Mining for Statistical Models of Availability in Large-Scale Distributed Systems: An Empirical Study of
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1 Mining for Statistical Models of Availability in Large-Scale Distributed Systems: An Empirical Study of Bahman Javadi 1, Derrick Kondo 1, Jean-Marc Vincent 1,2, David P. Anderson 3 1 Laboratoire d Informatique de Grenoble, MESCAL team, INRIA, France 2 University of Joseph Fourier, France 3 UC Berkeley, USA IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2009) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
2 Introduction and Motivation P2P, Grid, Cloud, and Volunteer computing systems B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
3 Introduction and Motivation P2P, Grid, Cloud, and Volunteer computing systems Main Features: Tens or hundreds of thousands of unreliable and heterogeneous hosts B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
4 Introduction and Motivation P2P, Grid, Cloud, and Volunteer computing systems Main Features: Tens or hundreds of thousands of unreliable and heterogeneous hosts Uncertainty of host availability B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
5 Introduction and Motivation P2P, Grid, Cloud, and Volunteer computing systems Main Features: Tens or hundreds of thousands of unreliable and heterogeneous hosts Uncertainty of host availability B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
6 Introduction and Motivation P2P, Grid, Cloud, and Volunteer computing systems Main Features: Tens or hundreds of thousands of unreliable and heterogeneous hosts Uncertainty of host availability Main Motivation Effective Resource Selection for Stochastic Scheduling Algorithms B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
7 Introduction and Motivation P2P, Grid, Cloud, and Volunteer computing systems Main Features: Tens or hundreds of thousands of unreliable and heterogeneous hosts Uncertainty of host availability Main Motivation Effective Resource Selection for Stochastic Scheduling Algorithms Goal Model of host availability (i.e., subset of hosts with the same availability distribution) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
8 Outline Introduction and Motivation 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
9 Define Availability Measurement CPU availability on each host CPU on host i A1 Time A2 An Length of Availability Intervals: A1, A2,..., An B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
10 Measurement Measurement Method BOINC Middleware for volunteer computing systems Underlying software infrastructure for projects such as B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
11 Measurement Measurement Method BOINC Middleware for volunteer computing systems Underlying software infrastructure for projects such as We instrumented the BOINC client to collect CPU availability traces: Total number of host traces: 226,208 Collection period: April 1, Jan 1, 2009 Total CPU time: 57,800 years Number of intervals: 102,416,434 Assume 100% or 0% availability B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
12 Outline Measurement Remove outliers 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
13 Outliers Measurement Remove outliers Data Set Remove Outliers B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
14 Outliers Measurement Remove outliers Data Set Remove Outliers Check for outliers: Artifacts resulted from a benchmark run periodically every five days B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
15 Outliers Measurement Remove outliers Data Set Remove Outliers Check for outliers: Artifacts resulted from a benchmark run periodically every five days B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
16 Outliers Measurement Remove outliers Data Set Remove Outliers Check for outliers: Artifacts resulted from a benchmark run periodically every five days B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
17 Outline Modelling Process Randomness Tests 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
18 Randomness Tests Modelling Process Randomness Tests Data Set Remove Outliers Randomness Tests Pass iid Hosts Not pass Non-iid Hosts To determine which hosts have truly random availability intervals B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
19 Randomness Tests Modelling Process Randomness Tests Data Set Remove Outliers Randomness Tests Pass iid Hosts Not pass Non-iid Hosts To determine which hosts have truly random availability intervals Four well-known non-parametric tests: Runs test Runs up/down test Mann-Kendall test Autocorrelation function test (ACF) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
20 Modelling Process Randomness Tests Randomness Tests Data Set Remove Outliers Randomness Tests Pass iid Hosts Not pass Non-iid Hosts To determine which hosts have truly random availability intervals Four well-known non-parametric tests: Runs test Runs up/down test Mann-Kendall test Autocorrelation function test (ACF) Test Runs std Runs up/down ACF Kendall All # of hosts Fraction B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
21 Modelling Process Randomness Tests Randomness Tests Data Set Remove Outliers Randomness Tests Pass iid Hosts Not pass Non-iid Hosts To determine which hosts have truly random availability intervals Four well-known non-parametric tests: Runs test Runs up/down test Mann-Kendall test Autocorrelation function test (ACF) Test Runs std Runs up/down ACF Kendall All # of hosts Fraction Result: 34% are i.i.d. hosts (2.2 PetaFLOPS) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
22 Outline Modelling Process Clustering 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
23 Modelling Process Clustering Distribution of Availability Intervals Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
24 Modelling Process Clustering Distribution of Availability Intervals Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts 8 x Frequency Availability (hours) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
25 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
26 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function Method: Hierarchical B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
27 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function Method: Hierarchical Compute all permutations B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
28 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function Method: Hierarchical Compute all permutations Memory intensive B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
29 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function Method: Hierarchical Compute all permutations Memory intensive K-means (fast K-means) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
30 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function Method: Hierarchical Compute all permutations Memory intensive K-means (fast K-means) Fast convergence B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
31 Clustering Method Modelling Process Clustering Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Not pass Non-iid Hosts Generate a few clusters based on availability distribution function Method: Hierarchical Compute all permutations Memory intensive K-means (fast K-means) Fast convergence Dependent on initial centroids B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
32 Distance Metrics Modelling Process Clustering Distance between CDF of two hosts B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
33 Distance Metrics Modelling Process Clustering Distance between CDF of two hosts Kolmogorov-Smirnov: Maximum difference between two CDFs B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
34 Distance Metrics Modelling Process Clustering Distance between CDF of two hosts Kolmogorov-Smirnov: Maximum difference between two CDFs Kuiper: Maximum deviation above and below of two CDFs B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
35 Distance Metrics Modelling Process Clustering Distance between CDF of two hosts Kolmogorov-Smirnov: Maximum difference between two CDFs Kuiper: Maximum deviation above and below of two CDFs Cramer-von Mises: Area between two CDFs B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
36 Modelling Process Clustering Distance Metrics Distance between CDF of two hosts Kolmogorov-Smirnov: Maximum difference between two CDFs Kuiper: Maximum deviation above and below of two CDFs Cramer-von Mises: Area between two CDFs Anderson-Darling: Area between two CDFs, more weight on the tail B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
37 Modelling Process Clustering Distance Metrics Important Challenge: Number of samples in each CDF Few samples > not enough confidence on the result B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
38 Modelling Process Clustering Distance Metrics Important Challenge: Number of samples in each CDF Few samples > not enough confidence on the result Too much samples > the metric will be too sensitive B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
39 Modelling Process Clustering Distance Metrics Important Challenge: Number of samples in each CDF Few samples > not enough confidence on the result Too much samples > the metric will be too sensitive Data Set: different hosts have different number of samples B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
40 Modelling Process Clustering Distance Metrics Important Challenge: Number of samples in each CDF Few samples > not enough confidence on the result Too much samples > the metric will be too sensitive Data Set: different hosts have different number of samples Our solution: randomly select a fixed number of intervals from each host (i.e., 30 samples) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
41 Clustering Results Modelling Process Clustering Dendrogram of hierarchical clustering: 5-10 distinct groups (bootstrap) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
42 Clustering Results Modelling Process Clustering Comparison of distances in clusters (k-means for all iid hosts): B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
43 EDF of clusters Modelling Process Clustering B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
44 Outline Modelling Process Model fitting 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
45 Methods Modelling Process Model fitting Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Model Fitting Not pass Non-iid Hosts B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
46 Modelling Process Model fitting Methods Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Model Fitting Not pass Non-iid Hosts Method: Maximum Likelihood Estimation (MLE) Moment Matching (MM) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
47 Modelling Process Model fitting Methods Data Set Remove Outliers Randomness Tests Pass iid Hosts Clustering Model Fitting Not pass Non-iid Hosts Method: Maximum Likelihood Estimation (MLE) Moment Matching (MM) Target Distributions: Exponential Pareto Weibull Log-normal Gamma B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
48 Graphical Test Modelling Process Model fitting PP-plots: Exponential, Pareto, Weibull, Log-normal, Gamma Cluster 1: B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
49 Graphical Test Modelling Process Model fitting PP-plots: Exponential, Pareto, Weibull, Log-normal, Gamma Cluster 1: Cluster 2: B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
50 Graphical Test Modelling Process Model fitting PP-plots: Exponential, Pareto, Weibull, Log-normal, Gamma Cluster 1: Cluster 2: Cluster 3: Cluster 4: Cluster 5: Cluster 6: B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
51 Modelling Process Model fitting Goodness Of Fit Tests Generate p-values by two GOF tests (average over 1000 runs): Kolmogorov-Smirnov (KS) test Anderson-Darling (AD) test B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
52 Modelling Process Model fitting Goodness Of Fit Tests Generate p-values by two GOF tests (average over 1000 runs): Kolmogorov-Smirnov (KS) test Anderson-Darling (AD) test Exponential Pareto Weibull Log-Normal Gamma Data sets AD KS AD KS AD KS AD KS AD KS All iid hosts Cluster Cluster Cluster Cluster Cluster Cluster B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
53 Modelling Process Model fitting Some properties of clusters Clusters # of hosts % of total avail. mean (hrs) Best fit Parameters shape scale All iid hosts Weibull Cluster Gamma Cluster Gamma Cluster Log-Normal Cluster Gamma Cluster Gamma Cluster Weibull Cluster sizes are different and often significant B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
54 Modelling Process Model fitting Some properties of clusters Clusters # of hosts % of total avail. mean (hrs) Best fit Parameters shape scale All iid hosts Weibull Cluster Gamma Cluster Gamma Cluster Log-Normal Cluster Gamma Cluster Gamma Cluster Weibull Cluster sizes are different and often significant Heterogeneity in distribution parameters (different scale parameters) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
55 Modelling Process Model fitting Some properties of clusters Clusters # of hosts % of total avail. mean (hrs) Best fit Parameters shape scale All iid hosts Weibull Cluster Gamma Cluster Gamma Cluster Log-Normal Cluster Gamma Cluster Gamma Cluster Weibull Cluster sizes are different and often significant Heterogeneity in distribution parameters (different scale parameters) Decreasing hazard rate B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
56 Outline Discussions Significance of Clustering Criteria 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
57 Discussions Significance of Clustering Criteria Significance of Clustering Criteria Could the same clusters have been found using some other static criteria? B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
58 Discussions Significance of Clustering Criteria Significance of Clustering Criteria Could the same clusters have been found using some other static criteria? Cluster by venue: Work, Home, School Cluster by Time zone: 6 different time zones B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
59 Discussions Significance of Clustering Criteria Significance of Clustering Criteria Could the same clusters have been found using some other static criteria? Cluster by venue: Work, Home, School Cluster by Time zone: 6 different time zones 10 5 Expected number per cluster Venue Timezone Reference Actual number per cluster B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
60 Discussions Significance of Clustering Criteria Significance of Clustering Criteria Could the same clusters have been found using some other static criteria? Cluster by venue: Work, Home, School Cluster by Time zone: 6 different time zones Cluster by CPU speed 6 x CPU speed (FLOPS) Cluster index B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
61 Outline Discussions Scheduling Implications 1 Introduction and Motivation 2 Measurement Remove outliers 3 Modelling Process Randomness Tests Clustering Model fitting 4 Discussions Significance of Clustering Criteria Scheduling Implications 5 Related Work 6 Conclusion and Future Work B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
62 Scheduling Implications Discussions Scheduling Implications Scheduling accuracy Global model vs. Individual cluster model B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
63 Scheduling Implications Discussions Scheduling Implications Scheduling accuracy Global model vs. Individual cluster model Ex: Completion probability of a 24-hour task: B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
64 Discussions Scheduling Implications Scheduling Implications Scheduling accuracy Global model vs. Individual cluster model Ex: Completion probability of a 24-hour task: Global model: <20% Cluster 4: 70% B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
65 Discussions Scheduling Implications Scheduling Implications Scheduling accuracy Global model vs. Individual cluster model Ex: Completion probability of a 24-hour task: Global model: <20% Cluster 4: 70% Resource Selection/Replication B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
66 Discussions Scheduling Implications Scheduling Implications Scheduling accuracy Global model vs. Individual cluster model Ex: Completion probability of a 24-hour task: Global model: <20% Cluster 4: 70% Resource Selection/Replication Single job: Prediction of task failure B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
67 Discussions Scheduling Implications Scheduling Implications Scheduling accuracy Global model vs. Individual cluster model Ex: Completion probability of a 24-hour task: Global model: <20% Cluster 4: 70% Resource Selection/Replication Single job: Prediction of task failure Multi-job: How the task size distribution follows the availability distribution B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
68 Related Work Related Work Different from other research Measurement B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
69 Related Work Related Work Different from other research Measurement Resource type: home, work, and school B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
70 Related Work Related Work Different from other research Measurement Resource type: home, work, and school Scale: 200,000 hosts B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
71 Related Work Related Work Different from other research Measurement Resource type: home, work, and school Scale: 200,000 hosts Duration: 1.5 years B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
72 Related Work Related Work Different from other research Measurement Resource type: home, work, and school Scale: 200,000 hosts Duration: 1.5 years Availability : CPU availability B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
73 Related Work Related Work Different from other research Measurement Resource type: home, work, and school Scale: 200,000 hosts Duration: 1.5 years Availability : CPU availability Modelling B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
74 Related Work Related Work Different from other research Measurement Resource type: home, work, and school Scale: 200,000 hosts Duration: 1.5 years Availability : CPU availability Modelling Classification according to randomness tests B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
75 Related Work Related Work Different from other research Measurement Resource type: home, work, and school Scale: 200,000 hosts Duration: 1.5 years Availability : CPU availability Modelling Classification according to randomness tests Cluster-based Model vs Global Model B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
76 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
77 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
78 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
79 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
80 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) Partitioning hosts into subsets by their availability distribution B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
81 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) Partitioning hosts into subsets by their availability distribution Modelling (Apply the methodology for the SETI@home) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
82 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) Partitioning hosts into subsets by their availability distribution Modelling (Apply the methodology for the SETI@home) 34% of hosts have truly random availability intervals B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
83 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) Partitioning hosts into subsets by their availability distribution Modelling (Apply the methodology for the SETI@home) 34% of hosts have truly random availability intervals Six clusters with three different distributions: Gamma, Weibull, and Log-normal B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
84 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) Partitioning hosts into subsets by their availability distribution Modelling (Apply the methodology for the SETI@home) 34% of hosts have truly random availability intervals Six clusters with three different distributions: Gamma, Weibull, and Log-normal B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
85 Conclusion and Future Work Conclusion and Future Work Discovering availability models for host subsets from a distributed system Conclusion Methodology Remove outliers Classification based on the randomness tests (iid vs non-iid) Partitioning hosts into subsets by their availability distribution Modelling (Apply the methodology for the SETI@home) Future Work 34% of hosts have truly random availability intervals Six clusters with three different distributions: Gamma, Weibull, and Log-normal Apply the result for improving makespan of DAG-applications Explore ability of clustering dynamically while the system is on-line B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
86 Conclusion and Future Work Failure Trace Archive Repository of availability traces of parallel and distributed systems, and tools for analysis Facilitate design, validation and comparison of fault-tolerance algorithms and models 15 data sets including data set B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
87 Conclusion and Future Work Failure Trace Archive Repository of availability traces of parallel and distributed systems, and tools for analysis Facilitate design, validation and comparison of fault-tolerance algorithms and models 15 data sets including data set More Details Poster Session at MASCOTS 2009 (Today 19:00-21:00) Website: B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
88 Conclusion and Future Work Thank You Questions? B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
89 Distance Metrics Conclusion and Future Work Distance between CDF of two hosts B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
90 Distance Metrics Conclusion and Future Work Distance between CDF of two hosts Kolmogorov-Smirnov: D n,m = sup F n(x) G m(x) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
91 Distance Metrics Conclusion and Future Work Distance between CDF of two hosts Kolmogorov-Smirnov: D n,m = sup F n(x) G m(x) Kuiper: V n,m = sup F n(x) G m(x) +sup G m(x) F n(x) B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
92 Distance Metrics Conclusion and Future Work Distance between CDF of two hosts Kolmogorov-Smirnov: D n,m = sup F n(x) G m(x) Kuiper: V n,m = sup F n(x) G m(x) +sup G m(x) F n(x) Cramer-von8 Mises: 9 T n,m = nm (n+m) 2 < nx mx = [F n(x i ) G m(x i )] 2 + [F n(y j ) G m(y j )] 2 : ; i=1 j=1 B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
93 Distance Metrics Conclusion and Future Work Distance between CDF of two hosts Kolmogorov-Smirnov: D n,m = sup F n(x) G m(x) Kuiper: V n,m = sup F n(x) G m(x) +sup G m(x) F n(x) Cramer-von8 Mises: 9 T n,m = nm (n+m) 2 < nx mx = [F n(x i ) G m(x i )] 2 + [F n(y j ) G m(y j )] 2 : ; i=1 Anderson-Darling: Q n = R [F(x) Fn(x)]2 ψ(f(x))df ψ(f(x)) = 1 F(x)(1 F (x)) j=1 B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
94 Conclusion and Future Work Fitting with Hyper-Exponential Fitting Method: Expectation Maximization (EM) [using EMpht package] Accurate Flexible Slow B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
95 Conclusion and Future Work Fitting with Hyper-Exponential Fitting Method: Expectation Maximization (EM) [using EMpht package] Accurate Flexible Slow Moment Matching (MM) Less accurate Not flexible Very fast B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
96 Conclusion and Future Work Fitting with Hyper-Exponential Fitting Method: Expectation Maximization (EM) [using EMpht package] Accurate Flexible Slow Moment Matching (MM) Less accurate Not flexible Very fast We used MM for 2-phase hyper-exponential by the first two moments as follows: p = 1 2 (1 CV 2 1 CV 2 +1 ) λ 1 = 2p µ λ 2 = 2(1 p) µ B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
97 PP-Plots Conclusion and Future Work (a) Cluster 1 (b) Cluster 2 (c) Cluster 3 (d) Cluster 4 (e) Cluster 5 (f) Cluster 6 B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
98 Conclusion and Future Work Goodness of Fit Tests Hyper-Exponential (MM) Hyper-Exponential (EM) Data sets Parameters AD KS Parameters AD KS All iid hosts p 1 = λ 1 = p 2 = λ 2 = Cluster 1 p 1 = λ 1 = p 2 = λ 2 = Cluster 2 p 1 = λ 1 = p 2 = λ 2 = Cluster 3 p 1 = λ 1 = p 2 = λ 2 = Cluster 4 p 1 = λ 1 = p 2 = λ 2 = Cluster 5 p 1 = λ 1 = p 1 = λ 2 = Cluster 6 p 1 = λ 1 = p 2 = λ 2 = p 1 = λ 1 = p 2 = λ 2 = p 3 = λ 3 = p 1 = λ 1 = p 2 = λ 2 = p 1 = λ 1 = p 2 = λ 2 = p 1 = λ 1 = p 2 = λ 2 = p 3 = λ 3 = p 1 = λ 1 = p 2 = λ 2 = p 1 = λ 1 = p 2 = λ 2 = p 1 = λ 1 = p 2 = λ 2 = p 3 = λ 3 = B. Javadi (INRIA) Statistical Models of Availability MASCOTS / 34
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