Fault analysis framework. Ana Gainaru, Franck Cappello, Bill Kramer
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1 Fault analysis framework Ana Gainaru, Franck Cappello, Bill Kramer Third Workshop of the INRIA Illinois Joint Laboratory on Petascale Computing, Bordeaux June
2 Contents Introduction Framework architecture Signal analysis module Extract signals Aperiodic events Correlations Periodic events Adaptive Filter
3 Framework overview Central database Different synchronized modules HELO (Hierarchical Event Log Organizer) extracts patterns from logs generated by the system Other modules in pipeline with HELO Have the input received by the output from HELO 11/22/10 INRIA Illinois Petascale Computing Joint Lab 3/28
4 HELO Overview Characterize events generated by large systems Extract patterns from historic log files Dynamically adapts templates as novel events appear in the system Deals with configuration changes or system updates Analyze the normal behavior for every type of signal 11/22/10 INRIA Illinois Petascale Computing Joint Lab 4/28
5 Database on the Central Webserver Central database to keep synchronized templates 11/22/10 INRIA Illinois Petascale Computing Joint Lab 5/28
6 Log files HELO offline Database on the Central Webserver Hierarchical Event Log Organizer (HELO) - extracts patterns from the log file - offline manner 11/22/10 INRIA Illinois Petascale Computing Joint Lab 6/28
7 Log files HELO offline Database on the Central Webserver Incoming events SN1 HELO online SNx For incoming events - Each service node runs HELO online independently 11/22/10 INRIA Illinois Petascale Computing Joint Lab 7/28
8 Log files HELO offline Database on the Central Webserver Incoming events SN1 HELO online SNx Each Service Node (SN) - Downloads templates from the database - Inspects each incoming event - If the new event is a match classify (parallel for each SN) 11/22/10 INRIA Illinois Petascale Computing Joint Lab 8/28
9 Log files HELO offline Database on the Central Webserver Incoming events SN1 HELO online SNx For a new event type - SN - Send the event to the database - Database Updates the templates - SN - Receive the new template list 11/22/10 INRIA Illinois Petascale Computing Joint Lab 9/28
10 Analysis modules Different modules can be added in the framework Synchronized by the database In a pipeline manner Each time a SN modifies the database The modules update their data 11/22/10 INRIA Illinois Petascale Computing Joint Lab 10/28
11 The system generates signals Daemons Monitoring information Errors generate noise Non periodic Influence other events Module aim Signal analysis Extract the normal behavior of a system How error change the shape / propagate in the system 11/22/10 INRIA Illinois Petascale Computing Joint Lab 11/28
12 Nyquist theorem Extracting the signal If a function x(t) contains no frequencies higher than B hertz, it is completely determined by giving its ordinates at a series of points spaced 1/(2B) seconds apart. Sampling rate Respecting Nyquist theorem Make computations fast but with a small delay 11/22/10 INRIA Illinois Petascale Computing Joint Lab 12/28
13 Extracting the signal Algorithm Extract the mean time between two events from the same type Errors could create more messages from one type Errors could make some messages dissapear Start with the mean time until max time Use autocorrelation function for time sample of t/2 until we get a periodic signal 11/22/10 INRIA Illinois Petascale Computing Joint Lab 13/28
14 Autocorrelation Cross correlation A measure of similarity of two waveforms as a function of a time lag applied to one of them Autocorrelation Cross correlation of a signal with itself Thresholds R coefficient of correlation between 1 and 1 Testing for the significance of the correlation coefficient 11/22/10 INRIA Illinois Petascale Computing Joint Lab 14/28
15 Results 50% periodic signals For all periodic signals the sampling rate was correct Using the constant threshold 10% of the periodic signals are not found Using adaptive threshold 30% of the found signals are not periodic Non periodic events are detected in the filtering stage 11/22/10 INRIA Illinois Petascale Computing Joint Lab 15/28
16 Aperiodic signals Time window of 1 sec offline analysis Correlations Similarity value the same as autocorrelation Extract the time difference between signals Graph of correlated events 11/22/10 INRIA Illinois Petascale Computing Joint Lab 16/28
17 INFO starting systemcontroller * Example WARNING endserviceaction is restarting the nodecards in midplane * as part of service action d+ WARNING node card is not fully functional SEVERE node card vpd check: * node in processor card slot * do not match. vpd ecid d+ found d+ ERROR can not get assembly information for node card SEVERE link card power module * is not accessible FAILURE no power module * found found on link card FAILURE temperature Over Limit on link card 11/22/10 INRIA Illinois Petascale Computing Joint Lab 17/28
18 Period signals Periodogram If a time series has a strong sinusoidal signal for some frequency, then there will be a peak in the periodogram at that frequency 11/22/10 INRIA Illinois Petascale Computing Joint Lab 18/28
19 Filtering signals In case of errors 11/22/10 INRIA Illinois Petascale Computing Joint Lab 19/28
20 Filtering signals Algorithm The frequencies that are prominent Iterative Find the horizontal line that divides the frequencies values into equal sets Eliminate all under the middle + 2 standard deviation Only keep 5% of all values If nothing changes in two iterative steps > stop Local maximum frequency 11/22/10 INRIA Illinois Petascale Computing Joint Lab 20/28
21 Filtering signals 11/22/10 INRIA Illinois Petascale Computing Joint Lab 21/28
22 Filtering signals 11/22/10 INRIA Illinois Petascale Computing Joint Lab 22/28
23 Filtering signals 11/22/10 INRIA Illinois Petascale Computing Joint Lab 23/28
24 Results If the noise is: 5 times more than the signal new frequencies Analysis: 3 weeks without errors Extracted the right signal 100% correct One month with errors 10 periodic signals 2 for which the noise error was too high to find the right frequencies 11/22/10 INRIA Illinois Petascale Computing Joint Lab 24/28
25 Results 11/22/10 INRIA Illinois Petascale Computing Joint Lab 25/28
26 Results 11/22/10 INRIA Illinois Petascale Computing Joint Lab 26/28
27 Offline analysis Conclusion & Future work Extracting the normal behavior for periodic signals Correlated events Online analysis How does an error change the normal behavior Same frequencies but higher values Other frequencies appear Error propagation in the correlated chain 11/22/10 INRIA Illinois Petascale Computing Joint Lab 27/28
28 Q&A Thank you 11/22/10 Third Workshop of the INRIA Illinois INRIA Illinois Joint Laboratory Petascale on Computing Petascale Computing, Joint Lab Bordeaux June /28
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