Analysis and Modeling of Time-Correlated Failures in Large-Scale Distributed Systems

Transcription

1 Analysis and Modeling of Time-Correlated Failures in Large-Scale Distributed Systems Nezih Yigitbasi 1, Matthieu Gallet 2, Derrick Kondo 3, Alexandru Iosup 1, Dick Epema TUDelft, 2 École Normale Supérieure de Lyon, 3 INRIA The Failure Trace Archive Delft University of Technology Challenge the future

2 Failures Do Happen Build a computing system with 10 thousand servers with MTBF of 30 years each, watch one fail per day Jeff Dean, Google Fellow, LADIS 09 Keynote Average worker deaths per MapReduce job is 1.2 MapReduce, OSDI % failures in TeraGrid Khalili et al., GRID 06 During the month of March 2005 on one dedicated cluster with 1500 Xeon CPUs, there were 32,580 Sawzall jobs launched, using an average of 220 machines each. While running those jobs, 18,636 failures occurred (application failure, network outage, system crash, etc.) that triggered rerunning some portion of the job... Rob Pike et al., Google 2

3 Are Failures Independent? Common assumption Is this realistic for large-scale distributed systems? Already know that space correlations exist Time correlations may impact Proactive fault-tolerance solutions Design decisions Checkpointing & scheduling decisions (e.g., migrate computation at the beginning of a predicted peak) M.Gallet, N.Yigitbasi, B.Javadi, D.Kondo, A.Iosup, D.Epema, A Model for Space-correlated Failures in Large-scale Distributed Systems, Euro-Par

4 Our Goals GOAL 1 Investigate whether failures have time correlations GOAL 2 Model the time-varying behavior of failures (peaks) 4

5 Outline Background Our Approach Analysis of Time-Correlation Modeling the Peaks of Failures Conclusions 5

6 Why Not Root-Cause Analysis? Root-cause analysis is definitely useful Challenges Systems are large and complex Not all subsystems provide detailed info Little monitoring/debugging support Environment-specific or temporary failures Huge size of failure data 19 systems 6

7 Failure Trace Archive (FTA) Provides Availability traces of diverse distributed systems of different scale Standard format for failure events Tools for parsing & analysis Enables Comparing models/algorithms using identical data sets Evaluation of the generality/specificity of models/algorithms across different types of systems Analysis of availability evolution across time scales And many more The Failure Trace Archive 7

8 FTA Schema Hierarchical trace format Resource centric Event-based Associated metadata Codes for different components and events Available in raw, tabbed and MYSQL formats 8

9 Sample Trace Identifiers Type Event of for event: the start/stop event/component/node/platform Node unavailability/availability name time (UNIX time) 9

10 Outline Background Our Approach Analysis of Time-Correlation Modeling the Peaks of Failures Conclusions 10

11 Our Approach (1): Outline Traces Nineteen failure traces from the FTA Mostly production systems Analysis Use the auto-correlation of failure rate time series Modeling Fit well-known probability distributions to the failure data to model failure peaks 11

12 Our Approach (2): Traces 100K+ hosts ~1.2 M failure events 15+ years of operation in total 12

13 Our Approach (3): Analysis Auto-Correlation Function (ACF) Similarity between observations as a function of the time lag between them Mathematical tool for finding repeating patterns Used for assessing time correlations [-1 1]: weak strong correlation 13

14 Our Approach (4): Modeling We use five probability distributions to fit to the empirical data Exponential, Weibull, Pareto, Log-Normal, and Gamma Maximum likelihood estimation + Goodness of Fit Tests 14

15 Outline Background Our Approach Analysis of Time-Correlation Modeling the Peaks of Failures Conclusions 15

16 Analysis (1): Auto-correlation WEBSITES Many systems exhibit moderate/strong auto-correlation for moderate/short time lags (GRID5K, LDNS, SKYPE, ) 16

17 Analysis (2): Auto-correlation TERAGRID Small number of systems exhibit low autocorrelation (TeraGrid, PNNL, NOTRE-DAME) 17

18 Analysis (3): Failure Patterns Daily/Weekly Cycles Daily/Weekly Cycles MICROSOFT SKYPE Systems with similar usage patterns have similar failure patterns 18

19 Analysis (4): Workload Intensity vs Failure Rate GRID5000 There is a strong correlation between the workload intensity and the failure rate in some systems 19

20 Outline Background Our Approach Analysis of Time-Correlation Modeling the Peaks of Failures Conclusions 20

21 Failure Peaks (1): Model μ+kσ μ 21

22 Failure Peaks (2): Identification Our goal Balance between capturing the extreme system behavior and characterizing an important part of the system failures We use a threshold to isolate peaks μ + kσ where k is a positive integer Large k=> Few periods explaining only a small fraction of failures Small k=> More failures of probably very different characteristics We use k=1 Tried k={0.5, 0.9, 1.0, 1.1, 1.25, 1.5, 2.0} Over all traces, average fraction of downtime and average number of failures are close (see Technical Report) 22

23 Failure Peaks (3): Modeling Results (1) 1. On average, 50% - 95% of the system downtime is caused by the failures that originate during peaks, but the fraction of peaks < 10% for all platforms 2. The average peak durations are on the order of 1-2 hours 3. The average time between peaks is on the order of hours 4. Average IAT over the entire trace is about 9x the IAT during peaks 23

24 Failure Peaks (4): Modeling Results (2) 5. Exponential distribution is not a good fit for IAT during peaks, time between peaks, and failure duration during peaks Traditional models are not enough 6. Model parameters do not follow a heavy-tailed distribution Goodness of fit test results (p-values) for the Pareto distribution are very low 7. Weibull and the Log-Normal provide the best fit See the paper for the parameters 24

25 Conclusions (1) Large-Scale Study Nineteen traces most of which are production systems 100K+ hosts ~1.2 M failure events 15+ years of operation Four new traces available in the FTA (3 CONDOR + 1 TERAGRID) GOAL 1: Analysis Failures exhibit strong periodic behavior & time correlation Systems with similar usage patterns have similar failure patterns Strong correlation between workload intensity and failure rate 25

26 Conclusions (2) GOAL 2: Modeling Peak duration, time between peaks, the failure IAT during peaks, and the failure duration during peaks On average 50% - 95% of the system downtime is caused by the failures that originate during peaks (fraction of peaks < 10%) Weibull & the Log-Normal distributions provide good fit 26

27 Thank you! Questions? Comments? The Failure Trace Archive More Information: Guard-g Project: The Failure Trace Archive: PDS publication database: 27