Lazarevic, A.

Sacks, L.

English

UCL Discovery

Lightweight Scheduling  Lightweight Scheduling for Grid Applications for Grid ApplicationsA.  A. Lazarevic Lazarevic and L. Sacks and L. SacksUniversity College London  A. LazarevićOutline Outline▪ Motivation▪ Methodology▪ Observed user behaviour▪ Prediction Strategy▪ ConclusionsUniversity College London  A. LazarevićBetter Grid Scheduling Better Grid Scheduling▪ Deadline based▪ Transparent to the user▪ (mostly) Self-managing▪ More effective▫ Out-of-Order execution▫ Priority replaced with confidence levelUniversity College London  A. LazarevićIf only If only… …▪ We knew…▫ Process’ execution time▫ Resource utilisation▫ Likelihood of a similar process arriving▫ Our confidence in these numbers▪ We could…▫ Be flexible & robust▫ Treat anomalies in the same way as reg data▫ Adapt to change in circumstance & prediction qualityUniversity College London  A. LazarevićMethodology MethodologyHighly GranularResource MonitoringSchedule performance monitoring & feedbackConfidence level and modelimprovementsUser-OrientatedDeadline Based SchedulerOne-step-aheadpredictionsStatistical Analysis:Job Arrival RateResource UtilisationExecution TimeSocial FactorsUniversity College London  A. LazarevićMethodology MethodologyTemporal AnalysisException HandlingStatistical Models:Linear/PolynomialTime-series ARIMAWavelets ...ForecastsQuality Control:Prediction ErrorsClass Temporal PropertiesClass Stat. PropertiesVariable Time HorizonDeadline Based Scheduler(Out-of-order Execution,Statistical Multiplexing)Raw Historical Data(Accounting, Resource Monitoring)1 2 3 4 n1…n Classes of Jobs1 2 3 4 n1…n Classes of ModelsOther n-DimensionalExploratory TechniquesMeta-Data Clustering(User / Group / Executable / Time of Day / Day of Week)University College London  A. LazarevićMATLAB Framework MATLAB FrameworkData Manipulation:Cleaning, Sorting, Meta-data extractionRaw Logs, Accounting FilesData Analysis & Preparation:Trends, Seasonality, Outliers, Smoothing, DifferencingForecasting:Method selection, Model Building, PredictingForecast Monitoring & QA:Residuals Analysis, Prediction Error Analysis,Prediction Variance & Confidence LevelAnomaly DetectionVisualisationUniversity College London  A. LazarevićPreliminary Data Analysis Preliminary Data Analysis▪ Data Source:▫ 200 CPU Sun Grid Engine cluster @ UCL▪ User community:▫ 20 UK e-Science projects▫ 5-10 different projects concurrently running▪ Scope:▫ 50,000 jobs (>5,500 CPU days)▫ Jul 2004 – Dec 2004University College London  A. LazarevićTrivial Approach  Trivial Approach – – All In All InAll Jobs (42,863)0 5000 10000 15000 20000 25000 30000 35000 40000 45000Job_ID0.55.050.0500.05000.050000.05E55E6WClockUniversity College London  A. LazarevićTemporal Job Selection Temporal Job SelectionWall Clock Execution Times - 1000 jobs38600 38800 39000 39200 39400 39600Job ID50500500050000Execution Time [s]1000 Jobs, 15 hours of total execution time, used in all following analysesUniversity College London  A. LazarevićIntra Intra- -Group Analysis  Group Analysis – – ocotir ocotirExecution Time - ocotir Group386243862638628386303863238634386363863838640386423864438646Job_ID050001000015000200002500030000350004000045000Execution Time [s]Execution Time Histogram - ocotir Group510020050020005000100002000030000Execution Time [s]0123456No of JobsTotal No: 19University College London  A. LazarevićIntra Intra- -Group Analysis  Group Analysis – – matsim matsimExecution Time - matsim Group38600 38800 39000 39200 39400 39600 39800Job_ID0100200300400500600700800900Execution Time [s]Execution Time Histogram - matsim Group71 124 176 229 281 334 386 439 492 544 597 649 702 754Execution Time [s]020406080100120140160No of JobsTotal No: 933University College London  A. LazarevićIntra Intra- -Group Analysis  Group Analysis – – geogrs geogrsExecution Time Histogram - geogrs Group82000860009000094000980001.02E51.06E51.1E51.14E5Execution Time [s]02468101214No of JobsTotal No: 48Execution Time - geogrs Group39120 39130 39140 39150 39160 39170 39180Job_ID0200004000060000800001E51.2E5Execution Time [s]University College London  A. LazarevićTemporal Locality? Temporal Locality?geogrs Group - All Jobs (2,802)Job_ID0.55.050.0500.05000.050000.0WClockUniversity College London  A. LazarevićAnomalies Anomalies▪ Detect sudden, significant and sustained changes in the quality of predictions▪ Detecting them▫ Using a self-tuneable system▫ Flexible definition of an anomaly▪ Dealing with them▫ Shorten the prediction horizon▫ Reduce the confidence intervalUniversity College London  A. LazarevićAnomaly Detection  Anomaly Detection – – Exp  Exp Smoothing SmoothingLine Plot (Spreadsheet11 11v*933c)0-.85 Diff0-.25 Diff -100-50050100150200250300350Line Plot (Spreadsheet11 11v*933c) WClock(L)0-.85 Diff(R) 0100200300400500600700800900-20020406080100120University College London  A. LazarevićPrediction Methods Prediction Methods▪ Trend prediction (linear, polynomial…)▪ Time-series▫ Exponential Smoothing (Holt, SES, etc)▫ Autoregressive & Moving Average▫ ARIMA (Box-Jenkins)▪ Bayesian Statistics▪ Wavelets▪ “All models are wrong, but some are useful”George E.P. Box, Professor EmeritiusUniversity College London  A. LazarevićForecasting ForecastingExecution Time - matsim Group38600 38800 39000 39200 39400 39600 39800Job_ID0100200300400500600700800900Execution Time [s]matsim Selection of JobsCase 1Case 21Case 41Case 61Case 81Case 101Case 121Case 141Case 161Case 181Case 201Case 221Case 241Case 261Case 281 0100200300400500600700800WClock▪ Forecasting method analysis done on a subset of matsimjobs (highlighted)University College London  A. LazarevićTrend Based Predictions  Trend Based Predictions – – matsim matsim0 50 100 150 200 250050100150200250300350400450500Job IDWClock Execution Time [s]Actual v. Predicted WClock Values - matsim (0:250)Predictor: 3rd order Poly FitActual ValuesPredicted ValuesUniversity College London  A. LazarevićTrend Based Predictions  Trend Based Predictions – – matsim matsim0 50 100 150 200 250-100-50050100150Job IDPercentage Error [of actual execution time error]WClock Percentage Prediction Error - matsim (0:250)Error ValueError MeanError StDUniversity College London  A. LazarevićConclusions Conclusions▪ Clear and pressing need for better Grid scheduling▪ Patterns do develop in Grid utilisation▪ These patterns vary between “groups”, and in time▪ User-independent predictions with reasonable accuracy are possible▪ Impact to the Grid utilisation, job workflow and administrative policies may be significant

Lightweight scheduling for Grid applications

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