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by: Ms. Billy Abbott


Ms. Billy Abbott
GPA 3.85


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Class Notes
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This 20 page Class Notes was uploaded by Ms. Billy Abbott on Monday October 26, 2015. The Class Notes belongs to TELCOM2120 at University of Pittsburgh taught by Staff in Fall. Since its upload, it has received 36 views. For similar materials see /class/229410/telcom2120-university-of-pittsburgh in Telecommunications at University of Pittsburgh.

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Date Created: 10/26/15
System Modeling and Simulation Continued TELCOM Z1Zn Network Perlnrmznce David Tipper Telecommunications Program university nl ttshulgh EventScheduled Computer Simulation Fwst ldenllly events and states necessavy in model the system gt Fuva dueueme wstENDAmval went DEpanuve went salesay numbevmthe wsemandsamsmsewey em mes scheduled in new at a d m 21 is 11le St Cluck advanced in the imminent event 7 Ateach eventtime gt Statesaveupdated edenmeevemmaleccuned meweuanew napshu eme ws1em isbmlt gt FutuveeventsavescheduledDAdeateeventhst lhlsisbeltevthan Benevatmgalleventatthebeginningasmtheexamplebvhand Easytuimplement rig a esluvasmall sys1em With genevalpuvpusela u g 39 i J Common Components of Simulation Models 1 System State Variables used to represent state variable 2 Simulation Clock Variable giving current value of simulated time 3 Event list List of time and type of each future event Initialization routine Subprogram to initialize simulation model at beginning of each un by 1 Set of simulation clock P 2 Set of system state and statistical counters 3 Schedule first event 5 Timing routine Subprogram that determines next event from event list and advances simulation clock 39 I Common Components of Simulation Models 6 Event Routines Subprograms one per event type to process an event by 7 Update system state 7 Possible update statistical counters 7 Scheduled future events of same type determines time of events and adds to the event list 7 Library routines Set of subprograms to generate random variables and gather statistics 8 Report Generator Subprogram that computes statistics and produces a report 9 Main program Reads inputs schedules initialization call event routines report generator end of program etc 39 Components of Simulation Models lnitiaiizatiun reutine Main pregrarn Timing reutine 1 I ml I 2 initialize system state type say l and statistical counters l lrivuke thetimirig routine RE 53mm 2 Advance tne simulation 3 initialize Eventlist 2 lrivuke Eventruutinei F V clock Event reutine l Library reutines i Update system state 2 Update statistical counters Generate random Fow chart Showmg how 3 Generate future events and add tn vanates EvEnLlli the various above interact 39 1 LJ Common Features 2 Implementing a discrete event simulation in a general purpose language is facilitated by having the following common features gt Random number generation 7 Must be uncorrelated actually u0 l fast reproducible Random variate generation v 7 Use random numberas inputs 7 Inverse Transform method Simulation initialization Advancing clock Event time calculation passing control among routines Statistics collection and analysis Output formatting Error debuggingtraces vvvvvv 39 i 1 Simulation Software 7 Can roughly categorize the software tools for building discrete event simulation into four categories General purpose languages gt C Pascal FORTRAN C ADA Java etc Event Scheduled Simulation Languages gt SLAM SIMAN SIMPAS SM JAVASIM etc Process Oriented Simulation Languages 7 CSIM EZSIM GPSS SIMAN SLAM GASP JAVASIM etc Application Oriented Simulators gt Opnet Comnet Tangram ns2 Qualnet Jade etc 39 a General Purpose GP Language x x x x x x C C Pascal Fortran etc Main advantage is modeler usually already knows one language Universally accessible on every computer Efficient in terms of execution time due to less implementation overhead More programming flexibility But benefits of using Simulation Language usually outweigh the advantages of using a general purpose language 39 1 Event Scheduled Simulation Language 7 Commonality of features need for discrete event simulation eg generation of random variables event list etc see Slide 6 lead to the development of event scheduled simulation languages eg Simpas Sim SLAM SIMAN etc Event Scheduled Simulation Languages provide a framework for event scheduled simulation Basically a set of library routines providing the common features needed in discrete event simulation gt For example random variable generation clock event list etc Languages are embedded in a higher level general purpose language eg SIMPAS Pascal SM C SLAM Fortran SIMAN Fortran x x 390 39 1 Event Scheduled Simulation Language 7 User writes event routines and main program in the general purpose language to call the library routines provided by the simulation language Example of SLAM program given on class web page Singsamf Pros and Cons gt Reduces lines of code and mistakes compared to a general purpose language gt Speed comparable to general purpose language gt Need to know the general purpose language that simulation language is embedded in well gt Still write lots of code if many events involved x 390 39 I ProcessOriented Simulation 7 A process describes the entire experience of entity as it ows through the system gt How the entities proceed through the system and leave the systems Simulation time can pass in the process More natural approach than eventscheduling Represents a system by a network ofnodes interconnect by branches The nodes model commonly occurring processes in discrete event systems The branches model entity movement x x x x x 39 A ProcessOriented Simulation 7 Common processes in discrete event systems Entity creation how entities arrive to the system Entity termination how entities are removed from the system Entity tranversal movement of entities through the system Resources substances that the entities use or consume or engage In bWNf Two types of resources 1 service server at a queue 2 regular item consumed by entities token in FIDDI Entity accumulation where entities queue Branch selection process of routing of entities in a system Entity mutiplicationreduction models cloning of entities or the batching of models N90 39 1 ProcessOriented Simulation 390 Process Model building consists of identifying the entities ofthe system and the processes they undergo Construct the model by selecting the appropriately nodes and linking them with branches One then parameterizes the nodes and branches Event scheduling simulation is conducted underneath the process model but it is hidden from users Several simulation languages implement the process approach SLAM SIMAN EZSIM CSIM GPSS OPNET Note process oriented simulation languages typically have many more nodes than the seven common processes eg nodes for statistics collection le attribute manipulation etc x x x x x 39 I 7 ProcessOriented Simulation 2 Single Server Queue Example 2 Entities jobs 2 Process creation accumulation resource termination 2 Model a 2 Parameters 2 Creation time of first creation 0 time between creation exponential random variable with mean 1 attribute 1 time of eation 2 Accumulation FIFO queue infinite capacity 2 Resource 1 server service time exponential with mean 5 2 Termination stop after 6000 jobs 2 Branches all branches unconditional with zero time delay 39 i 1 Process Oriented Simulation Languages 39Pri di 2 Most process oriented simulation have a graphical user interface to facilitate model building and parameter entering gt Single server queue example in EZSIM demonstrated in class gt SLAM process model given on class web page processdat gt CSIM Processoriented SL based on C language 7 Example csimexample on class web page Pros and Cons Provides natural building blocks for modeling Requires little or no programming Better dynamic resources allocation Less error Fewer lines automatic error checkingidentification Provide data structures that facilitate statistics collection Lack of flexibility in modeling systems Slower execution time as using prewritten precompiled blocks of discrete event simulation co e 39x vvvvvvv 39 1 Application Oriented Simulator 2 Simulation packages that provide software to model a particular application domain 2 Provide prewritten event scheduled simulation to model common elements in a particular application eg routers ethernet segments token rings etc 2 Typically have GUI gt Graphical representation of simulated system animation data analysis tools gt Required little or no programming effort gt Reusability of software 2 Cons gt Limited configurations not flexible accuracy of model gt Expensive 2 Opnet Comnet lll Tangram ll Jade Qualnet ns2 39 NS2 example 7 Two nodes and a link with a FIFO queue Link direction 39 i U Implementation 2 1st M gt Send every pkt after an exponential random delay 7 2quotd M gt The pkt size is exponential so the service time transmission time is exponential 2 1 gt The link is the server gt The queue size can be configured easily in NS 2 A large value can emulate MMl l queue 39 Configurations and Run 2 Set up the parameters de ne the parameters of the MM1k queueing system set Iamda 10 set ml 125 set qsize 10000000 k pkts set bw 1000 link bandwidth bits set propagationdelay 50 link propagation delay seconds for better nam animation set samplinginterval 01 for observation seconds 7 Run the program ns mm1ktcl de ne the simulation time set simulationstoptime 10000 seconds O utp uts pi0 017219999999999999 Simulation results pi1 013830000000000001 pi2 Con gurations pi3 0085500000000000007 Iamda 10 pi4 0076600000000000001 mu 125 pi5 0065699999999999995 rholamdalmu 080000000000000004 pi6 0072099999999999997 queue size 10000000 pkts pi7 0049299999999999997 link bandwidth 1000 bitssec pi8 0044400000000000002 link propagation delay 50 secs pi9 0037999999999999999 pi10 0026100000000000002 sults Total pkts sent 1027 Utilization of the link 081899999999999995 Total pkts received 1007 Average pkt num in queue 37761 Total bytes sent 104565 Average pkt num in system 46039000000000003 Total bytes received 102470 Average pkt delay see gure otal kt loss 0 U Outputs Queue length MlNIll0000ltqueue leng 1gt queue tengm 20 0000 17mm 150000 100000 39 1 Vi H Outputs Packet endtoend delay MINI1110000000ltpktdela Avg4501247 pk delay delnyu 200000 15 0000 100000 50000 1 0m x 10 o 0000 02000 04000 05000 08000 10000 1T 4 Outputs Steady state probability MlM110000000ltsteady stategt prupnbility x 10393 1800000 PiT 1500000 1400000 1200000 100 0000 300000 500000 400000 200000 numberin system 00000 200000 400000 500000 800000 1000000 I 1 L Animation 2 Automatic pop up or type in nam outnam 3 339 mcb emudell mow wade Object Attributes Allrbules are dynamically changeable during simulation Processes have access to all object allribules quot Object Attributes D A New Project in Modeler 7 The nude cuntamstwu processormo ues te une queue module and Mn packeisneams mudehng MM Cumams a new smano wmch cumams une node mode Packet aveam suuvce and sum Pmcessm U Attributes of Source 9 Packet mteramva me gt Dmvmmmn name expunennax gt Mean uutcume 1n 9 Packet swze gt Dmvmmmn name expunennax gt Mean uutcume gum Attributes of Queue 1 Queue atmbutes gt Pmcessmade ach a mm gt Semwte 95cm n Attributes of Sink 7 Slnk allrlbuleS gt Pm 55 made smk mm m u m mm Results 7 Probe Emzor 5 used m anect queue 5sz and queue de ay Resuhs Run for 7 hours and Analysis Tool is used to View results Queue delay ypmm1netrmml avugn subqueue 0 ufmlqu gmwmm Resuhs Queue size ypmm1 nel m uquot 3 5 m1 subqueue 01 ofm1qu aglu m an m wkl 39 Selecting Simulation Software 7 Do not focus only on a single issue eg ease of use gt Accuracy level of detail learning curve support 7 Execution speed important when debugging 7 Beware ofclaims advertisement 7 Customized routines with external languages gt But be sure its powerful enough to avoid such thing 7 Tradeoff between application simulators and SLs gt Still need procedural logic and debugging gt No programming requiredquot can be tricky 39 1quot Ll Tradeoff 7 Development time Simulator lt SL lt GP 7 Run time GP lt SL lt Simulator 7 Degree of portability Simulator lt SL lt GP 7 Cost GP lt SL lt Simulator Factors to Consider General Special Simulation Special Simulation Application Factors Oriented Languages Event Oriented Process Oriented Simulators 1 Cost Lowests 50 Med s 500 Med 1500730000 Hignest gt 30000 2 Portability Greatest Med Med Least 3 Learning curve steepest Medangh Med Easiest szfjp g fde39 Difficult HardMed MedLow Easy 5 Software support Little Medalee Medangh Most 3quoti39f 3 mquot None Little Medangh Hignest 7 Statistical capability None MoneSome SomeAutomatic Some allexibility Hign Hign Medangh Low Xp39ammquot Low Low Medangh Hign 10 Execution speed Fastest Fast MedaSlow Slow


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