Transcript Auto-ID Cockpit

Kai Sachs (TU Darmstadt)
Supervisors:
Christof Bornhoevd (SAP)
Mariano Cilia (TU Darmstadt)
Evaluation of performance
aspects of the Auto-ID
Infrastructure
CONTENTS
Auto-ID Infrastructure
Measurement Approach
Results of the Experiments
Final Conclusions
Auto-ID Infrastructure
Measurement Approach
Results of the Experiments
Final Conclusions
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
AII: Overview
(2)
 The illustration below shows an overview of SAP RFID landscape:
Device
Controller
Reader
RFID
Tags
SAP Auto-ID
Infrastructure (AII)
SAP
Exchange
Infrastructure
(XI)
SAP R/3
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
Auto-ID Node System Architecture
XML
TG
Message
Dispatcher
Activities
Rule
Engine
AIN
Repository
From: SAP Auto-ID Infrastructure
XML
Integration Layer (XI)
XML
Auto-ID Node
Communication Layer
DC
Communication Layer
Auto-ID Cockpit
IDoc
BE
IDoc
BE
CONTENTS
Auto-ID Infrastructure
Measurement Approach
Results of the Experiments
Final Conclusions
Test Environment
What should be observed?
Experiments settings
 Multiple readers
 Message size
Customized
Traffic Generator
System behavior
 CPU load
Microsoft
Performance
 IO Activities
 Single processes
 Memory …
Throughput
Customized
Traffic Generator
Components on the Auto – ID Infrastructure
 Gross Times
 Gross CPU Times
JARM
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.
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
JARM Measurement Points
XML
TG
Message
Dispatcher
Activities
Rule
Engine
AIN
Repository
XML
Integration Layer (XI)
XML
Auto-ID Node
Communication Layer
DC
Communication Layer
Auto-ID Cockpit
IDoc
BE
IDoc
BE
JARM Measurement Points
XML
TG
Message
Dispatcher
Rule
Engine
Parser
HTTP
Activities
AIN
Repository
XML
Integration Layer (XI)
XML
Auto-ID Node
Communication Layer
DC
Communication Layer
Auto-ID Cockpit
Rule
Processor
IDoc
BE
IDoc
BE
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
…
CONTENTS
Auto-ID Infrastructure
Measurement approach
Results of the Experiments
Conclusion
Results of Experiments
CPU Load
IO Activities
Throughput
J2EE Components of the Auto-ID Node
Different VM settings
Settings of Message Dispatcher
Results of Experiments
CPU Load
IO Activities
Throughput
J2EE Components of the Auto-ID Node
Different VM settings
Settings of Message Dispatcher
CPU Load
CPU Load (9 EPCs per msg.)
Fall down
100
90
CPU Usage in %
80
70
60
Other
50
Server
Dispatcher
MaxDB
40
30
20
10
0
0
10
0
20
0
30
0
40
0
50
0
60
0
70
0
80
0
0
0
0
0
0
0
0
0
0
0
90 100 110 120 130 140 150 160 170 180
time in sec.
Incursions
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.

AverageThroughput
Keyfigure
AverageCPULoad
IO Activities I
Savepoints of
MaxDB
4000.00
Physical Disk vs. MaxDB
3500.00
3000.00
2500.00
Physical Disk
2000.00
MaxDB IO Data
100kBytes/sec
1500.00
1000.00
500.00
time in sec
1840
1760
1680
1600
1520
1440
1360
1280
1200
1120
1040
960
880
800
720
640
560
480
400
320
240
160
80
0
0.00
IO Activities II
MaxDB IO vs. Processor Load
100
90
80
70
60
MaxDB IO /
(180 *1024)
Processor
50
40
30
20
10
10
0
20
0
30
0
40
0
50
0
60
0
70
0
80
0
90
0
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
0
0
time in sec.
Savepoints of MaxDB
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!!!
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
Throughput II
Avg. throughput
285
300
271
255
250
259
241
EPCs per sec.
196
200
150
216
Measured values
181
Key figures
100
50
0
9 EPCs
45 EPCs
90 EPCs
Message size
900 EPCs
Throughput III
Avg. throuhgput
300
EPCs per sec.
250
9 EPCs
per msg.
200
45 EPCs
per msg.
150
90 EPCs
per msg.
100
900 EPCs
per msg.
50
0
[300,600]
[601,900]
[901,1200]
Interval
[1201,1500]
[1501,1800]
Throughput IV
Avg. Throughput
EPCs per sec.
190
185
180
175
170
165
160
155
1
3
5
Simulated readers
7
10
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
Auto-ID Node Components
Avg. Gross Time for one request
10000
ms.
1000
Gross Time
100
y=3.87 x + 18.9
10
1
9
45
90
EPCs per msg. (x)
900
Auto-ID Node Components
Gross Time of AII Components
100%
90%
80%
70%
Other
60%
RuleProcessor
50%
Parser
40%
Http Server
30%
20%
10%
0%
9
45
90
Msg. Size
900
Auto-ID Node Components II
Gross Time CPU of Rule Processor Components
100%
90%
80%
Others
70%
60%
Activity:CREATE_
CURRENT_STATE
50%
Activity:REGISTER
_UNEXPECTED_O
BJECT
Rule Engine
40%
30%
20%
10%
0%
9
45
90
Msg. size
900
Auto-ID Node Components III
REGISTER UNEXPECTED OBJECT
Gross Time
100%
80%
60%
DB: Read Records
40%
DB: Insert Records
App. Server
20%
0%
9
45
90
EPCs per msg.
900
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.
CONTENTS
Auto-ID Infrastructure
Measurement Approach
Results of the Experiments
Final Conclusions
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
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.