Base Stations Two nodes can communicate ifthere are in communicationrange Typically, the source anddestination nodes of aconnection are several hopsaway Thus,Thus all nodes have to relaydata for othersChapter 1. Introduction to Simulation13 Mobile node Communication range Source node Relay node Destination nodeDr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 4 For the analysisyof a MANET amobility model is needed Assumption Movement area: Rectangle withoutobstacles Simple model: Random-Waypointmobility model A node selects uniformly a pointon the simulation area p (x, y) Velocity v [vmin, vmax] Pause time tpause The node moves to the point pwith velocity v Stays for tpause time units on p andrestarts movementChapter 1. Introduction to Simulation14Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction – Example 41000 What's about the pprobabilityy that anode is on point p (x,y) on themovement area?800 Uniformlyy distributed?600 Are some areas preferred?400- Since x and y are uniformly selected. What's about the influence of theparameters?2000200400600800 Velocity Pause time AlthoAlthoughgh simple to describedescribe,mathematically it is hard to get aclosed form formulae.2E-61,5E-61E-65E-700Chapter 1. Introduction to Simulation15200400y60080010000200400x6008001000Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsIntroduction to Simulation What is a simulation? A simulation is the imitation of the operation of a real-worldsystem over time. What is the method? GGeneratet an artificialtifi i l hihistorytoff a systemt Draw inferences from the artificial history concerning thecharacteristics of the systemy How it is done? Develop a model Model consists of entities (objects)Chapter 1. Introduction to Simulation16Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsWhen simulation is appropriate Simulation can be used for the followinggppurposes:p Simulation enables the study of experiments with internalinteractions Informational,Informational organizationalorganizational, and environmental changes can besimulated to see the model’s behavior Knowledge from simulations can be used to improve the system ObservingObi resultslt ffrom simulationi l ti can givei insighti i ht tto whichhi hvariables are the most important ones Simulation can be used as pedagogical device to reinforce thelearning material Simulations can be used to verify analytical results, e.g.queueing systems Animation of a simulation can show the system in action, so thatthe plan can be visualizedChapter 1. Introduction to Simulation17Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsWhen simulation is not appropriate Simulation should not be used, in the case when problem is solvable by common sensewhen the problem can be solved mathematicallywhen direct experiments are easierwhen the simulation costs exceed the savingswhenhen the simsimulationlation reqrequiresires timetime, whichhich is not aavailableailablewhen no (input) data is available, but simulations need datawhen the simulation cannot be verified or validatedwhen the system behavior is too complex or unknown Example: human behavior is extremely complex to modelChapter 1. Introduction to Simulation18Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsAdvantages and disadvantages of simulation Advantagesg of simulation Policies, procedures, decision rules, information flows can be exploredwithout disrupting the real system New hardware designs, physical layouts, transportation systems cantested without committing resources Hypotheses about how or why a phenomena occur can be tested forfeasibility Timee cacan be cocompressedp essed oor eexpandedpa ded- Slow-down or Speed-up Insight can be obtained about the interaction of variables Insight can be obtained about the importance of variables to theperformancefoff ththe systemt Bottleneck analysis can be performed to detect excessive delays Simulation can help to understand how the system operates rather thanhow people think the system operates “What if” questions can be answeredChapter 1. Introduction to Simulation19Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsAdvantages and disadvantages of simulation Disadvantagesg of simulation Model building requires training, it is like an art.- Compare model building with programming. Simulation results can be difficult to interpret- Most outputs are essentially random variables- Thus, not simple to decide whether output is randomness or systembehavior Simulation can be time consuming and expensive- Skimping in time and resources could lead to useless/wrong results The disadvantages are offsetffas ffollows Simulation packages contain models that only need input dataSimulation ppackagesg contain output-analysispy capabilitiespSophistication in computer technology improves simulation timesFor most of the real-world problems there are no closed form solutionsChapter 1. Introduction to Simulation20Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsAreas of Application Applicationppareas of simulation Manufacturing applicationsSemiconductor manufacturingConstr ction engineering and project managementConstructionMilitary applicationsLogistics, supply chain and distribution applicationsTransportation models and trafficBusiness process simulationHealth careCall-centerComputers and NetworksGames.Chapter 1. Introduction to Simulation21Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSystems and System Environment System A system is a group of objects that are joined together in someregular interaction or interdependence toward theaccomplishmentli ht off some purpose. Example: Automobile factory- Machines,, pparts,, and workers operatepjjointlyy to pproduce a vehicle Example: Computer network- User, hosts, routers, lines establish a network System environment Everything outside the system, but affects the system Attention It is important to decide on the boundary between the systemand the system environment This decision depends on the purpose of the studyChapter 1. Introduction to Simulation22Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsComponents of a System In order to understand and analyze a system, we need someterms General Terminology EntityAttributeA ti itActivitySystem state Event Endogenous ExogenousChapter 1. Introduction to SimulationObject of interest in the systemProperty of an entityA titime periodi d off specifiedifi d llengththCollection of variables required to describe thesystemyat anyy timeAn instantaneous occurrence that mightchange the state of the systemActivities and Events occurring within thesystemActivities and Events in the environment(outside the system) that affect the system23Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsComponents of System – ate ing depositsArrival;departureNumber of busytellersNumber of waitingcustomerRapid railRidersSourceDestinationTravelingArrival atstationArrival atdestinationNumber of ridersat each stationNumber of rider intransitProductionMachinesSpeedCapacityBreakdown rateWeldingStampingBreakdownStatus ransmittingArrival atdestinationNumber of waitingmessages to DemandLevels ofinventoryMobility modelNodePositionVelocityTravelEnd offmovementPositionVelocityChapter 1. Introduction to Simulation24Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsDiscrete and Continuous Systems Discrete Systemsy State variables change only atdiscrete set of points Example: Bank ContinuousC tiSSystemst State variables changecontinuously over time Example: Head of waterbehind a damChapter 1. Introduction to Simulation25Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsModel of a System What is a model? A model is a representation of a systemfor the purpose of studying the system.- It is necessary to consider those aspects ofthe system that affect the problem underinvestigation Avoid too much detail Physical model SystemOutput”The tendency is nearly always tosimulate to much detail rather than toolittle Thuslittle.Thus, one should always design themodel around the question to beanswered rather than imitate the realsystem exactly.” [Shannon, 1975]Prototype of a system for the purpose ofstudy.Mathematical model InputA mathematical model uses symbolicnotation and mathematical equations torepresent a system.Chapter 1. Introduction to Simulation26I(t)ModelO(t)Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsModel of a System Examplep 1: Movement Model: d v tAssumptions: Constant velocity v over the whole time tAdvantage: Simple formulae and intuitiveDisadvantage: Seldom valid for a whole travel (human, car,planes)Chapter 1. Introduction to Simulation27Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsModel of a System Example 2: Radio signal propagation Free-Space-Modell Gt Gr λ2 Model: PLdB (d ) 10log 2 2 (4π)d d Assumptions:Ass mptions Advantages:- Simple asymptotic formulae for openspace Disadvantages:- NNot reallyll usefullf ll forf iindoordandd cityienvironmentsChapter 1. Introduction to Simulation28-25strong signalmeasured mean signal strengththeoretical signal strength-30received signaal strength [dBm]- Direct line of of sight (LOS) betweencommunication peers- No obstaclesMeasurements in Computer ScienceDepartment, Informatik 4-35-40-45-50-5555weak signal-605101520distance from access point [m]25Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSimulation Models Simulation Model A simulation model is a particular type of mathematical model of asystem.Ti l ti modelsd l Typesoff simulation Static: Represent a system at a particular point in time. Dynamic: Represent a system over a time interval. Deterministic: Simulation models without random variables. Stochastic: Simulation models with random variables. Discrete: System state changes occur only at discrete time points. Continuous: System state changes occur continuously.We will focus on discrete, dynamic, and stochastic simulationmodelsChapter 1. Introduction to Simulation29Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSimulation ntinuousChapter 1. Introduction to Simulation30discreteDr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsDiscrete-Event System Simulation DiscreteDiscrete-eventevent Simulation System state changes only at discrete set of points in time. Simulation model is analyzed by numerical methods. Numerical methods employ computational procedures to “solve”mathematical models. The model is rather “run”run than “solved”solvedChapter 1. Introduction to Simulation31Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSimulation for static models Monte Carlo simulation Mainly used for mathematical problems which are notanalytically tractable Example: Approximate π Area of a circle: A π r 2 if r 1 A π CCountt ththe numberb off pointsi t iinsideid andd outsidet id a unitit quartertcircle.Chapter 1. Introduction to Simulation32Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSimulation of dynamic,dynamic continuous models Systemydescribed byydifferential equation Typically involves numericalsolution of these equationsq No real difference to anumerically basedmathematical solution Typical example:predator/prey systems Let x(t) be the size of theprey population Let y(t) be the size of thepredator population Growth rate of the ppreyypopulation without predators r x(t) Predator changeg rate -s y(t) Interactionsdx r x(t ) a x(t ) y (t )dtdy s y (t ) b x(t ) y (t )ddt Parameters x(0), y(0), a, b, r, s Metrics x(t), y(t) Solve system of differentialequationsChapter 1. Introduction to Simulation33Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSteps in a Simulation Study1.2.3.4.Problem formulation Clearly understand problemReformulation of the problem Which questions should be answered?Is simulation appropriate?Costs? No general guideModeling tools in research, e.g. UML How to get data?Are random distributions appropriate? Program Does the program that, what the model describes? Do the results match the reality?In cases with no real-world system, hard to validate Which alternatives should be run?Which pparamters should be varied? Program documentation – how does the program workgdocumentation – chronologygy of the workProgressSetting of objectives and overall project planModel conceptualizationData collection5.Model perimental designProduction runs and analysisMore runs?Documentation and reportingImplementationChapter 1. Introduction to Simulation34Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSteps in a Simulation StudyPhase 1:Discoverysco e y aanddOrientationPhase 2:Model buildingand datacollectionMost crucial step isvalidation.If model is invalidresults can lead todangerous andexpensivedecisions!Phase 3:Run the modelPhase 4:ImplementationChapter 1. Introduction to Simulation35Dr. Mesut Güneş
Computer Science, Informatik 4Communication and Distributed SystemsSummary Motivated the course by examplesIntroduced simulation as a notionpurposespsimulation is usefulDiscussed for what pIntroduction of a general terminologyIntroduction of discrete-event simulationDiscussed the steps of a simulation studyChapter 1. Introduction to Simulation36Dr. Mesut Güneş
Modeling and Evaluation of Communication Systems Simulation - B ildth b h i f t ithiBuild the behavior of a system within a program The content of this course is described in the subtitle Modeling and Performance Analysis of by means of Discrete-Event Simulation Chapter 1. Introduction to Simulation 4 Dr. MesutGüneş
I Introduction to Discrete-Event System Simulation 19 1 Introduction to Simulation 21 1.1 When Simulation Is the Appropriate Tool 22 1.2 When Simulation Is Not Appropriate 22 1.3 Advantages and Disadvantages of Simulation 23 1.4 Areas of Application 25 1.5 Some Recent Applications of Simulation
1 Simulation Modeling 1 2 Generating Randomness in Simulation 17 3 Spreadsheet Simulation 63 4 Introduction to Simulation in Arena 97 5 Basic Process Modeling 163 6 Modeling Randomness in Simulation 233 7 Analyzing Simulation Output 299 8 Modeling Queuing and Inventory Systems 393 9 Entity Movement and Material-Handling Constructs 489
I Introduction to Discrete-Event System Simulation Chapter 1 Introduction to Simulation 1.1 When Simulation 1s the Appropnate Tool 1.2 When Simulation 1s Not Appropriate 1.3 Advantages and Disadvantages of Simulation 1.4 Areas of Application 1.5 Systems and System Environment 1.6 Co
Solutions Manual Discrete-Event System Simulation Fourth Edition Jerry Banks John S. Carson II Barry L. Nelson David M. Nicol January 4, 2005. Contents 1 Introduction to Simulation 1 2 Simulation Examples 5 3 General Principles 19 4 Simulation Software 20 5 Statistical Models in Simulation 21 6 Queueing Models 36 7 Random-Number Generation 44 8 .
Engineering Analysis with SolidWorks Simulation 2012 33 Once Simulation has been added, it shows in the main SolidWorks menu and in CommandManager. Figure 2-3: Simulation tab is a part of the SolidWorks CommandManager. Selecting Simulation tab in the CommandManager displays Simulation menu items (icons).
Simulation is a process of emulating real design behavior in a software environment. Simulation helps verify the functionality of a design by injecting stimulus and observing the design outputs. This chapter provides an overview of the simulation process, and the simulation options in the Vivado Design Suite. The process of simulation includes:
4.5 Simulation result for BD generator output system for case one. 78 4.6 Simulation result for BD generator output system for case two. 79 4.7 Simulation result for MT output for case one and two 80 4.8 Simulation results of Surrette battery for two cases. 82 4.9 Simulation data of converter components for two cases. 83
n Simulation and embedded system design ufunctional simulation uperformance simulation ŁPOLIS implementation Łpartitioning example uimplementation simulation Łsoftware-oriented Łhardware-centric n Summary Cycle-based, logic simul. Design flow Behavior capture Mapping (partitioning) Architecture capture Functional simul. Architecture simul .
