MEASURING THE BUFFER OCCUPATION OF SAP ERP

SYSTEM APPLICATIONS

Stephan Gradl, Manuel Mayer, Alexandru Danciu, Ramón Escrihuela,

Holger Wittges and Helmut Krcmar

Technische Universitaet Muenchen, Boltzmannstrasse 3, 85748 Garching, Germany

Keywords: ERP, SAP, Performance, Load Test, Measurement, Analysis.

Abstract: Enterprise resource planning (ERP) systems form the backbone for the execution, controlling and

management of business processes in today’s large companies. Availability and performance of ERP

systems is extraordinary critical for a company as even short unavailability or reduced throughput can be

very costly. As companies are evolving, the number of applications and the kinds of applications that have

to be supported are rising, which inherently also increases performance needs. However, the determination

of what makes up the performance needs is critical. The performance of SAP ERP systems strongly depends

on the usage of buffers for caching database contents. In order to predict the performance of SAP ERP

systems, it is necessary to understand and measure the buffer usage of applications running on SAP ERP

systems. In this work we explain the basic concepts and introduce a method for measuring the buffer usage

of SAP ERP applications. This method will be illustrated by a case study where each step of a business

process, executed on a SAP ERP system, will be analyzed according to the memory usage.

1 INTRODUCTION

The performance of an enterprise resource planning

(ERP) system is a business critical, non-functional

requirement (Schneider 2008).

In particular, we focus on the performance

analysis of a SAP Enterprise Resource Planning

(SAP ERP, formerly SAP R/3) system. Such

information systems are vital for companies business

processes (Krcmar 2009). SAP ERP is an integrated

backend application with tens of thousands of

installations worldwide, designed for tracking and

managing business processes in midsize and large

enterprises. From a technical perspective, this

application is built on top of a software integration

platform that provides primitives to control the

concurrency offered by application server and

database server.

The performance of these applications depends

on the resource used by the software integration

platform and the resource passed to the application.

A crucial part of these applications are the

implemented buffering techniques, as they allow the

processes to access data up to two orders of

magnitude faster than fetching the data from disks.

ERP systems are transaction based systems with a

high number of database accesses. This is reflected

by the fact that in average the time used for database

accesses takes up to one third of the overall

processing time. Therefore it is important to

understand the usage concepts and to be able to

measure the needed amount of resources in the

buffers. Without enough resources in the buffers, the

overall system, and the implemented business

processes are significantly slowed down.

The aim of this work is to illustrate the buffer

concept of SAP ERP Systems and to introduce a

method for measuring the resource demands of SAP

ERP applications in these buffers..

2 RELATED WORK

The key literature about performance measurement

and analysis of (enterprise) software systems are the

books of Jain (1991) and Lilja (2000). These authors

describe elaborately the whole process of

performance measurement, pointing out what

performance is, how it is measured, and which

factors affect the performance of a software system.

266

Gradl S., Mayer M., Danciu A., Escrihuela R., Wittges H. and Krcmar H..

MEASURING THE BUFFER OCCUPATION OF SAP ERP SYSTEM APPLICATIONS.

DOI: 10.5220/0003502302660272

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 266-272

ISBN: 978-989-8425-55-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

We are basing our work on the definitions made in

these books, and adopt them to the fields of ERP.

The importance of performance analysis is pointed

out by Menacé in his paper “Software, Performance,

or Engineering?” (Menasce & Almeida 2002). An

overview of the factors that determine the

performance of an application is given by (Bailey &

Snavely 2005) and (Hollingsworth et al. 2005). For

the performance of an ERP system, we refer to

(Schneider-Neureither 2004). In this book, the

author explains in detail the effect of the SAP

architecture and configuration on its performance,

focusing on the solution of concrete operational

problems. Although the book is written as an

administrator manual, it provides a good overview

of the factors affecting the SAP system´s

performance. An overview of existing SAP

benchmarks is given in (Prior 2003). The usage of

SAP trances is also mentioned in the work of (Schult

& Kassem 2008). Here, the traces are utilized for

system analysis to create recommendations for

automatic customization of a SAP system.

A scientific approach for the measurement of

ERP performance behaviour – in this case focusing

on the effects of virtualization – is presented by

(Jehle 2009) and (Bögelsack et al. 2010). While

Jehle (2009) is focusing on the response time

behaviour using a load test, (Bögelsack et al. 2008)

is analyzing systems´ internal patters, especially the

CPU time, for interpreting its effect on the system

performance.

Jin (2007) shows a method for performance

prediction of legacy information systems. As the

internal architecture of the investigated productive

information system is not known, the authors used a

method that is based on a black box approach for

predicting the technical performance of this legacy

information system with historical values. This

approach combines benchmarking, production

system monitoring, and performance modelling

(BMM). By analyzing and correlating the

performance values derived from the benchmarks

and monitoring, a model is created that is used for

the performance prediction.

In (Rolia et al. 2009), a LQM model for the

performance prediction of SAP ERP system is

introduced. In this approach the statistical records

provided by the SAP system are used for

performance analysis and prediction. In addition, the

authors also used CPU values gathered from an SAP

tool called saposcol. The workload used is based on

a sales and distribution scenario, very similar to the

workload that is applied in the SAP Sales and

Distribution benchmark. Buffers, both from the

application servers and the database, are not

mentioned in this work.

3 SYSTEM ARCHITECTURE

To provide an understanding of the ERP system

architecture, in the later referred to as SAP WebAS,

shown in Figure 1, we derive the system components

from the ERP process step-by-step by analyzing the

recorded trace and the abstraction of the trace

entries. These components are described in detail in

(Schneider 2008). The process step of calling a

program involves many components of the SAP

system.

Figure 1: Schematic architecture of SAP WebAS.

Searching for the program includes access to

internal buffers as well as access to the database

tables. This access is made by the so called

disp+work processes of the SAP system. Such

processes are responsible for executing programs,

processing user or web service requests, and

accessing the database. Before a request is

associated to one of the disp+work processes of the

SAP system, a dispatcher process is accessed. The

dispatcher process manages all other processes in the

SAP system, and his primary task is to assign a user

request to a free disp+work process. In our model,

we assume the database as a black box.

After the SAP system got the information which

program has to be executed, it loads a compiled

version of the program from the database and

executes it. Sometimes such compiled programs are

held in the internal buffers of the SAP system to

avoid database accesses.

After the request is processed, the data should be

saved to the database. This is done by the disp+work

process(es) together with a process called update

process. This process receives data and stores it in

corresponding database tables.

MEASURING THE BUFFER OCCUPATION OF SAP ERP SYSTEM APPLICATIONS

267

Simultaneously, a lock on a central table is

established, which may be described as a small

repository of all available material master records

(MMR) within the system. This lock is not set by the

disp+work process itself; it triggers a so-called

enqueue process. The only task of the enqueue

process is to set locks on any tables in the SAP

system, and to manage such locks. After the lock

was set successfully, the disp+work process can

store the data into the central MMR repository.

4 BUFFER CONCEPTS

Each SAP instance or application server has its own

buffers. SAP buffers occupy memory areas that are

local to the work process, and in individual shared

memory segments that can be accessed by all work

processes. These memory areas are executed for the

application server. SAP buffers store frequently-

used data, and make this data available to the local

application server instance. This helps to reduce the

number of database accesses, the load on the

database server, and network traffic. As a result,

system performance is considerably improved. The

data that is buffered includes ABAP programs and

screens, ABAP Dictionary data, and company-

specific data. Typically, these remain unchanged

during system operation. The sizes of buffers can be

changed or tuned to optimize performance for a

particular hardware and workload configuration.

There are several ways to tune buffers.

To support the performance of the SAP WebAS,

there are a number of buffers that can be classified

in seven groups: Repository Buffers, Table Buffers,

Program Buffer, SAP GUI Buffers, Roll and Paging

Buffers, SAP Calendar Buffer and SAP Cursor

Cache. As the SAP WebAS initially contains only

the complete source code of all the available

applications, they are compiled at the first access.

The compiled code is permanently stored in the

database and in the the Program Buffer. Active table

and field definitions, the ABAP code runs on, are

stored in the Repository Buffers. This group consists

of four buffers for the different to deliver

information of tables and fields for different use

cases. To support the presentation of the data using

the SAPGui, the SAP WebAS holds generated

screens, menus and push-button definitions in the

SAP Gui Buffers . The SAP WebAS shares his work

processes to all the users on the system. Therefore

after finishing a user request the memory context of

the specific user session has to be stored outside the

work process. To have the user session information

available this data is held in the Roll and Paing

Buffers. As an extension to these buffer there are the

Export/Import Buffers and the Export/Import Shared

Memory. The Calendar Buffer is mentioned to have

different kinds of calenders, defining holidays and

other free days available for the work processes. The

number of parsing procedures for SQL statements is

reduce by storing relevant information in the SAP

Cursor Cache.

5 MEASURING BUFFER

RESOURCES IN SAP WEBAS

5.1 Measurement Tool

The SAP WebAs provides a large number of

measurement tools for performance monitoring and

analysis. The gui of these tools are implemented as

SAP transactions and therefore not suitable test

series. To execute an automated test series, the web

service SAPTUNE_GET_SUMMARY_STATISTICS

was used. This web service delivers detailed

information about the current state of SAP WebAS

buffers, as exemplarily shown in table 1.

In SAP, the size of the buffers is defined in

kilobytes and number of entries. A buffer can be

therefore full, although there is space left, by

reaching the limit of entries. When the SAP WebAS

is started, the buffers are initialized with their full

size by allocating the needed amount of memory.

The web service delivers the currently amount of

memory that is used in the different buffers

(Used[KB]), the hitratio, the overall (allocated) size

of the buffer (Alloc[KB]), the percentaged free

space (Freespace[%]), the number of entries

(DirSize), the percentaged number of free entries

(FreeDir[%]), the number of replaced entries

(Swaps), and the number of Database accesses

needed to fill the buffer (Schneider 2008). The

hitratio is defined using formula 1:

=

  − ℎ 

 

∗ 100

(1)

For the management of the buffer some memory

is needed, which is the reason for the difference

shown in formula 2:









+ 







≠ 







(2)

Although the data is not accessed using the SAP

Gui, the execution of the webservice request does

also use the buffer infrastructure of the SAP WebAS

and therefore creates a certain amount of buffer

entries. This bias has to be overcome by adapting the

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Table 1: Delivered values by SAPTUNE_GET_SUMMARY_STATISTICS.

Buffer

Used

[KB]

Hitratio [%] Alloc [KB]

Freesp

[KB]

Freespace

[%]

Dir

Size

FreeDir

[%]

Swaps

Accs

TTAB 486 95,3646 6797 5214 91,47369 19990 91,47073 0 59536

FTAB 984 97,4883 31564 29018 96,720215 19990 98,554276 5246 20450

SNTAB 16 97,8395 3625 2984 99,46667 4997 98,81929 0 3795

IRBD 196 44,2553 6625 5804 96,73333 4997 94,576744 672 17484

TABL 2835 99,2928 29297 24888 89,773834 5000 99,04 45 116574

TABLP 101 86,4173 10000 9711 98,97064 500 97 5 130183

PXA 278248 99,0965 300000 72 0,025868416 75000 91,305336 12595 117483

CUA 78 99,5748 3000 2309 96,7323 1500 99,73334 0 116

PRES 15 99,6205 4297 4078 99,63352 2000 99,55 0 177

CALE 211 100 488 267 55,857742 200 49 0 1020

EIBUF 9 91,0683 4096 3272 99,72569 2000 99,95 0 0

OTR 0 100 4096 3281 100 2000 100 0 0

ESM 0 99,5268 4096 3281 100 2000 100 0 0

measurement approach as introduced in the

following chapter.

5.2 Measurement Approach

The memory consumption in the SAP Buffers

strongly depends on the workload the system has to

process. Therefore, the analysis has to be oriented to

the workload in order to capture all its impacts.

As the selected measurement tool is executed

using the buffer infrastructure of the SAP WebAS,

these measurements are intrusive and therefore

biased. For a significant measurement, the impact

baseline of this measurement tool has to be

identified and taken into account within the analysis.

Figure 2: Approach for measuring occupancy level of SAP

WebAS buffers.

To identify the baseline resource consumption by

the measurement tool, SAP buffers have to be

cleared. Within the whole process shown in Figure 2

it has to be guaranteed that no other workload by

users or scheduled background processes are

executed on the SAP system. To clear the buffers the

SAP function $SYNC was used, as “this resets all

buffers of the application server” (SAP 2009). To

identify the baseline for the measurement, the

resources in the buffer consumed by the

measurement tool have to be measured. This is done

by executing the SAPTUNE_GET_SUMMARY_-

STATISTICS web service several times.

Doing this, it turned out that the mentioned

amount of memory for the management of the buffer

does not only minimize the available space in the

buffer. This management information is created

when the buffer is accessed. To avoid a large

number of buffer synchronizations in the SAP

WebAs, there is a number of requests that are

always answered by the database directly after a

buffer is refreshed. The combination of these two

aspects of the buffer management in the SAP

WebAS is shown in Figure 3 using the example of

the table buffers:

Figure 3: Buffer occupation of measurement tool.

Depending on the access rate of the different

table buffers, it takes some iterations (x-axis) until

MEASURING THE BUFFER OCCUPATION OF SAP ERP SYSTEM APPLICATIONS

269

the buffers are used and the management

information is created. This effect has to be taken

into account when measuring the resources used in

the buffers of applications. Without doing this, the

mean of the measurement values suffers a significant

standard deviation as shown in table 2.

Table 2: Baseline values of SAPTUNE_GET_-

SUMMARY_STATISTICS without warm-up phase.

Buffer MEAN STD.DEV

TTAB 3622,15 20,61240046

FTAB 26161,1 2306,299152

SNTAB 272,75 2,953588362

IRBD 1898,2 32,60222789

TABL 27628,95 3,605186323

TABLP 991,5 27,0972906

PXA 277185,55 444,9519755

PRES 116,35 3,013565819

Taking into account only the values after the

buffer is completely initialized, the values in our test

series are constant showing no deviation (Figure 3).

With these criteria, these values can be taken as the

baseline for the measurement of the resources a

workload consumes in the buffers.

5.2.1 Measurement

The measurement follows the same principles as the

identification of the baseline using the web service

SAPTUNE_GET_SUMMARY_STATISTICS. As

shown in Figure 4, the effects of previous workloads

are eliminated by refreshing the buffers using the

OK-Code /$SYNC (SAP 2009). To eliminate the

already described aspects of the SAP WebAS buffer

management, the workload has to be executed

several times to secure that the buffer has been

completely initialized and used within the workload

to be measured as shown in Figure 4.

Figure 4: Process for measuring workload buffer

occupation level.

As the workload can produce data in the

database, the amount of space used in the buffers

can constantly rise. The influence of this aspect is

generally rather small as the SAP WebAS does not

buffer tables if more than 1% of database accesse are

modifying the content (SAP 2007). If the occupancy

level of the buffers is constant or rises constantly,

the workload measure cycle can be stopped, as the

mentioned aspects of SAP WebAS buffer

management are eliminated.

5.2.2 Evaluate Results

In the measurement cycle, the workload and the

measurement tool have been executed several times.

Therefore, the buffers are filled with both the data of

the workload and the data of the measurement tool,

as shown exemplarily in Figure 5. In order to get

significant results, the values of the measurement

tool (introduced as baseline in this chapter) have to

be subtracted from the measured values of the

workload measurement step.

Figure 5: Identifying base value of exemplary workload

buffer occupation level.

The base value shows the minimum occupancy

level of the respective buffer. If the buffer

occupancy shows a constant expansion, then the

difference between two measurement actions is

referred to as expansion rate. To reliably evaluate

the amount of memory a certain workload occupies

in the buffer, the following formula can be used:

  =  



   

∗  

(3)

The method does not depend on the specific

workload, as every program executed by the SAP

ERP system uses a subset of the buffers. The buffer

requests are not triggered by the program itself. The

SAP System provides an abstraction layer between

the program and the database that decides if the data

is fetched from the buffer or from the database.

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6 CASE STUDY

A main task of ERP systems is the management and

monitoring of business processes. In our case study

we look into an exemplary production planning

process, since it demands many core functionalities

like master data management or work organization.

6.1 Explanation of the Case Study

The workload is based on a SAP University

Competence Center (Weidner 2006) case study and

includes the creation of material master data, bills of

materials, and routing (work processing sheets) for a

motorcycle, consisting of the engine, cylinder block,

cam shaft, and the chassis. For the semi finished

product, the engine, and the finished product, the

motorcycle, bills of materials and work processing

sheets are defined. These work steps - and

consequently the whole business process - are core

business processes and frequently used in business.

The underlying ERP System was a SAP ECC

6.00 system with the IDES dataset running on

application- and database server with Sun Solaris 10.

6.2 Measurement

Following the introduce method (chapter 6), the

measurement started by resetting the SAP Buffers

using the OK-Code /$SYNC. The result of the

baseline identification is shown in Figure 3. In our

test scenarios we identified a constant baseline.

After another reset of the buffers, the workload,

consisting of the already described business process

followed by the measurement web service

SAPTUNE_GET_SUMMARY_STATISTICS, was

executed 35 times. Figure 6 shows that in the

laboratory experiment the base value was reached

after 5 executions (x-axis) of the business process

and the measurement web service.

6.3 Measurement Results

The measurement tool provides the analysis of all

buffers in the SAP WebAS. In the following we

focus on the table buffers TTAB, FTAB and TABL

due to their bigger relevance. The behaviour of the

other buffers was similar to the ones displayed in the

following graphs.

The measured results of the buffer occupation

under the defined workload, as shown in table 3, had

a significant deviation from the arithmetic mean, as

the base value was not taken into account. Applying

the introduced method, the values obtained were

Figure 6: Buffer occupation level of workload and

measurement tool.

constant, showing no deviation from the mean.

Figure 7 shows the corresponding result. From the

measurement values shown in Figure 6 only the

values after the base value were taken into account.

By substracting the base line from these values we

obtained our final measurement results, shown in

Figure 7.

Figure 7: Buffer occupation level of business process

without measurement tool.

This graph shows that we were able to receive

constant and therefore significant values for the

buffer occupation under the defined workload.

MEASURING THE BUFFER OCCUPATION OF SAP ERP SYSTEM APPLICATIONS

271

7 CONCLUSIONS AND FUTURE

RESEARCH

In this paper we introduced a method for measuring

the buffer usage of SAP ERP systems and proved

the technical feasibility of this method by a case

study. Understanding the resource demands of each

application running on the system is crucial for

maintaining a high-performance system.

The proposed method implies the access to the

SAP ERP graphical user interface for resetting the

buffers. Therefore, the method cannot be fully

automated using technologies like web services.

The measurements have shown that the buffer

usage passes a transient phase for several databse

accesses after the system is started.

Further research is required for understanding the

results of the introduced method. Currently, the

method only considers the occupancy level of

buffers. In a next step, the content of these buffers

should be considered as well. The strategy of

replacing buffered content should be analysed as

well.

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