A WORKLOAD ANALYSIS AND FORECASTING STORY

By Carlo Grandi, SYNTAX S.P.A.

1. Background and study goals.

In January 1983 the bank management asked SYNTAX S. p. A. to do a workload

forecasting stuQy with the aim of estimating the workload growth over the next

two years.

2. Methodology and tools

An overall view of the methodology used is illustrated on figure 1.

The upper half of the chart explains how to deal with current workloads while

the lower part applies to the new application workloads.

We will now describe the two sections.

2.1. Current workloads

The first step is to define a workload classification that includes at least

categories like: control progra'l\ used (e.g. batch, rr'IS, TSO etc), business

application involved and type of service required. This step will also provide

information on how to distribute raw sys tern data such as SMF, RMF, PAn, c/nls

etc. into the appropriate workload groups.

After that, a data collection phase must be implemented in order to build a

workload data base reflectihg the workload classification just defined. After

sorae data has been collected, it is important to review the classification to

check if its goal has been met. The collection phase from raw data may be

avoided in some cases if the organisation already has implemented a data base

of measurement data that; satisfies the classification requirement.

A workload analysis with the basic aim of identifying the most important

business application has to be performed using the reporting capabilities of

the data base management system. At this pOint the decision of which business

application will be forecast with the Business Planning Units technique

(GJFE8l) has to be made, based upon the amount of resources used and the

importance of the application. The remaining portions will be grouped together

and forecast by means of a statistical analysis.

The opportunity of linkine the edp workload forecast back to the company

business forecast; is the main reason behind the choice of the BPU technique.

163

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Workload 'Claasification

BPUs data Collection

Data Filtering

Capture ratios Analysis

Regression Analysis

BPUs Forecasting

for most important appl ications

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Forecast User Review

DP terms BPUs forecast TrSBiation

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for other appl ications

other Users Measure

Similar Applications

Application Modr.lin,g

BPUs

Forecasting

for each new applicatiC"n

Forecast User Review

OP terms BPUs foreeas t Translation

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This fact will not only improve the accuracy of the forecast but is very

helpful in establishing a bridge among the users, the management and the

capaci ty planning people because they will all use a common language, rather

than edp jargon. Last but not least the edp forecast will reflect the company

goals towards which everybody is working, thus increasing the probability of

the reality unfolding as forecast.

The next steps will be repeated for each business application for which the BPU

technique will be applied.

The most probable BPUs for the business application have to be identified and

the historical data co:lected. Before using the statistical regression

technique to identify the BPUs which drive resources consumption, both the edp

and business data have to be filtered to remove holiday and other unusual data.

In reality, some times one just has one possible BPU and so the statistical

analysis will tell if we can or cannot apply this type of technique.

At this point one has to look for future information about the BPUs. The best

solution is to find the BPU forecast already made by the user or the company

planning function, but this will seldom be the case; instead we may have

general company aims stated that we can try to translate in numbers with the

users' help. When none of the above cases occur one can use some statistical

analysis techniques to develop the BPU forecast.

This is still better than making the resources consumption forecast directly

because the company data is more stable and has nothing to do with the fast

changing edp environment. in addition we have the opportunity of collaborating

with the users to modifY our forecast.

One is now ready to translate the BPU forecast into resource consumption terms

using the previous regression analysis results. This technique generates other

useful information such as resources consumption per unit of business, a very

important item for costing purposes.

The remaining applications have to be grouped by control program used and a

forecast. can be developed using statistical routines.

2.2. New applications wJrkload

The only possible way of knowing the amount of resources that will be used by a

new application is to apply the BPU technique after having quantified the

165

*

*

resources used by a single BPU. To accomplish this task there are different

ways such as by guesswork, measuring similar applications, using other computer

centre's measurements in case of packaged applications, and best of all using a

model like BGS's CRYSTAL TM.

In any case it is important to measure the application as soon as possible.

e.g. during the test phase.

2.3.Total system workload figure

The single business applications forecast has to be summed up by the control

program used. This can be done for particular time points in the future in a

discrete manner or continuously on a weekly or monthly basis.

The best way of forecasting the system overhead resources consumption is to use

a performance predictor model like BGS' s BEST/I TM. In this case the service

level obtained by the system will also be computed. Another way is to use a

regression analysis on the historical data to quantify the amount of system

overhead due to each control program and assume that the overhead ratios will

remain the same in the future.

The tools used are SAS TM i.e. Statistical Analysis System and a collection of

SAS programs developped by SYNTAX called WARM (workload and response time

managemen t) .

The SAS product was chosen above other languages, data base management systems,

and report writers, because of its unique ability to handle the high volume and

random nature of measurement data, in addition to its extensive inventory of

reporting capabilities, its comprehensive statistical analysis facilities, and

because of the flexibility and ease of use of the SAS language.

SAS is registered trademarks of SAS Institute Inc., Cary, NC, USA.

CRYSTAL, BEST/! are registered trademarks of BGS SYSTEMS Inc., WALTHAM, MA, USA.

166

3. Current Situation

3 . 1 Ha rdwa re and S of twa re

Nowadays two systems are installed:

IBM 3033UP with 8 megabytes of real memory connected to 42 disk units IBM

3350, 8 tape units IBM 3420-6, 2 remote line controllers IBM 3705,

and 4 printers. The operating system used is MVS/SP 1.1 with TSO,

ACF/VTMI 3 and IMS 1.1.5.

I Bt,l 3032 with 8 megabytes of real memory. For backup reasons the system is

connected to the same peripheral uni ts used by the IBM 3033UP and

runs the same software.

The IBM 3033UP system runs the bank online applications, the production batch

and a small testing activity while the IBM 3032 system is devoted to TSO and

the software development activity.

3.2 Resources utilization and service levels

The analyis of the current situation showed a system constrained by the lack of

real memory.

One of the symptoms was the reduction of the percentage of online teller

transaction completed by five seconds at peak hours (figure 2) and the

correlation between this phenomenon and the paging activity shown in figure 3.

In this chart the dots represent the data observed, while the lines show the

r'egression curve with the 95% confidence limits (PIST75).

Another symptDm of the problem was the high paging activity as one sees on

fi!3ure 4 where the 95th percentile reaches the value of 140 paging operations

per second at peak time. This rate indicated a serious problem because the

machine was runni.ng just batch and DIS.

Fo:' this rem;on we then spent some time investigating the problem further and

we found, from HUiF data, that at peak time there was paging activity at IMS

167

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1 A R){ - lORKLOAD AND RESPOlISE 'I1IIE ANALYSIS IllS CorreI.tin lDabU SYNTAX S.P.A.

3. TOTAL PACING RATE PER SECOND

168

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Figuri:! 4 TI A R Ii - TloRKIJ)AD ANtI RESPONSE TIIiE ANALYSL~

PAGINAZIONE giorno medio di GENNAIO 83 SYN1AX S .f' .i •.

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LEGEND: WORKLOAD ........ PAGING

Figure 5

SISTEMA 30:13

169

control region level which of course severely impacted the response time.

The mean service time for paging datasets was satisfactory, as shown on figure

5, because the system programmers spread the paging devices over different

strings connected to separa~e channels. This should not be considered a general

statement because others have got ten better results grouping the paging

datasets together on a dedicated I/O path (LEVY82).

The other components of the systems, CPUs and disks subsystem, were not highly

utilized as shown on figures 6, 7 and 8.

4. Workload classifications

The raw SM!" /RMF data and some portions of IMS data have been collected with

WARM and organized into a SAS data base such that the workload can be broken

down by bank business functions, type of work (production, test etc ... ) and

control program used.

The average CPU utilization by workload is shown on figure 9 for the production

sys tern both for the peak hour and the overall day.

For each control program it is also indicated in brackets, the CPU percentage

utilization that includes the system overhead as distributed by a regression

analysis routine.

For production batch and IMS the figures 10 and 11 pinpoint the workload

distribution by bank business functions.

It is interesting to note that with no more than 5 or 6 applications you cover

more than 75% of the total workload resource consumption while "conti correnti"

is responsible for about 50% of the production work.

Our best effort has been devoted to these parts of the workload and in this

paper we will report in detail how the forecast task has been approched for

some of the most important applications.

170

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Giorno medio di GENNAIC!I 983

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LE!;.END WORKLOAD ___ CAPACITY +-+-+ P95TOTAl ~_ TOTAL

SISTEMA 30:1:3

Figure 6

WAIl~! - lfl)Rf.LOAD ANII P.ESP'JtlSE TIME AlIAlYSL~

Giorno medio di GENNAIO 1983

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Analisi giorno medio di GENNAIO 83 EWUIPHEiH CLAS':; ... Dl':;k S'YNT.AX S'.f'.A.

\ s,.. ,a,.. ,2,8. 14:88 t6:8' ,a,a. 2.,.. 22'.. 20, ..

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LEGEND: WORKLOAD ........... CAPACITY r-3832 ............. Jell

Figure 8

172

PERCENTUAL1 D1 D1STRIBUZI0NE

CONTROL TIPO SERVIZIO NELLE ORE DI PUNTA SU TUTTA LA G10RNATA PROGRAM

BATCH 37(28) 53 (43) BATCH PROD 46 61 BATCH PROD DEPOSIT! 20 13 BATCH PROD CO"lTI CORR. 13 42 BATCH PROD PORTAFOGLIO 11 3.4 BATCH PROD TITOLI 11 5.5 BATCH PROD ANAGRAFE 9.8 8.2 BATCH PROD MUTUI 9 5.4 BATCH PROD BONIFICI 7.5 6 BATCH PROD SERVIZI 6.3 2.4 BATCH PROD TESORERIA 0.16 4.4 BATCH PROD RlMANENTE 11.9 9.7 BATCH GESTIONE 30 BATCH TEST 22 BATCH ALTRO 2

Iks 46( 57) 33( 43) IMS PROD CONTI CORR. 53 53.5 1MS PROD MUTUI 7.1 7 1MS PROD TITOLI 6.8 6.4 IMS PROD ANA GRAFE 6.3 6.3 1MS PROD TESORERIA 6.2 6.1 IMS PROD DEPOSITI 5.3 5.2 1MS PROD GARANZIE 5.3 5.4 1MS PROD MERCI ESTERO 4.8 5 IMS PROD BONIFICI 3.8 4 IMS PROD RlMANENTE 1.4 1.1

TSO 13(13 ) 11(11) TSO PROD 60 66 TSO PROD APL 100 100 TSO TEST 27 21 TSO GESTIONE 13 12

R1MANENTE 4(2) 3(3)

Figure 9

173

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Dati di Gennaio 1983

Figure 10

11 A R I{ - WORKLoAD ANI! RE~'POIISE IDlE AlIALYSI:l REL 2.0

Suddivisione carico I M S per servlzlo S Y N 1 A)' S .f· .. ':'

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174

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5. Workload Analysis and Forecast

5.1 "Conti Correnti" Application

This is the biggest and most important edp application; it has two parts: the

online running under IMS, serving all the. branches, to keep the customers

account up-to-date; and a batch part for special updating, reporting, back-up

of the databases etc.

The business parameter driving the reso~ce consumption of the application

might be the number of "conti correnti" operations performed by the tellers and

so we try to correlate the daily resource utilization figures, in terms of

software physics kilowork (KOKE77), to the number of "conti correnti"

operations performed.

We were able to use just two months of daily data but the regression analysis

gave good results (figure 12).

We sum up with, an average resourc.e usage figure for a teller operation as shown

in figure 13 which can be useful not only for the forecasting purpose but also

for costing.

IMS CPU kilowork

Disk kilowork

NO. transactions

Batch CPU ki lowork

Disk kilowork

1419

64.0

1.9

1755

26.8

Figure 13

Another comment over these numbers is that we have made similar analysises for

other banks in Italy and the numbers are so close to each other that they have

reinforced our feeling on the possibility of having the lcnged-for "industry

standard" .

At this point the problem of forecasting has been transferred from edp data to

banking data. We succeeded in getting the past 12 years of monthly "conti

correnti" operations data from the organization department. Using the Box and

175

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176

Jenkins technique by SAS ARIMA procedure we got the result seen on figure 14.

On this graph we reported the last three years data plus the forecast for the

next two years with the 95% upper confidence limit.

As can be seen the seasonal pattern has been taken into account and the

difference between the mean forecasted value and the 95% upper limit are small,

indicating a repetitive historical pattern and the numbers of "conti correnti"

operations forecasted has to be considered an upper limit because· in the 1980-

1982 period there was a slowing down of the growth experienced in the previous

years.

The absence of application modification plans allowed the use of the average

resources consumption figures for a single operation to translate the business

volumes forecasted in terms of edp resources utilization.

5.2 "Merci Estero" Application

This application manages the foreign currency exchange business. As usual it

has an online part, running under HIS, and a batch part.

The business planning unit was found to be the number of exchange operations.

In fact comparing the application software work with the number of operations

on a monthly basis, values of average work for operation were found with a

maximum oscillation of 1~fo around the mean value that can be seen on the figure

15.

ms CPU kllowork 1050

Disk kilowork 31

Batch CPU kilowork 1109

Disk kilowork 35

Figure 15

As for the previous application, a forecast of the number of exchange

operations was then worked out based on the preceding 12 years of history.

The resul t;s are shown on figure 16 where a seasonal pattern with the peak

summer months can be seen When the tourist season is at its maximum level.

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For the online application there were no modifications planned and so the

rrumber of bank operation forecast was used to develop the future resource

consumption forecast.

The batch application part was developped in conjunction with a software house

that used that experience to build up a package. For this reason some

maintenance modifications are expected and a 5% growth of resource utilization

per year per bank operation will occur.

5.3 "Titoli" application

A software house package to manage the bank and customer stocks and state bonds

will be installed by June 1983. In order to forecast the amount of computer

resources needed we decided to get information from one of the actual users of

this package.

Before that, one has to try to forecast the volume of stocks and state bonds

the bank will manage in the future. This step happened to be very difficult

because the historical pattern, figure 17, has a terrific jump at the end of

1980 when a lot of people swi tched to the state bonds from other types of

savings due to the higher interest rates offered.

The problem was solved by consulting the stocks office chief and his best guess

was an increase of 10% in volume.

We succeded in having information on the amount of computer resources needed by

looking for users of the package with the same amount of stocks and state bonds

managed.

5.4. Data consolidation forecast at control program level.

The consolidation stage has been accomplished for two specific future time

periods: December 1983 and 1984. December was selected because it is the month

with the highest bank activity.

For each control program a table that shows the amount of daily resource needed

by each application has been built up together with the total control program

figures and the yearly increase or decrease percentages. The unit of measure

used is gigawork, i.e. measure of work in billion.

179

i ~

6. Total systems utilization

Using the current situation average daily patterns, and the control programs

yearly growth percentage, we tried to estimate how the future daily patterns

will look.

The assumption made with this approach is that the daily pattern will remain

the same in the future. This assu~tion may not be true in the case of very

large growth, or if some components of the system become saturated, or if the

way of scheduling the work will change. Due to this reason we suggest carefully

investi§ating these problems before making this type of assumption.

The system overhead has been forecast applying the current capture ratios to

the fUture data. The values used are presented on figure 19 and they are a mean

value of the historical data. As for other rrumbers already mentioned in this

paper the "industry standard", computed in the same manner, happened to be very

close to those presented here.

CPU Disk

Batch 1.00 1.30

IMS 1.75 1.90

TSO 1.37 3.2

Figure 19

Using this method the system overhead figure may be underestimated because the

relationship between user workload and overhead is almost linear. In reality if

some threshold values are reached, the relationship becomes exponential.

The averages for a day in December 1983 is shown on figures 20, 21 and 22. From

these a maximum utilization level at morning peak hour is around 60% for the

IBM 3033U CPU and 50% for the IBM 3032. The disks subsystem will be used to

about 40% of its capacity thanks to the installation of new hardware, planned

by June 1983.

In December 1984, the disks subsystem and the IBM 3032 will still be used below

the critical level (figures 23,24); while the IBM 3033U will present a peak

hour utilization of 80% with a possible service level degradation (figure 25).

The first problem of service level degradation will arise in the batch part of

180

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11 ARM - woru:IJ)AD ANti RESPONSE TIME ANALYSIS REL 2.0

Giorno medio di DICEMBRE 1983 IYNTAX S.P.A •

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LEGEND: WORKLOAD ::::::: ~amITY ':"i"! i~a

PREVISIONI PER IL SISTEMA 3033

'/I A R I{ - WORKLOAD AlID RESPONSE IDlE ANALYSIS

Figure 20

REL 2.0

Giorno medio di DICEMBRE 1983 EfJUIPHENT CLASS=CPU SYNTAX S.P.A •

- .. -.-~

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TIME

LEGEND: WORklOAD = 8~~~~ ::::::: ~amITY ':"i"! f~a

PREVISIONI PER IL SISTEMA 3032

Figure 21

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Figure 22

, A R Il - lOllrulAD At.'D RESPONSE ron: ANALYSIS REL 2.0

Giorno medio di DICEMBRE 1984 EQUlf'"ENT CLASS-UISI( S.P.A.

I, .. 18:" t4:'. t6:" '8:86 28:'8 22:8' 24:8,"

TUtE ........... CAPACI TY +-+-+ 38:52 ...-....... J'll

Figure 23

182

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PREVISIONI PER IL SISTEMA 30:32 Figure 24

'II A R V - 'IIORKLOAD AND RESPONSE IDlE ANALYSIS REL 2.0

Giorno medio di DICEMBRE 1984

- - --,-----,-----,-7.'00 "·99 6:90

LEGEND: WORKLOAD

EQUIPl'iENf CLASS=Cf'U

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SYNTAX S.P.A •

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PREVISIONI PER IL SISTEMA 3033

Figure 25

183

the workload, usually a lower priority workload, even if we cannot guarantee an

absence of problems in the IMS area.

Up till now we have only considered average days, what about taking peak days

into account?

Comparing the monthly average day peak hour with the day peak hour of the first

five days of the month, we have found an increase of about 25% of the resources

utilization levels. Assuming that it will also be true in the future, we finish

up with the IBM 3033U system saturated at "peak-peak" hour towards the end of

period examined (figure 26).

The si tuation will be better for the IBM 3032 system (figure 27) and the disks

subsys tern (figure 28). This last point will be true only if all the needed

tuning activity is successfully accomplished.

7. Conclusions

After having presented our study to the bank management they decided to install

an add-on memory of 8 megabytes as soon as possible and to replace the IBM 3032

with an IBM 3083J systems by the end of the 1983 in order to support the new

3380 disks from both machines.

From our point of view the bes t result was the opportunity of showing them that

it is possible to build a workload forecast related to the business activity

even when previous years of edp data are not available.

This also becames the message that we would like to pass on: it is important to

begin in the workload forecast area; it does not matter how much one has to

guess or how inaccurate the result will be; it is a continuous learning process

and thee only way to improve is to make a plan to be compared with reality.

Nothing ventured is nothing gained.

184

WAR I.! - WORKLOAD AND RE~'POIlSE TWE ANALYSIS [tEL 2.0

Andamento previsionale utilizzo IBM 3033U h.lllU'hlNI ClASS==l Hl SYNTAX S.P .A.

'.01~--------------------------------~------____________________________ ~

•• a.

,. e •• ______ -

..---. ..---50

J.

2. ,.

I , • • iii iii I i

DEf.S2 HARB3 APR8J .JUN8J AUG8l OCT8l DECSl FE884 APRS4 JUNS4 AUC;S4 DCT84 DEe84

DATA

LEGEND; WORKLOAD ............. CAPACITY -ItEAM .......... PEAk

Figure 26

Daile ore 11:00 aile 12:00

'II A R Y - WORKLOAD AND RESPONSE TWE ANALYSL~ REL 2.0

Andamento previsionale utilizzo IBM 3032 EQUIPHENT ClASS=CPU SYNTAX S.P.A.

'°·"1 ." S. 70

N ••

J.

2.

,. e ~'....-T"..,..,.......--.- iii Iii ii' i i

or::Cll2 "ARSJ APRaJ ..K.JHa3 AU&Sl OCTa3 DECSl FEB84 APRR4 JUN84 AUGS" DCTS4 DEes"

DATA LE&END' WORKLOAD ~ CAPACITY ............ "EAN ......-.. PEAK

Figure 27

Dalle ore 15:00 aile 16:00

185

11 A R ~ - WORKLOAD AN[) RESPONSE TIME ANALYSL'S PlL 2('

Andamento previsionale utilizzo DISCHI

DEC82 "A1(83 APR83 JUN83 AUG83 OCT83 DEca3 fEBB", APR94 JUNB4 AUGB4 OCT84 DECEl4

DATA

LEGEND: WORkLOAD ......-... CAPACITY _____ PEAte

S.f' .A..

Dalle ore 11:00 aile 12:00

Figure 28

186

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REFERENCES

GJFB81 Converting Business plan to DP Workload Forecast, ICCCM april 1981

proceedings.

KoLL77 Kollence, W. Kenneth, An Introduction to Software Physiscs, ISE 1977.

BOJE76 Box, G.E.P., and G.M. Jenkins, Time Series Analysis: Forecasting and

Control, revised edition, San Francisco: Holden - Day, 1976.

PIST75 Pizer, Stephen M., Numerical Computing and Mathematical Analysis,

Science Research Associates Inc.: Chicago (1975).

LEVY82 Levy Ken, Configuring Paging Va1umes, North-East Computer Measurement Group, Oct. 1982 Proceedings.

187