1. Analysis of Simulation Results Andy Wang CIS 5930-03 Computer Systems Performance Analysis

2. Analysis of Simulation Results Check for correctness of implementation –Model verification Check for representativeness of assumptions –Model validation Handle initial observations Decide how long to run the simulation 2

3. 3 Model Verification Techniques Verification is similar to debugging –Programmer’s responsibility Validation –Modeling person’s responsibility

4. Top-Down Modular Design Simulation models are large computer programs –Software engineering techniques apply Modularity –Well-defined interfaces for pieces to coordinate Top-down design –Hierarchical structure 4

5. Antibugging Sanity checks –Probabilities of events should add up to 1 –No simulated entities should disappear Packets sent = packets received + packets lost 5

6. Structured Walk-Through Explaining the code to another person Many bugs are discovered by reading the code carefully 6

7. Deterministic Models Hard to verify simulation against random inputs Should debug by specifying constant or deterministic distributions 7

8. Run Simplified Cases Use only one packet, one source, one intermediary node Can compare analyzed and simulated results 8

9. Trace Time-ordered list of events –With associated variables Should have levels of details –In terms of occurred events, procedure called, or variable updates –Properly indented to show levels Should allow the traces to be turned on and off 9

10. On-line Graphic Displays Important when viewing a large amount of data –Verifying a CPU scheduler preempt processes according to priorities and time budgets 10

11. Continuity Test Run the simulation with slightly different values of input parameters –Δ change in input should lead to Δ change in output –If not, possibly bugs 11

12. Degeneracy Test Test for extreme simulation input and configuration parameters –Routers with zero service time –Idle and peak load Also unusual combinations –Single CPU without disk 12

13. Consistency Tests Check results for input parameters with similar effects –Two sources with arrival rate of 100 packets per second –Four sources with arrival rate of 50 packets per second If dissimilar, possibly bugs 13

14. Seed Independence Different random seeds should yield statistically similar results 14

15. Should validate –Assumptions –Input values and distributions –Output values and conclusions Against –Expert intuition –Real system measurements –Theoretical results 15 Model Validation Techniques

16. May not be possible to check all nine possibilities –Real system not available May not be possible at all –The reason why the simulation was built As the last resort –E.g., economic model 16

17. Expert Intuition Should validate assumptions, input, and output separately and as early as possible 17 % packet loss Throughput Why would increased packet loss lead to better throughput?

18. Real-System Measurements Most reliable way for validation –Often not feasible System may not exist Too expensive to measure Apply statistical techniques to compare model and measured data Use multiple traces under different environments 18

19. Theoretical Results Can apply queueing models If too complex –Can validate a small subset of simulation parameters E.g., compare analytical equations with CPU simulation models with one and two cores –Use validated simulation to simulate many cores –Can validate only the common scenarios 19

20. Transient Removal In most cases, we care only about steady-state performance We need to perform transient removal to remove initial data from analysis Difficulty –Find out where transient state ends 20 Transient Removal

21. Long Runs Just run the simulation for a long time –Waste resources –Not sure if it’s long enough 21

22. Proper Initialization Start the simulation in a state close to steady state –Pre-populate requests in various queues –Pre-load memory cache content Reduce the length of transient periods 22

23. Truncation Assume steady state variance < transient state variance Algorithm –Measure variability in terms of range –Remove the first L observations, one at a time –Until the (L + 1)th observation is neither min nor max or the remaining observations 23

24. Truncation 24 L

25. Initial Data Deletion m replications n data points for each replication X ij = j th data point in i th replication 25

26. Initial Data Deletion Step 1: average across replications 26

27. Initial Data Deletion Step 2: compute grand mean µ Step 3: compute µ L = average last n – L values,  L 27

28. Initial Data Deletion Step 4: offset µ L by µ and normalize the result to µ by computing relative change Δ µL = (µ L - µ)/µ 28 Transient interval

29. Moving Average of Independent Replications Similar to initial data deletion Requires computing the mean over a sliding time window 29

30. Moving Average of Independent Replications m replications n data points for each replication X ij = j th data point in i th replication 30

31. Moving Average of Independent Replications Step 1: average across replications 31

32. Moving Average of Independent Replications Step 2: pick a k, say 1; average (j – k) th data point to (j + k) th data point,  j; increase k as necessary 32 Transient interval

33. Batch Means Used for very long simulations Divide N data points into m batches of n data points each Step 1: pick n, say 1; compute the mean for each batch Step 2: compute the mean of means Step 3: compute the variance of means Step 4: n++, go to Step 1 33

34. Batch Means Rationale: as n approaches the transient size, the variance peaks Does not work well with few data points 34 Transient interval

35. Terminating simulations: for systems that never reach a steady state –Network traffic consists of the transfer of small files Transferring large files to reach steady state is not useful –System behavior changes with time Cyclic behavior –Less need for transient removal 35 Terminating Simulations

36. Final Conditions Handling the end of simulations Might need to exclude some final data points –E.g., Mean service time = total service time/n completed jobs 36

37. Stopping Criteria: Variance Estimation If the simulation run is too short –Results highly variable If too long –Wasting resources Only need to run until the confidence interval is narrow enough Since confidence interval is a function of variance, how do we estimate variance? 37

38. Independent Replications m runs with different seed values Each run has n + n 0 data points –First n 0 data points discarded due to transient phase Step 1: compute mean for each replication based on n data points Step 2: compute µ, mean of means Step 3: compute  2, variance of means 38

39. Independent Replications Confidence interval: µ ± z 1-α/2  2 –Use t [1-α/2; m – 1], for m < 30 This method needs to discard mn 0 data points –A good idea to keep m small –Increase n to get narrower confidence 39

40. Batch Means Given a long run of N + n 0 data points –First n 0 data points discarded due to transient phase N data points are divided into m batches of n data points 40

41. Batch Means Start with n = 1 Step 1: compute the mean for each batch Step 2: compute µ, mean of means Step 3: compute  2, variance of means Confidence interval: µ ± z 1-α/2  2 –Use t [1-α/2; m – 1], for m < 30 41

42. Batch Means Compared to independent replications –Only need to discard n 0 data points Problem with batch means –Autocorrelation if the batch size n is small Can use the mean of i th batch to guess the mean of (i + 1) th batch –Need to find a batch size n 42

43. Batch Means Plot batch size n vs. variance of means Plot batch size n vs. autocovariance –Cov(batch_mean i, batch_mean i+1 ),  i 43

44. Method of Regeneration Regeneration –Measured effects for a computational cycle are independent of the previous cycle 44 Regeneration points Regeneration cycle

45. Method of Regeneration m regeneration cycles with n i data points each Step 1: compute y i, sum for each cycle Step 2: compute grand mean, µ Step 3: compute the difference between expected and observed sums w i = y i - n i µ Step 4: compute  2 based on w i 45

46. Method of Regeneration Step 5: compute average cycle length, c Confidence interval: µ ± z 1-α/2  2 /(c  m) –Use t [1-α/2; m – 1], for m < 30 46

47. Method of Regeneration Advantages –Does not require removing transient data points Disadvantages –Can be hard to find regeneration points 47

48. 48 White Slide