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Evaluation of performance aspects of the Auto-ID Infrastructure

Evaluation of performance aspects of the Auto-ID Infrastructure. Kai Sachs (TU Darmstadt) Supervisors: Christof Bornhoevd (SAP) Mariano Cilia (TU Darmstadt). CONTENTS. Auto-ID Infrastructure. Measurement Approach. Results of the Experiments. Final Conclusions. Auto-ID Infrastructure.

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Evaluation of performance aspects of the Auto-ID Infrastructure

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  1. Evaluation of performance aspects of the Auto-ID Infrastructure Kai Sachs (TU Darmstadt)Supervisors: Christof Bornhoevd (SAP) Mariano Cilia (TU Darmstadt)

  2. CONTENTS Auto-ID Infrastructure Measurement Approach Results of the Experiments Final Conclusions

  3. Auto-ID Infrastructure Measurement Approach Results of the Experiments Final Conclusions

  4. AII: Overview (1) • SAP Auto-ID Infrastructure 2.0 (AII) • Middleware solution • Receiving RFID data from data capture sources (e.g. RFID devices) • Integrates the data into enterprise applications. • Early prototype

  5. XML/PML IDoc XML LLI Traffic Generator Traffic Generator AII: Overview (2) • The illustration below shows an overview of SAP RFID landscape: SAP Exchange Infrastructure (XI) Device Controller SAP Auto-ID Infrastructure (AII) SAP R/3 Reader Backend RFID Tags AII Auto-ID Cockpit(Web User Interface) From: SAP RFID Solution Package SAP Auto-ID Infrastructure 2.0 (AII) Theory

  6. Auto-ID Node System Architecture Auto-ID Cockpit Auto-ID Node DC BE IDoc Message Dispatcher Activities XML XML Integration Layer (XI) Communication Layer Communication Layer XML TG BE IDoc Rule Engine AIN Repository From: SAP Auto-ID Infrastructure

  7. CONTENTS Auto-ID Infrastructure Measurement Approach Results of the Experiments Final Conclusions

  8. Test Environment

  9. What should be observed? • Experiments settings • Multiple readers • Message size • System behavior • CPU load • IO Activities • Single processes • Memory … • Throughput • Components on the Auto – ID Infrastructure • Gross Times • Gross CPU Times Customized Traffic Generator MicrosoftPerformance Customized Traffic Generator JARM

  10. Microsoft Performance • Part of Microsoft Windows 2000 & XP • System Monitor • Allows to observe: • Single processes • IO Activities • CPU load • … • Observations could be logged in a CSV - file.

  11. JARM • Allows observation of Java components • Provides averages values and sums per component • Hierarchies of components are possible • Results are accessible through Visual Administrator • Needs source code modifications! • Problems, if JMS is used

  12. JARM Measurement Points Auto-ID Cockpit Auto-ID Node DC BE IDoc Message Dispatcher Activities XML XML Integration Layer (XI) Communication Layer Communication Layer XML TG BE IDoc Rule Engine AIN Repository

  13. JARM Measurement Points Auto-ID Cockpit Auto-ID Node DC BE IDoc Message Dispatcher Activities XML XML Integration Layer (XI) Communication Layer Communication Layer XML TG BE IDoc Rule Engine AIN Repository Parser Rule Processor HTTP

  14. Customized Traffic Generator • Based on SAP Traffic Generator • Used to simulate reader observations • New logging functions were added Every sent request can be logged Allows better review of throughput • Other new functions: • Add Timeframes for experiments • Send a defined number of messages • Possibility to run different scripts parallel • Scenario – Definitions • …

  15. CONTENTS Auto-ID Infrastructure Measurement approach Results of the Experiments Conclusion

  16. Results of Experiments • CPU Load • IO Activities • Throughput • J2EE Components of the Auto-ID Node • Different VM settings • Settings of Message Dispatcher

  17. Results of Experiments • CPU Load • IO Activities • Throughput • J2EE Components of the Auto-ID Node • Different VM settings • Settings of Message Dispatcher

  18. CPU Load Fall down Incursions

  19. CPU Load • Incursions and the observed fall down have heavy influence on the average CPU load • CPU load differ for the experiments • Throughput depends on CPU load • Need for a key figure for comparison of the different experiments. • 

  20. IO Activities I Savepoints of MaxDB

  21. IO Activities II Savepoints of MaxDB

  22. IO Activities III • MaxDB Savepoints have a significant influence on the system behavior. • Settings for MaxDB Savepoint intervals can be changed. • Influence of Savepoints is bigger, if the files are fragmented. • The Savepoints could not explain the CPU load fall down in the end of the experiment time frame!!!

  23. Throughput • Different message sizes • 9 EPCs per message • 45 EPCs per message • 90 EPCs per message • 900 EPCs per message • Multiple readers • 1 simulated reader • 3 simulated readers • 5 simulated readers • 7 simulated readers • 10 simulated Reader

  24. Throughput II

  25. Throughput III

  26. Throughput IV

  27. Throughput V • Conclusions: • Influence of message size: • Bigger message size Higher throughput in no. of EPCs per sec. • Influence of multiple simulated RFID readers: Throughout increases up to n reader; decreases after that • Throughput decreases over time

  28. Auto-ID Node Components

  29. Auto-ID Node Components

  30. Auto-ID Node Components II

  31. Auto-ID Node Components III

  32. Auto-ID Node Components IV • Conclusions: • Gross Times scale linear for different message sizes. • The activities are the dominating part of the Auto-ID Node. • The activities are dominated by database accesses.

  33. CONTENTS Auto-ID Infrastructure Measurement Approach Results of the Experiments Final Conclusions

  34. Final Conclusions I • CPU Load: • CPU load has short incursions • Number of simulated readers has no influence on the CPU load • Message size influences the proportions of the system processes regarding CPU load • CPU load decrease at the end of the experiment time frame • IO Activities: • MaxDB Savepoints have a significant influence on the system behavior • Throughput: • Throughput is higher for larger messages • Throughput decreases over time • Throughput depends on number of readers

  35. Final Conclusions II • Components of the Auto-ID Node: • Auto-ID Node components scale linear • Rule Activities are the dominating component • Performance of Activities is dominated by database accesses • Number of simulated readers has significant influence on the Gross Time • Settings of Java Virtual Machine: • Heap size is the most important parameter for higher throughput • JMS settings of Message Dispatcher: • Throughput is lower, if JMS is used. • Gross Time is higher, if JMS is used.

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