1. Software Performance Engineering Failure Modes and Effects Analysis Presented by Kevin Mobley

2. SPE FMEA Agenda Definition What is the problem solved with a SPE FMEA SPE FMEA anatomy –Risk: anti-pattern assessment –Likelihood of occurrence: frequency analysis –Voice of the customer: willingness to wait –Detection: control plan SPE FMEA life cycle with examples SPE FMEA perquisites and tools

3. Modeling Problem/Goal/Scope Problem – In order to simulate the day to day and peak day operations of an application, an accurate identification of the most critical business process is required Goal – Define the top 20% business processes that create 80% of the server requests, as well as outlier business processes that are severe performance risks In Scope – Definition of business processes, Anti- Pattern analysis, business frequency, willingness to wait and detection review Resource Plan Business Analyst Development Architect and Leads Performance Architects Business Case Ensures performance engineering focuses on the most critical user activity that will impact the application and system performance Sources of Financial Benefits: Ensures investment in performance delivers the operational readiness of application First Year Annualized Benefits: Establishes a traceable and defensible methodology of how and why business processes were and were not included in the performance engineering effort. The confidence in this BP set will be tested during the first year of production Milestones Gather the business processes used currently by client(s) Rank anti-patterns for each business process Analyze historical business process usage Complete willingness to wait with client (implementations only) Complete detection section Review business process selection with stakeholders Sign off on business processes

4. SPE FMEA is a structured approach Identifies ways in which software can fail to meet critical performance requirements (response time, CPU and network utilization, etc.) Estimates the risk of an unanticipated performance failure Evaluates the current control plan for identifying and/or preventing these performance failures from occurring Prioritizes actions that should be taken to improve the software

5. SPE FMEA Anatomy Risk: Anti-Pattern Assessment Likelihood of Occurrence: Frequency Analysis Voice of the Customer: Willingness to Wait Detection: Control Plan Business process Description Key risks RiskFrequency Willingness to wait Detection Phase 1 Rank Priority Number (RPN)

6. SPE FMEA – Risk Section TP Anti-Patterns Customization to Core Product Parse Cycles % of Returned Data Used RT - Browser to Web Server DB Inserts 1 - used out of box1 - no parse cycles1 - 100% 1 - 1 or less round trips 1 - 1 or less inserts 2 - minor3 - one parse cycle2 - 90%2 - 2 round trips2 - 2 inserts 5 - modest 10 - 2 or more parse cycles 3 - 80%5 - 3 round trips3 - 3 inserts 9 - substantial5 - 70%8 - 4 round trips4 - 4 inserts 10 - new functionality7 - 60% 10 - 5 or more round trips 5 - 5 inserts 8 - 50%6 - 6 inserts 9 - 40%7 - 7 inserts 10 - 30% or less8 - 8 inserts 9 - 9 inserts 10 - 10 or more inserts

7. SPE FMEA – Risk Section TP Anti-Patterns Content/Message Size % Sort Tier Debug Configuration RT ES to DBDB Reads 1 - 95 - 100%1 - no sort 1 - full debug admin 1 - 5 or less round trips 1 - 1 or less reads 2 - 90 - 95%2 - client sort 3 - limited debug admin 3 - 10 round trips 2 - up to 10 reads 3 - 85 - 90%3 - database sort 8 - no debug admin 7 - 15 round trips 3 - up to 20 reads 4 - 80 - 85% 6 - application layer sort 10 - no debug data 10 - 20 or more round trips 4 - up to 30 reads 5 - 70 - 80%10 - web server sort 5 - up to 50 reads 6 - 60 - 70% 7 - up to 100 reads 7 - 50 - 60% 10 - up to 500 reads 8 - 40 - 50% 9 - 30 - 40% 10 - 30% or less

8. SPE FMEA – Risk Section TP Anti-Patterns Message SizeCache Hit RatioRT ES to HostDB Updates 1 - small -- < = 5K1 - no cache used 1 - 3 or less round trips 1 - 1 or less updates 3 - average -- < = 20k1 - 100% 5 - 5 or less round trips 2 - 2 updates 5 - high -- > 20k and <= 50K 2 - 90% 10 - 6 or more round trips 3 - 3 updates 8 - very high -- > 50k and <= 100K 3 - 80%4 - 4 updates 10 - extreme -- > 100k4 - 70%5 - 5 updates 5 - 60%6 - 6 updates 6 - 50%7 - 7 updates 7 - 40%8 - 8 updates 8 - 30%9 - 9 updates 9 - 20%10 - 10 updates 10 - 10% or less

9. SPE FMEA – Risk Section TP Anti-Patterns Bandwidth ImpactsXSL TransformationRT WS to ES 1 - 0% empty XML tags1 - 0 transformations1 - 5 or less round trips 2 - 5% empty XML tags3 - 1 transformation3 - 10 round trips 5 - 10% Empty XML tags10 - 2 or more transformations7 - 15 round trips 8 - 15% empty XML tags10 - 20 or more round trips 10 -20% or greater empty XML tags

10. Detection with a Zero Wait DetectionCriteria: Likelihood the existence of a defect will be detected by a "test* content before software advances to next life cycle phase 1 - Fully covered by previous SPE SPE has already analyzed the business process in its current software and implementation state. 7 - Increased usageBusiness process will be used at least 20% more than in previous SPE analysis 8 - Infrastructure differs from previous SPE Effort SPE has analyzed the business process with different infrastructure 10 - SPE has never analyzed business process SPE has never analyzed business process

11. Detection with a Two Month Wait DetectionCriteria: Likelihood the existence of a defect will be detected by a "test* content before software advances to next life cycle phase 1 - Dashboard CoversDashboard as is will detect and report performance problem, and monitoring team knows how to detect problem 5 - Dashboard Admin Training Update Required Dashboard as is will detect and report performance problem, however monitoring team requires detection training 6 - Dashboard Admin Change Required Dashboard with administration updates will detect and report performance problem. Note, monitoring team will require detection training

12. Detection with a Six Month Wait DetectionCriteria: Likelihood the existence of a defect will be detected by a "test* content before software advances to next life cycle phase 1 - Dashboard CoversDashboard as is will detect and report performance problem, and monitoring team knows how to detect problem 2 - Dashboard Admin Training Update Required Dashboard as is will detect and report performance problem, however monitoring team requires detection training 3 - Dashboard Admin Change Required Dashboard with administration updates will detect and report performance problem. Note, monitoring team will require detection training 5 - Dashboard Code Modification Required Dashboard with customization will detect and report performance problem. Note, monitoring team will require detection training.

13. SPE FMEA Life Cycle SPE FMEA during the design phase SPE FMEA during software development SPE FMEA during performance testing and optimization SPE FMEA during production

14. SPE FMEA During Design Guessing is okay as long as the process is structured and consistent Review code and interview application developers to complete risk section Survey clients and product management for frequency data Advantages –Huge opportunity for redesign with smaller software budget impact –Localize the concept of performance into architecture design and coding decisions –Establish early how the non-functional requirements will be assessed Challenges –Stakeholders have lower confidence in SPE FMEA –High resistance by developers because most have never thought of the Anti-patterns during design –Non-functional requirements are not standard in software development

15. Risk Example Message size is 24 kb – Risk Rank is 5 Content to Message Ratio is 11.2% -- Risk Rank is 10 A snippet of XML Message 0.00

16. SPE FMEA During Development Measure data values for anti-patterns Make code changes Each developer delivers an Anti-Pattern spec sheets with checked in code Advantages –Greatest breath of analysis of software code –Product SPE FMEA is more comprehensive with each release Challenges –Architecture changes are harder –Impact to software budget increases

17. Risk Example We wrote a bridge parse tool Message Name: SMValidateUser (REQUEST) Total Tags: 5 Total Empty Tags: 0 Content/Message Size Ratio: 15.13% Empty Tag/Total Tag Ratio: 0.00%

18. SPE FMEA During Performance Testing and Optimization Demonstrate correlation between Anti-patterns and performance metrics Key driver for focus of the testing and optimization effort Advantages –High risk areas are focus of performance testing and optimization, assuring the performance of most important business processes –Creates a paper trail of why certain business process were focused on and others were left out –Forces a review of performance monitoring solution to determine if adequate and timely detection of poor performance with second and third tier business processes is in place –Statistical analysis between performance and anti-patterns is available Challenges –Early generations of an application SPE FMEA will be less accurate, causing some business processes to be improperly categorized –Anti-pattern weights may require adjusting

19. Risk Example Relationship between response size and response time One-way ANOVA: Time versus Response Size S = 2243 R-Sq = 38.24% R-Sq(adj) = 36.96% SourceDFSSMSFP Response Size10149815853814981585429.790 Error48124191904905029502 Total4913917349029

20. SPE FMEA Perquisites and Tools Develop application anti-patterns –Put system under load –Create a cause and effect model –Assign initial risk values Collect and maintain usage data from clients Create parse tools to use during unit and performance testing

21. Thank You Kevin Mobley kevin.mobley@gmail.com kevin.mobley@fnf.com