Evaluation of performance aspects of the Auto-ID Infrastructure

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

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AII Overview (2)

• The illustration below shows an overview of SAP
  RFID landscape

SAP Exchange Infrastructure (XI)
Device Controller
SAP R/3
SAP Auto-ID Infrastructure (AII)
Reader
Backend
RFID Tags
AII
Auto-ID Cockpit(Web User Interface)
From SAP RFID Solution Package SAP Auto-ID
Infrastructure 2.0 (AII) Theory
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Auto-ID Node System Architecture

Auto-ID Cockpit
Auto-ID Node
DC
BE
IDoc
Activities
Message Dispatcher
XML
XML
Integration Layer (XI)
Communication Layer
Communication Layer
XML
TG
BE
IDoc
Rule Engine
AIN Repository
From SAP Auto-ID Infrastructure
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CONTENTS
Auto-ID Infrastructure
Measurement Approach
Results of the Experiments
Final Conclusions
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Test Environment
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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
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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.

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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

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JARM Measurement Points

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

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

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

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

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Results of Experiments

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

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CPU Load
Fall down
Incursions
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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. ?
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IO Activities I
Savepoints of MaxDB
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IO Activities II
Savepoints of MaxDB
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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!!!

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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

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Throughput II
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Throughput III
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Throughput IV
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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

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Auto-ID Node Components
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Auto-ID Node Components
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Auto-ID Node Components II
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Auto-ID Node Components III
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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.

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

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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.