Evaluating dependability metrics of critical systems: Monte ... - iaria

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Accuracy: how accurate is ¯X n ? We can evaluate the accuracy of ¯X n by means of the Central Limit Theorem, which allows us to build the following confidence interval: ( CI = ¯X n − c √ ασ , ¯X n + c ) √ ασ n n ◮ meaning: P(µ ∈ CI) ≈ 1 − α; α: confidence level ◮ (that is, on a large number M of experiences (of estimations of µ using ¯X n ), we expect that in roughly a fraction α of the cases (in about αM cases), the confidence interval doesn’t contain µ) ◮ cα = Φ −1 (1 − α/2) where Φ is the cdf of N(0, 1) ◮ σ 2 = Var[X] = E[X 2 ] − E 2 [X], usually unknown and estimated by S 2 n = 1 n − 1 n∑ i=1 Xi 2 − n n − 1 ¯X n 2 . G. Rubino (INRIA) Monte Carlo DEPEND 2010 10 / 72
Remarks on the confidence interval Size of the confidence interval: 2c α σ/ √ n. The smaller α, the more confident we are in the result: P( µ belongs to CI ) ≈ 1 − α. But, if we reduce α (without changing n), c α increases: ◮ α = 10% gives cα = 1.64, ◮ α = 5% gives cα = 1.96, ◮ α = 1% gives cα = 2.58. The other way to have a better confidence interval is to increase n. The 1/ √ n factor says that to reduce the width of the confidence interval by 2, we need 4 times more replications. G. Rubino (INRIA) Monte Carlo DEPEND 2010 11 / 72
Page 1 and 2: Evaluating dependability metrics of
Page 3 and 4: Outline 1 Introduction 2 Monte Carl
Page 5: Rare events Rare events occur when
Page 12 and 13: A fundamental example: evaluating i
Page 14 and 15: Other examples Reliability at t:
Page 16 and 17: From the accuracy point of view, th
Page 18 and 19: Variance reduction: antithetic vari
Page 20 and 21: Variance reduction: common variable
Page 22 and 23: Variance reduction: control variabl
Page 24: Monte Carlo drawbacks So, is there
Page 27 and 28: What is crude simulation? Assume we
Page 29 and 30: Inefficiency of crude Monte Carlo:
Page 31 and 32: Robustness properties: Bounded rela
Page 33 and 34: Relation between BRE and AO AO weak
Page 37 and 38: Estimator and goal of IS Take (Y i
Page 39 and 40: If A = [T, ∞), i.e., µ = P[Y ≥
Page 41 and 42: IS for a discrete-time Markov chain
Page 43 and 44: Zero-variance IS estimator for Mark
Page 45 and 46: Zero-variance approximation Use a h
Page 47 and 48: Drawbacks of the learning technique
Page 49 and 50: Other procedure: optimization withi
Page 51 and 52: Adaptive learning in Cross-Entropy
Page 53 and 54: Splitting: general principle Splitt
Page 55 and 56: Splitting and Markov chain {Y j ; j
Page 57 and 58: The different implementations Fixed
Page 59 and 60: Issues to be solved How to define t
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Optimal values In a general setting
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Simplified setting: fixed splitting
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Illustration, fixed effort: a tande
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Confidence interval issues Robustne
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IS probability used Failure Biasing
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Asymptotic explanation When ε smal
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Tutorial on Monte Carlo techniques,
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• Very used in dependability anal
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• Usual situation: at some time s
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first order (in ε) expressions bef
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efore after a 1 a 2 x ε 2 ε 1 a 1
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symbolically, for an operational st
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• Always the same idea: take adva
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ε 1 ε 2 a 1 x a 2 before ε 4 ε
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• The zero variance idea in IS le
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• A variance-reduction procedure
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s t r 1 r 2 r 3 r 4 r 5 R st = r 1
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A COMMUNICATION NETWORK backbone, o
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A COMMUNICATION NETWORK • nodes a
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• Ω : set of all partial sub-gra
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• #failed = 0 • for m = 1, 2,
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• the correct answer given by the
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R = 1 R = r × R = r |K | = 1 |K |
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1 R r 2 = R r 1 r 2 R r 1 r 2 r 3 r
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1 R r 2 = R r 1 r 2 R r 1 r 5 etc.
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• Again, with P be a path and P-u
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- if the network is r 1 r 2 r 3 - t
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- then if the network is - then Z =
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• We partition Ω in the followin
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Ω = {L 1 L 2 , L 1 , L 1 L 2 } Z =
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Ω = {L 1 L 2 , L 1 , L 1 L 2 } Z =
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• It consists of generalizing thi
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Ω L 1 L’ 2 L’ 3 L 1 L 1 L’ 2
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L 1 L’ 2 L’ 3 L 1 L 1 L’ 2 L
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L 1 L’ 2 L’ 3 L 1 L 1 L’ 2 L
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L 1 L’ 2 L’ 3 L 1 L 1 L’ 2 L
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• sum = 0.0 • for m = 1, 2, …
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|V | = 9, |K | = 4, |E | = 12 M = 1
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• We varied K (|K | = 2, |K | = 5
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• We also varied r i = r from 0.9
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• A time-reduction procedure for
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• Internal loop: sampling a graph
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• We consider the case of case r
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• For a table with M rows, - each
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• Dividing the mean cost of the s
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• The procedure can be improved f
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• Reference: G. Rubino, B. Tuffin
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Evaluating dependability metrics of critical systems: Monte ... - iaria

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