Managing Performance Engineering in Agile Projects

ManagingPerformance Engineering in Agile Projects STC 2011 Pramod Waghmode Dr. Asheesh Choksi

Abstract Web 2.0 New Challenges Performance Space in Agile Process Traditional Performance Engineering Activities Performance Engineering Activities in Agile Exploring Opportunities Performance Measurements Findings and Recommendations ConclusionsSTC 2011 2 2

New Challenges in Web 2.0 Applications Requirement Provide improved user experience  Rich and interactive UI elements  Social interactivity: publish and share contents, events  Pull contents from several portals to provide customized pages  Using third party controls and libraries for quick implementationSTC 2011 3 3

Performance Space in Agile Process Development Characteristics  Short development Cycles  Focused on Product Backlog Performance Engineering Challenge  User stories are not available to set the Performance goals for sprints No Check leads to Poor Quality Artifacts  Hawthorne Effect*  The Broken Window theory** * http://en.wikipedia.org/wiki/Hawthorne_effectSTC 2011 ** http://www.codinghorror.com/blog/2005/06/the-broken-window-theory.html 4

Performance Engineering ActivitiesSTC 2011 http://msdn.microsoft.com/en-us/library/bb924359.aspx 5 5

Performance Engineering Activities in AgileSTC 2011 6 6

Exploring Opportunities Optimization Opportunities  Minify JavaScript  Cache static resources, set appropriate expiry  Compress datasets (XML, JSON). This needs extra CPU cycles.  Configure Etags Baseline component performance  Check the response time, throughput, latency with different loads  Check DB related requests for their responses  Monitor CPU, memory, connection pool, disk I/O  Find result set paging opportunities  Identify requests to third party components/serversSTC 2011 7 7

Case StudySTC 2011 8

Load Plan Total number of Virtual users: 20 Number of Virtual users per batch: 20 Batch Ramp up time: 10 min Think time: Normal Distribution Pattern [Application specific parameters] Number of Iterations: 15STC 2011 9 9

Page Response vs. User Load Time  CPU: 2.66 GHz dual core  RAM: 2 GB  Windows 2K3 serverSTC 2011 10 10

Web Processor Utilization vs. User Load  CPU: 2.66 GHz dual core  RAM: 2 GB  Windows 2K3 serverSTC 2011 11 11

W3wp – Private Bytes vs. User Load  CPU: 2.66 GHz dual core  RAM: 2 GB  Windows 2K3 serverSTC 2011 12 12

Findings and Recommendations The application appeared to be non scalable as the response time increases linearly with user load It was found that there were some un-optimized queries Memory leaks were detected in a particular library Further code profiling was recommended Memory Dump Analysis revealed high frequency of GC. It was observed that large size objects were getting disposed. This is being optimized.STC 2011 13 13

Conclusions Performance Testing process in Agile Methodology brings in a change in “Attitude of team” Though there are no clear performance goals in a given sprint, the system bottlenecks are constantly checked The issues are known and fixed much earlier in the dev cycle This results in a robust application as the application is benchmarked across its different features The benchmark data provides crisp pointers for capacity planning However, this does not do away the need for Load testing on Realistic Production Environment.STC 2011 14 14

Thank You! rfi@harbingergroup.comwww.harbinger-systems.com STC 2011 15

Managing Performance Engineering in Agile Projects

by Harbinger Systems on Dec 08, 2011

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Managing Performance Engineering in Agile Projects — Presentation Transcript