Enabling Grids for E-scienc E Design of an

Enabling Grids for E-scienc. E Design of an Expert System for Enhancing Grid Fault Detection based on Grid Monitoring Data Gerhild Maier March 2 nd 2008 www. eu-egee. org EGEE-III INFSO-RI-222667 EGEE and g. Lite are registered trademarks

Outline Enabling Grids for E-scienc. E problem description approach • association rule mining • design of an expert system current status, example outlook and summary EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 2

Problem Description Enabling Grids for E-scienc. E Dashboard database: a lot of information about jobs Dashboard monitoring tools: find faulty Grid components exit codes detect error source underneath the exit codes fast, to solve problems quickly automatization EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 3

Approach Enabling Grids for E-scienc. E combine machine created knowledge with human knowledge to an expert system Association Rule Mining on Monitoring Data (machine created knowledge) Human Knowledge (the rule interpretation) Expert System EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 4

Association Rule Mining (1/2) Enabling Grids for E-scienc. E RULE {user=AB, ce=red. unl. edu} antecedent QUALITY {ERROR=8001} (0. 367/100. 000/11. 330) consequent (s%/c%/lift) rule: set of items item: attribute-value pair support: s% of the data includes all items confidence: c% of the data including the antecedent also include the consequent lift: measurement of interestingness lift = support(AB)/(support(A)*support(B)) EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 5

Association Rule Mining (2/2) Enabling Grids for E-scienc. E Apriori Algorithm Pruning the rules rule 1 rule 2 item set 1 item set 2 rule 1 rule 2 … rule k . . . … rule n item set n Find frequent item set Remove not interesting rules Create association rules Job Monitoring Information of the Dashboard Database Set of association rules EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Set of association rules Gerhild Maier 6

Expert System (1/2) Enabling Grids for E-scienc. E a program solving problems like an expert example: decision support system to detect a problem • • . . . Did you plug in the printer? → yes Did you install a driver? → no. . . 2 components: 1. knowledge base: collection of human expert knowledge in a problem domain 2. inference engine: defines how to use the knowledge EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 7

Expert System (2/2) Enabling Grids for E-scienc. E building the ES using the ES maintaining the ES EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 8

QAOES - Input Enabling Grids for E-scienc. E QAOES = Quick Analysis Of Error Sources time range: last 12 hours, last 24 hours support: – minimum number of jobs – low number → many rules → long runtime confidence: – significance of the rule – high percentage → good rules background: 8 job attributes • • site, ce, queue, worker node dataset user, application exit code EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 9

QAOES - Output Enabling Grids for E-scienc. E list of rules, with quality measures: support, confidence, lift association rules: • interesting dependencies of job attributes • unusual patterns in the dataset link to dashboard job summary page example: • • • CMS analysis jobs from 12 hours: 30761 min 100 jobs => support = 0. 26 % confidence: 90% runtime: 5 min number of rules: 7 EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 10

QAOES - Output Enabling Grids for E-scienc. E EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 11

Output Verification (1/2) Enabling Grids for E-scienc. E one user has problems on different sites EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 12

Output Verification (2/2) Enabling Grids for E-scienc. E one user has problems with different datasets EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 13

Output Interpretation Enabling Grids for E-scienc. E user has problems on different site, with different datasets → problem in his code? exit code 60 xxx → stage out problem → problem with the storage element? … … collection of rule interpretations rule generalization input for the knowledge base EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 14

Outlook Enabling Grids for E-scienc. E continuous adaptation of the association rule mining parameters building the knowledge base development of the inference engine EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 15

Summary Enabling Grids for E-scienc. E building the Expert System Association Rule mining completed collecting Human Knowledge web interface currently deployed for analysing CMS analysis jobs QAOES easy to adapt to different VOs job data EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 16

Links and References Enabling Grids for E-scienc. E QAOES: http: //dashb-cms-mining-devel. cern. ch/dashboard/request. py/rules Twiki: https: //twiki. cern. ch/twiki//bin/view/Arda. Grid/Automatic. Fault. Detection Association Rule Mining: article: Mining Association Rules between Sets of Items in Large Databases, Agrawal R, Imielinski T, Swami AN. Pruning Association Rules: article: Efficient Statistical Pruning of Association Rules, Alan Ableson, Janice Glasgow Expert Systems: book: Introduction to Expert Systems, Peter Jackson EGEE-III INFSO-RI-222667 Mining Job Monitoring Data Gerhild Maier 17