performance of sap erp systems with memory virtualization ... performance of sap erp systems:...
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Technische Universität München
Performance of SAP ERP Systems with Memory Virtualization using
IBM Active Memory Expansion as an example
5th International Workshop on Virtualization
© Prof. Dr. H. Krcmar
Marcus Homann
Technical University Munich
5th International Workshop on Virtualization
Technologies in Distributed Computing (VTDC)
Technische Universität München
Agenda
• Performance of SAP ERP Systems: Research at Technical University Munich
• Background & Motivation
• Performance Measurement Process
© Prof. Dr. H. Krcmar2
• Performance Measurement Process
• Performance Measurements Results
• Conclusion and next Steps
Technische Universität München
Performance of SAP ERP Systems: Research at Technical University Munich
ERP System
J2EEABAP
Performance SimulationStephan Gradl:• Performance
simulation with increasing number of concurrent users
• Focus on ABAP-Stack
Manuel Mayer:• Performance
simulation with increasing number of concurrent users
• Focus on Portal (J2EE-Stack)
© Prof. Dr. H. Krcmar3
Virtualization
Performance Measurement
J2EEABAPAndre Bögelsack:• Critical load• Focus on
ABAP-Stack• Comparing several
virtual machines
Holger Jehle:• Average load• Focus on
J2EE-Stack• Investigation of 1 virtual
machine
Marcus Homann:• Critical load• Focus on
ABAP-Stack• Focus on main-
memory-compression
Main-Memory Compression
Technische Universität München
In one sentence…
How does main-memory virtualization affect the performance of SAP ERP systems and which recommendations can be derived for data center operations?
© Prof. Dr. H. Krcmar4
Technische Universität München
SAP
ERP
SAP
ERP
SAP
ERP
System
SAP
ERP
System
Virtual Main Memory
SAP
ERP
System
SAP
ERP
System
Background & Motivation (1)
Scenario 1: Without Main-Memory Compression Scenario 2: With Main-Memory Compression
© Prof. Dr. H. Krcmar5
ERP
System
Physical Main
Memory
ERP
SystemVirtual Main Memory
Physical Main
Memory
Main memory compression
Technische Universität München
SAP
ERP
SAP
ERP
SAP
ERP
System
SAP
ERP
System
Virtual Main Memory
SAP
ERP
System
SAP
ERP
System
Background & Motivation (1)
Scenario 1: Without Main-Memory Compression Scenario 2: With Main-Memory Compression
© Prof. Dr. H. Krcmar6
ERP
System
Physical Main
Memory
ERP
SystemVirtual Main Memory
Physical Main
Memory
Performance ?
Main memory compression
Technische Universität München
Background & Motivation (2)Main-memory compression expands the main-memory capacity, but can negatively affect the application performance
Application Throughput
Uncompressed main-memory
Performance of Main-Memory CompressionConcept: Main-Memory Compression
© Prof. Dr. H. Krcmar7
Main-Memory Expansion Factor
CPU Utilization
Application Response TimePhysical Main-
Memory
Compression
main-memory data
Compressed main-memory
data
(Michel 2010, p. 7)(Michel 2010, p. 5)
Technische Universität München
Assumptions and Research Questions
The performance of SAP ERP systems is influenced negatively at a
certain main-memory expansion factor.
Using main-memory compression, additional SAP ERP systems can be operated on a physical server without any performance degradation.
Which main-memory compression techniques exist in literature, how is their performance evaluated and which performance results are available
A1
A2
RQ1
© Prof. Dr. H. Krcmar8
their performance evaluated and which performance results are available
specific for SAP ERP based workloads?
To what extent do different main-memory expansion factors affect the
performance of SAP ERP systems?
Which recommendations can be given based on the performance measurement results of RQ2?
RQ3
RQ1
RQ2
Technische Universität München
LitReview: Performance of main-memory virtualization Literature review shows that there is little knowledge about the performance
behavior of SAP ERP systems using main-memory virtualization.
• Main-memory compression is no new topic (Douglis 1993, Kaplan 1999)
• Distinction between hardware- and software-based main-memory
compression techniques; there is a trend towards software-based
techniques
© Prof. Dr. H. Krcmar9
• Only recently available in products of major virtualization vendors
• Evaluation is mainly based on the hardware-oriented SPEC CPU
benchmark suite
• Only one paper can be found where a SAP ERP workload is used for
performance evaluation (Michel 2010); however the author does not
describe what load generator he uses and how his test environment looks
like.
���� An detailed study about the performance behavior of SAP ERP
systems using main-memory compression is missing
Technische Universität München
Performance Measurement Process
• Environment:
– IBM Power 750 Server (512 GB RAM, 4 CPUs, 32 Cores, 3,3 GHz)
– LPAR: 4 virtual processors, 0.1 processing unit each)
– SAP ECC system EHP 4 (64 configured workprocesses)
• Load Generator and Measurement Tool: Zachmanntest (Bögelsack et. al
2011)
© Prof. Dr. H. Krcmar10
– Synthetic SAP benchmark, simulates a SAP power user
– Uses internal tables of the application server
– Outcome: throughput of the environment in rows per second
• 2 general Test setups: native, AME
• Variables:
– Number of parallel Zachmanntests (~ generated Load): 1, 2, 3, 6, 14, 20, 164)
– AME factor: 1.0, 1.3, 3.0, 5.0, 10.0
• Values of interest: Throughput (Zachmanntest: rows per second)
• Three runs per test setting: result is arithmetic mean
Technische Universität München
Measurement Results
© Prof. Dr. H. Krcmar11
Technische Universität München
Conclusion and next Steps
1. The performance of a SAP ERP system is influenced by activating AME.
2. At some point during the execution, a SAP ERP system may encounter a
huge performance collapse. This is especially true when choosing a very high AME memory expansion factor, e.g. 5.0, 10.0.
3. The performance of a SAP ERP system is influenced by both the activation of AME and the work load.
© Prof. Dr. H. Krcmar12
of AME and the work load.
4. At peak performance the AME factor seem to have no influence
5. Our proposed baseline with AME=1.0 does not reflect the best
performance. Instead, the best performance is reached with AME=1.3.
Next Steps• Gaining better understanding of AIX memory management
• Testing with a finer granuarity of AME steps
Technische Universität München
References
Douglis, F.: The Compression Cache: Using On-line Compression to Extend Physical
Memory. In: USENIX Conference, 1993, pp. 519-529.
Kaplan, S. F.: Compressed Caching and Modern Virtual Memory Simulation. Disseration
at University of Texas, Austin 1999.
Hepkin, D.: Active Memory Expansion: Overview and Usage Guide. IBM Whitepaper
2010.
© Prof. Dr. H. Krcmar13
2010.
Hevner, A.; Chatterjee, S.: Design Research in Information Systems. Springer Verlag,
Berlin 2010.
Michel, D.: Active Memory Expansion Performance. IBM Whitepaper, 2010.
Tremaine, R. B., Franaszek, P. A., Robinson, J. T., Schulz, C. O., Smith, T. B.,
Wazlowski, M. E.; Bland, P. M.:IBM Memory Expansion Technology (MXT). IBM Journal
of Research and Development, Vol. 45, No. 2, 2001, p. 271-285.
Tuduce, I.C. and T. Gross: Adaptive main memory compression. USENIX Association,
2005.