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

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Presentation on theme: "Evaluation of performance aspects of the Auto-ID Infrastructure Kai Sachs (TU Darmstadt) Supervisors:Christof Bornhoevd (SAP) Mariano Cilia (TU Darmstadt)"— Presentation transcript:

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

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

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

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

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

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

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 Microsoft Performance Customized Traffic Generator JARM Customized Traffic Generator

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

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

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

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 Incursions Fall down

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

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

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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