performance of sap erp systems with memory virtualization ... performance of sap erp systems:...

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)


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


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)


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


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?



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


ERP

System

Physical Main

Memory

ERP

SystemVirtual Main Memory

Physical Main

Memory

Main memory compression


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


ERP

System

Physical Main

Memory

ERP

SystemVirtual Main Memory

Physical Main

Memory

Performance ?

Main memory compression


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


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)


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


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


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


• 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


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)


– 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


Measurement Results



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.


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


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.


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.

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