CPE 619 Introduction To Simulation

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CPE 619 Introduction To Simulation. Aleksandar Milenković The LaCASA Laboratory Electrical and Computer Engineering Department The University of Alabama in Huntsville http://www.ece.uah.edu/~milenka http://www.ece.uah.edu/~lacasa. Overview. Simulation: Key Questions

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### CPE 619Introduction To Simulation

Aleksandar Milenković

The LaCASA Laboratory

Electrical and Computer Engineering Department

The University of Alabama in Huntsville

http://www.ece.uah.edu/~milenka

http://www.ece.uah.edu/~lacasa

Overview
• Simulation: Key Questions
• Introduction to Simulation
• Common Mistakes in Simulation
• Other Causes of Simulation Analysis Failure
• Checklist for Simulations
• Terminology
• Types of Models
Simulation: Key Questions
• What are the common mistakes in simulation and why most simulations fail?
• What language should be used for developing a simulation model?
• What are different types of simulations?
• How to schedule events in a simulation?
• How to verify and validate a model?
• How to determine that the simulation has reached a steady state?
• How long to run a simulation?
Simulation: Key Questions (cont’d)
• How to generate uniform random numbers?
• How to verify that a given random number generator is good?
• How to select seeds for random number generators?
• How to generate random variables with a given distribution?
• What distributions should be used and when?
Introduction to Simulation

The best advice to those about to embark on a very large simulation is often the same as Punch\'s famous advice to those about to marry: Don\'t!

-Brately, Fox, and Schrage (1987)

Common Mistakes in Simulation

1. Inappropriate Level of Detail:More detail Þ More time Þ More Bugs Þ More CPU Þ More parameters ¹ More accurate

2. Improper Language

General purpose Þ More portable, More efficient, More time

3. Unverified Models: Bugs

4. Invalid Models: Model vs. reality

5. Improperly Handled Initial Conditions

6. Too Short Simulations: Need confidence intervals

7. Poor Random Number Generators: Safer to use a well-known generator

8. Improper Selection of Seeds: Zero seeds, Same seeds for all streams

Other Causes of Simulation Analysis Failure

2. No Achievable Goal

3. Incomplete Mix of Essential Skills

(b) Modeling and

(c) Programming

(d) Knowledge of the Modeled System

4. Inadequate Level of User Participation

5. Obsolete or Nonexistent Documentation

6. Inability to Manage the Development of a Large Complex Computer Program Need software engineering tools

7. Mysterious Results

Checklist for Simulations

1. Checks before developing a simulation:

(a) Is the goal of the simulation properly specified?

(b) Is the level of detail in the model appropriate for the goal?

(c) Does the simulation team include personnel with project

leadership, modeling, programming, and computer systems

backgrounds?

(d) Has sufficient time been planned for the project?

2. Checks during development:

(a) Has the random number generator used in the simulation

been tested for uniformity and independence?

(b) Is the model reviewed regularly with the end user?

(c) Is the model documented?

Checklist for Simulations (cont’d)

3.Checks after the simulation is running:

(a) Is the simulation length appropriate?

(b) Are the initial transients removed before computation?

(c) Has the model been verified thoroughly?

(d) Has the model been validated before using its results?

(e) If there are any surprising results, have they been validated?

(f) Are all seeds such that the random number streams will not overlap?

Terminology
• Introduce terms using an example of simulating CPU scheduling
• Study various scheduling techniques given job characteristics, ignoring disks, display…
• State Variables: Define the state of the system
• Can restart simulation from state variables
• E.g., length of the job queue.
• Event: Change in the system state
• E.g., arrival, beginning of a new execution, departure
Terminology: Types of Models
• Continuous Time Model
• State is defined at all times
• Discrete Time Models
• State is defined only at some instants
Terminology: Types of Models (cont’d)
• Continuous State Model
• State variables are continuous
• Discrete State Models
• State variables are discrete
Terminology: Types of Models (cont’d)
• Discrete state = Discrete event model
• Continuous state = Continuous event model
• Continuity of time ¹ Continuity of state
• Four possible combinations
• 1. discrete state/discrete time
• 2. discrete state/continuous time
• 3. continuous state/discrete time
• 4. continuous state/continuous time
Terminology: Types of Models (cont’d)
• Deterministic and Probabilistic Models
• Deterministic - If output predicted with certainty
• Probabilistic - If output different for different repetitions

Output

Output

Input

Input

(Linear)

(Non-Linear)

Terminology: Types of Models (cont’d)
• Static and Dynamic Models
• Static - Time is not a variable
• Dynamic - If changes with time
• E.g.: CPU scheduler is dynamic, while matter-to-energy model E=mc2 is static
• Linear and nonlinear models
• Linear - Output is linear combination of input
• Nonlinear - Otherwise

cpu

cpu

open

closed

Terminology: Types of Models (cont’d)
• Open and closed models
• Open - Input is external and independent
• Closed - Model has no external inputs
• Ex: if same jobs leave and re-enter queue then closed, while if new jobs enter system then open
Terminology: Types of Models (cont’d)
• Stable and unstable
• Stable - Model output settles down
• Unstable - Model output always changes
Computer System Models
• Continuous time
• Discrete state
• Probabilistic
• Dynamic
• Nonlinear
• Open or closed
• Stable or unstable
Selecting a Language for Simulation
• Four choices
• 1. Simulation language
• 2. General purpose
• 3. Extension of a general purpose language
• 4. Simulation package
Selecting a Language for Simulation (cont’d)
• Simulation language – built in facilities for time steps, event scheduling, data collection, reporting
• General-purpose – known to developer, available on more systems, flexible
• The major difference is the cost tradeoff (SL vs. GPL)
• SL+: save development time (if you know it), more time for system specific issues, more readable code
• SL-: requires startup time to learn
• GPL+: Analyst\'s familiarity, availability, quick startup
• GPL-: may require more time to add simulation flexibility, portability, flexibility
• Recommendation may be for all analysts to learn one simulation language so understand those “costs” and can compare
Selecting a Language for Simulation
• Extension of general-purpose – collection of routines and tasks commonly used. Often, base language with extra libraries that can be called
• Simulation packages – allow definition of model in interactive fashion. Get results in one day
• Tradeoff is in flexibility, where packages can only do what developer envisioned, but if that is what is needed then is quicker to do so
• Examples: GASP (for FORTRAN)
• Collection of routines to handle simulation tasks
• Compromise for efficiency, flexibility, and portability.
• Examples: QNET4, and RESQ
• Input dialog
• Library of data structures, routines, and algorithms
• Big time savings
• Inflexible Þ Simplification
Types of Simulation Languages
• Continuous Simulation Languages
• CSMP, DYNAMO
• Differential equations
• Used in chemical engineering
• Discrete-event Simulation Languages
• SIMULA and GPSS
• Combined
• SIMSCRIPT and GASP
• Allow discrete, continuous, as well as combined simulations.
Types of Simulations

1. Emulation: Using hardware or firmware

2. Monte Carlo Simulation

3. Trace-Driven Simulation

4. Discrete Event Simulation

Java program

Java VM

Process

Process

Operating System

Hardware

Types of Simulations (cont’d)
• Emulation
• Simulation that runs on a computer to make it appear to be something else
• Examples: JVM, NIST Net
Types of Simulation (cont’d)

Monte Carlo method [Origin: after Count Montgomery

de Carlo, Italian gambler and random-number

generator (1792-1838).] A method of jazzing up the

action in certain statistical and number-analytic

environments by setting up a book and inviting bets on

the outcome of a computation.

- The Devil\'s DP Dictionary

McGraw Hill (1981)

Monte Carlo Simulation
• A static simulation has no time parameter
• Runs until some equilibrium state reached
• Used to model physical phenomena, evaluate probabilistic system, numerically estimate complex mathematical expression
• Driven with random number generator
• So “Monte Carlo” (after casinos) simulation
• Example, consider numerically determining the value of 
• Area of circle = 2 for radius 1
Monte Carlo Simulation (cont’d)
• Imagine throwing dart at square
• Random x (0,1)
• Random y (0,1)
• Count if inside
• sqrt(x2+y2) < 1
• Compute ratio R
• in / (in + out)
• Can repeat as many times as needed to get arbitrary precision
• Unit square area of 1
• Ratio of area in quarter to area in square = R
•  = 4R
Monte Carlo Simulation (cont’d)
• Evaluate the following integral
• 1. Generate uniformly distributed x ~ Uniform(0,2)
• 2. Density function f(x)=1/2 iff 0x 2
• 3. Compute:
Trace-Driven Simulation
• Uses time-ordered record of events on real system as input
• Example: to compare memory management, use trace of page reference patterns as input, and can model and simulate page replacement algorithms
• Note, need trace to be independent of system
• Example: if had trace of disk events, could not be used to study page replacement since events are dependent upon current algorithm

1. Credibility

2. Easy Validation: Compare simulation with measured

3. Accurate Workload: Models correlation and interference

Detailed workload Þ Can study small changes in algorithms

5. Less Randomness:

Trace Þ deterministic input Þ Fewer repetitions

6. Fair Comparison: Better than random input

7. Similarity to the Actual Implementation:

Trace-driven model is similar to the system

Þ Can understand complexity of implementation

1. Complexity: More detailed

2. Representativeness: Workload changes with time, equipment

3. Finiteness: Few minutes fill up a disk

4. Single Point of Validation: One trace = one point

5. Detail

Discrete Event Simulations
• A simulation using a discrete state model of the system is DISCRETE EVENT SIMULATION
• Continuous-event simulations – the state of the system takes continuous values
• Typical components:
• Event scheduler
• Simulation Clock and a Time Advancing Mechanism
• System State Variables
• Event Routines
• Input Routines
• Report Generator
• Initialization Routines
• Trace Routines
• Dynamic Memory Management
• Main Program
Components of Discrete Event Simulations
• Event scheduler – linked list of events waiting
• Schedule event X at time T
• Hold event X for interval dt
• Cancel previously scheduled event X
• Hold event X indefinitely until scheduled by other event
• Schedule an indefinitely scheduled event
• Note, event scheduler executed often, so has significant impact on performance
• Simulation Clock and a Time Advancing Mechanism
• Global variable representing simulated time (maintained by the scheduler)
• Two approaches
• Unit-time approach: increment time and check for events
• Event-driven approach: move to the next event in queue
Components of Discrete Events Sims (cont’d)
• System State Variable
• Global variables describing the state of the systems(e.g., the umber of jobs in CPU scheduling simulation)
• Local variables (e.g., CPU time required for a job is placed in the data structure for that particular job)
• Event Routines -- one per event; update state variables and schedule other events
• E.g., job arrivals, job scheduling, and job departure
• Input Routines
• Get model parameters (e.g., means CPU time per job) from the user
• Very parameters in a range
Components of Discrete Events Sims (cont’d)
• Report Generator
• Output routines run at the end of the simulation
• Initialization Routines
• Set the initial state of the system state variables. Initialize seeds.
• Trace Routines
• Print out intermediate variables as the simulation proceeds
• On/off feature
• Dynamic Memory Management
• New entities are created and old ones are destroyed
• Periodic garbage collection
• Main Program
• Tie everything together

Tail

Next

Next

Next

Previous

Previous

Previous

Event 2

Event n

Event 1

Code for

event 2

Code for

event n

Code for

event 1

Event-Set Algorithms
• Event Set = Ordered linked list of future event notices
• Insert vs. Execute next
• 1. Ordered Linked List: SIMULA, GPSS, and GASP IV
• Search from left or from right

t

Tail 1

t+Dt

Tail 2

t+nDt

Tail 3

Event-Set Algorithms (cont’d)
• 2. Indexed Linear List
• Array of indexes Þ No search to find the sub-list
• Fixed or variable Dt. Only the first list is kept sorted

1

15

2

19

3

28

4

5

6

7

23

27

39

45

8

25

9

47

10

11

50

12

48

34

(a) Tree representation of a heap.

Event-Set Algorithms (Cont)
• 3. Tree Structures: Binary tree Þ log2 n
• Special case: Heap: Event is a node in binary tree
Summary
• Common Mistakes: Detail, Invalid, Short
• Discrete Event, Continuous time, nonlinear models
• Monte Carlo Simulation: Static models
• Trace driven simulation: Credibility, difficult trade-offs
• Even Set Algorithms: Linked list, indexed linear list, heaps

### Analysis of Simulation Results

Overview
• Analysis of Simulation Results
• Model Verification Techniques
• Model Validation Techniques
• Transient Removal
• Terminating Simulations
• Stopping Criteria: Variance Estimation
• Variance Reduction
Model Verification vs. Validation
• The model output should be close to that of real system
• Make assumptions about behavior of real systems
• 1st step, test if assumptions are reasonable
• Validation, or representativeness of assumptions
• 2nd step, test whether model implements assumptions
• Verification, or correctness
• Four Possibilities
• Unverified, Invalid
• Unverified, Valid
• Verified, Invalid
• Verified, Valid
Model Verification Techniques
• Top Down Modular Design
• Anti-bugging
• Structured Walk-Through
• Deterministic Models
• Run Simplified Cases
• Trace
• On-Line Graphic Displays
• Continuity Test
• Degeneracy Tests
• Consistency Tests
• Seed Independence
Top Down Modular Design
• Divide and Conquer
• Modules = Subroutines, Subprograms, Procedures
• Modules have well defined interfaces
• Can be independently developed, debugged, and maintained
• Top-down design Þ Hierarchical structure Þ Modules and sub-modules
Top Down Modular Design (cont’d)

Computer Network Simulator for Congestion Control studies

Verification Techniques
• Anti-bugging: Include self-checks
• å Probabilities = 1
• Jobs left = Generated - Serviced
• Structured Walk-Through
• Explain the code another person or group
• Works even if the person is sleeping
• Deterministic Models: Use constant values
• Run Simplified Cases
• Only one packet
• Only one source
• Only one intermediate node
Verification Techniques (cont’d)
• Trace = Time-ordered list of events and variables
• Several levels of detail
• Events trace
• Procedure trace
• Variables trace
• User selects the detail
• Include on and off
Verification Techniques (cont’d)
• On-Line Graphic Displays
• Make simulation interesting
• Help selling the results
• More comprehensive than trace
Verification Techniques (cont’d)
• Continuity Test
• Run for different values of input parameters
• Slight change in input Þ slight change in output
• If not, investigate

Before

After

Verification Techniques (cont’d)
• Degeneracy Tests: Try extreme configuration and workloads
• One CPU, Zero disk
• Consistency Tests
• Similar result for inputs that have same effect
• Four users at 100 Mbps vs. Two at 200 Mbps
• Build a test library of continuity, degeneracy and consistency tests
• Seed Independence: Similar results for different seeds
Model Validation Techniques
• Ensure assumptions used are reasonable
• Final simulated system should be like the real system
• Unlike verification, techniques to validate one simulation may be different from one model to another
• Three key aspects to validate
• Assumptions
• Input parameter values and distributions
• Output values and conclusions
• Compare validity of each to one or more of
• Expert intuition
• Real system measurements
• Theoretical results
•  9 combinations
• Not all are
• always possible,
• however

Which alternative

looks invalid? Why?

Throughput

0.2

0.4

0.8

Expert Intuition
• Most practical and common way
• Experts = Involved in design, architecture, implementation, analysis, marketing, or maintenance of the system
• Present assumption, input, output
• Better to validate one at a time
• See if the experts can distinguish simulation vs. measurement
Real System Measurements
• Most reliable and preferred
• May be unfeasible because system does not exist or too expensive to measure
• That could be why simulating in the first place!
• But even one or two measurements add an enormous amount to the validity of the simulation
• Should compare input values, output values, workload characterization
• Use multiple traces for trace-driven simulations
• Can use statistical techniques (confidence intervals) to determine if simulated values different than measured values
Theoretical Results
• Can be used to compare a simplified system with simulated results
• May not be useful for sole validation but can be used to complement measurements or expert intuition
• E.g.: measurement validates for one processor, while analytic model validates for many processors
• Note, there is no such thing as a “fully validated” model
• Would require too many resources and may be impossible
• Can only show is invalid
• Instead, show validation in a few select cases, to lend confidence to the overall model results
Transient Removal
• Most simulations only want steady state
• Remove initial transient state
• Trouble is, not possible to define exactly what constitutes end of transient state
• Use heuristics:
• Long runs
• Proper initialization
• Truncation
• Initial data deletion
• Moving average of replications
• Batch means
Long Runs
• Use very long runs
• Effects of transient state will be amortized
• But … wastes resources
• And tough to choose how long is “enough”
• Recommendation … don’t use long runs alone
Proper Initialization
• Start simulation in state close to expected state
• Ex: CPU scheduler may start with some jobs in the queue
• Determine starting conditions by previous simulations or simple analysis
• May result in decreased run length, but still may not provide confidence that are in stable condition
Assume variability during steady state is less than during transient state

Variability measured in terms of range

(min, max)

If a trajectory of range stabilizes, then assume that in stable state

Method:

Given n observations {x1, x2, …, xn}

Ignore first l observations

Calculate (min,max) of remaining n-l

Repeat for l = 1…n

Stop when l+1th observation is neither min nor max

Truncation

Ignore first (l=1), range is (2, 11) and 2nd observation (l+1) is the min

Ignore second (l=2), range is (3,11) and 3rd observation (l+1) is min

Finally, l=9 and range is (9,11) and 10th observation is neither min nor max

Transient

Interval

Truncation Example
Find duration of transient interval for:

11, 4, 2, 6, 5, 7, 10, 9, 10, 9, 10, 9, 10

When l=3, range is (5,10) and 4th (6) is not min or max

“Real” transient

Assumed transient

Truncation Example 2 (2 of 2)
Initial Data Deletion (1 of 3)
• Study average after some initial observations are deleted from sample
• If average does not change much, must be deleting from steady state
• However, since randomness can cause some fluctuations during steady state, need multiple runs (w/different seeds)
• Given m replications size n each with xij – jth observation of ith replication
• Note j varies along time axis and i varies across replications
Initial Data Deletion (2 of 3)
• Get mean trajectory:

xj = (1/m)xijj=1,2,…,n

• Get overall meanx = (1/n)xjj=1,2,…,n
• Set l=1. Assume transient state l long, delete first l and repeat for remaining n-l

xl = (1/(n-l))xjj=l+1,…,n

• Compute relative change

(xl – x) / x

• Repeat with l from 1 to n-1. Plot. Relative change graph will stabilize at knee. Choose l there and delete 1 through l

xij

xj

j

j

transient

interval

xl

(xl – x) / x

knee

l

l

Initial Data Deletion (3 of 3)

xj

j

transient

interval

Mean xj

knee

j

Moving Average of Independent Replications
• Compute mean over moving time window
• Get mean trajectory

xj = (1/m)xijj=1,2,…,n

• Set k=1. Plot moving average of 2k+1 values:

Mean xj= 1/(2k+1) (xj+l)

With j=k+1, k+2,…,n-k

With l=-k to k

• Repeat for k=2,3… and plot until smooth
• Find knee. Value at j is length of transient phase.
Run for long time

N observations

Divide up into batches

m batches size n each so m = N/n

Compute batch mean (xi)

Compute var of batch means as function of batch size (X is overall mean)

Var(x) = (1/(m-1))(xi-X)2

Plot variance versus size n

When n starts decreasing, have transient

n

2n

3n

4n

5n

(Ignore)

Responses

Variance of

batch means

transient

interval

Observation number

Batch size n

Batch Means
Terminating Simulations
• For some simulations, transition state is of interest no transient removals required
• Sometimes upon termination you also get final conditions that do not reflect steady state
• Can apply transition removal conditions to end of simulation
• Take care when gathering at end of simulation
• E.g.: mean service time should include only those that finish
• Also, take care of values at event times
• E.g.: queue length needs to consider area under curve
• Say t=0 two jobs arrive, t=1 one leaves, t=4 2nd leaves
• qlengths q0=2, q1=1 q4=0 but q average not (2+1+0)/3=1
• Instead, area is 2 + 1 + 1 + 1 so q average 5/4=1.25
Stopping Criteria: Variance Estimation
• Run until confidence interval is narrow enough
• For Independent observations:
• Independence not applicable to most simulations
• Large waiting time for ith job Þ Large waiting time for (i+1)th job
• For correlated observations:
Variance Estimation Methods

1. Independent Replications

2. Batch Means

3. Method of Regeneration

Independent Replications
• Assumes that means of independent replications are independent
• Conduct m replications of size n+n0 each

1. Compute a mean for each replication:

2. Compute an overall mean for all replications:

Independent Replications (cont’d)

3. Calculate the variance of replicate means:

4. Confidence interval for the mean response is:

• Keep replications large to avoid waste
• Ten replications generally sufficient
Batch Means
• Also called method of sub-samples
• Run a long simulation run
• Discard initial transient interval, and Divide the remaining observations run into several batches or sub-samples.

1. Compute means for each batch:

2. Compute an overall mean:

Batch Means (cont’d)

3. Calculate the variance of batch means:

4. Confidence interval for the mean response is:

• Less waste than independent replications
• Keep batches long to avoid correlation
• Check: Compute the auto-covariance of successive batch means:
• Double n until autocovariance is small
Case Study 25.1: Interconnection Networks
• Indirect binary n-cube networks: Used for processor-memory interconnection
• Two stage network with full fan out.
• At 64, autocovariance < 1% of sample variance
Method of Regeneration

Regeneration Points

• Behavior after idle period does not depend upon the past history Þ System takes a new birthÞ Regeneration point
• Note: The regeneration point are the beginning of the idle interval. (not at the ends as shown in the book).

QueueLength

Method of Regeneration (cont’d)
• Regeneration cycle: Between two successive regeneration points
• Use means of regeneration cycles
• Problems:
• Not all systems are regenerative
• Different lengths Þ Computation complex
• Overall mean ¹ Average of cycle means
• Cycle means are given by:
Method of Regeneration (cont’d)
• Overall mean:

1. Compute cycle sums:

2. Compute overall mean:

3. Calculate the difference between expected and observed cycle sums:

Method of Regeneration (cont’d)

4. Calculate the variance of the differences:

5. Compute mean cycle length:

6. Confidence interval for the mean response is given by:

7. No need to remove transient observations

Method of Regeneration: Problems

1. The cycle lengths are unpredictable. Can\'t plan the simulation time beforehand.

2. Finding the regeneration point may require a lot of checking after every event.

3. Many of the variance reduction techniques can not be used due to variable length of the cycles.

4. The mean and variance estimators are biased

Variance Reduction
• Reduce variance by controlling random number streams
• Introduce correlation in successive observations
• Problem: Careless use may backfire and lead to increased variance.
• For statistically sophisticated analysts only
• Not recommended for beginners
Summary
• Verification = Debugging  Software development techniques
• Validation  Simulation = Real  Experts involvement
• Transient Removal: Initial data deletion, batch means
• Terminating Simulations = Transients are of interest
• Stopping Criteria: Independent replications, batch means, method of regeneration
• Variance reduction is not for novice