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

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

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

13. JARM Measurement Points
Auto-ID Cockpit
Auto-ID Node DC BE
IDoc
XML
Message
Dispatcher
Activities
XML
Integration Layer (XI)
Communication Layer
Communication Layer
XML TG
IDoc BE
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. Different message sizes
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.