Performance Risk Management
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1. There are many stages in performance risk management where risk prevails including proof of concept, requirements gathering, design, execution, monitoring, and analysis. 2. During the monitoring phase, it is important to monitor the right metrics and collect all monitored data in order to properly analyze performance. 3. Two case studies are presented analyzing vmstat output during an application spike and system overload to demonstrate the importance of analysis in identifying and addressing performance risks.
Performance Risk Management
- 1. Performance RiskManagement Tech-Talk 2.0 ByViswa
- 2. Different Stages where “RISK” prevails !!!POCRequirements GatheringDesign ExecutionMonitoringAnalysisReports
- 3. POCNo good understanding of the over all architecture .Compare the suggested Tool with those of other vendors Pro’s and Con’s
- 4. Customer change his vendor..
- 5. Requirements Gathering Picking –up the right business use casesDetermining the work loadTime lines/Dead linesSimulation of Production Environment
- 6. Advance Search taking 2 minutesImport 10000 items taking 5 minutes
- 7. DesignHow quick we can adopt to the technology ?Is the data provided to test resemble “LIVE” DataOther configuration settings like Compression, Proxy, logging etcAvoid .css/gif files and extrasVery important to have validation to each and every script
- 9. ExecutionVery important aspect where engineers tend to forget the confirmation is “ Production like Environment “System Configuration of Load Generators Results settingsIn definite executions.
- 10. More number of transactions, High through put, Less response times, No sqls with > 10 secs
- 11. MonitoringVery important phase in the whole cycle.What needs to be monitored.Does it require to collect every monitored data.(Ans-YES)
- 12. AnalysisHere I would like to share an CASE STUDY Below is Vmstat and column definitions
- 13. Case Study –Application Spikeprocs memory swap io system cpur b swpd free buff cache si so bi bo in cs us sywa id4 0 200560 91656 88596 176092 0 0 0 0 103 12 0 0 0 1000 0 200560 91660 88600 176092 0 0 0 0 104 12 0 0 0 1000 0 200560 91660 88600 176092 0 0 0 0 103 16 1 0 0 990 0 200560 91660 88600 176092 0 0 0 0 103 12 0 0 0 1001 0 200560 90200 88608 176100 0 0 8 0 153 118 56 31 0 130 0 200560 88692 88612 179036 0 0 2940 0 249 249 44 4 24 282 0 200560 88708 88612 179036 0 0 0 484 254 94 39 22 1 380 0 200560 88708 88612 179036 0 0 0 0 121 22 0 0 0 1000 0 200560 88708 88612 179036 0 0 0 0 103 12 0 0 0 1000 0 200560 91660 88604 176092 0 0 0 80 108 28 0 0 6 94
- 14. Case Study 2-OverLoad Schedulerprocs memory swap io system cpur b swpd free buff cache si so bi bo in cs us sywa id2 1 207740 98476 81344 180972 0 0 2496 0 900 2883 4 12 57 270 1 207740 96448 83304 180984 0 0 1968 328 810 2559 8 9 83 00 1 207740 94404 85348 180984 0 0 2044 0 829 2879 9 6 78 70 1 207740 92576 87176 180984 0 0 1828 0 689 2088 3 9 78 102 0 207740 91300 88452 180984 0 0 1276 0 565 2182 7 6 83 43 1 207740 90124 89628 180984 0 0 1176 0 551 2219 2 7 91 04 2 207740 89240 90512 180984 0 0 880 520 443 907 22 10 67 05 3 207740 88056 91680 180984 0 0 1168 0 628 1248 12 11 77 04 2 207740 86852 92880 180984 0 0 1200 0 654 1505 6 7 87 06 1 207740 85736 93996 180984 0 0 1116 0 526 1512 5 10 85 00 1 207740 84844 94888 180984 0 0 892 0 438 1556 6 4 90 0
- 15. Reports The majority of “RISK” factor involves how effectively monitored, captured results, Analysis etc structured which resemble the good work .
- 16. Effect of poor “PERFORMANCE RISK MANAGEMENT”