PROGNOSTIC CAPACITY MANAGEMENT FROM AN IT

SERVICE MANAGEMENT PERSPECTIVE

Thomas Jirku

University of Applied Sciences “Technikum” Vienna, Austria

Peter Reichl

Telecommunications Research Center Vienna (ftw.), Vienna, Austria

Keywords: IT Service Management, ITIL, Capacity Management.

Abstract: Maintaining the balance between keeping the IT continuously running and at the same time enhancing the

quality of the services and responding with increasing agility towards changing business needs under the

usual budget constraints is one of the core challenges for IT service management. Over the last years, ITIL

has become the de facto standard best practice recommendation for this purpose. Among the 10 different

ITIL disciplines, capacity management is one of the most important ones. However, the ITIL perspective is

restricted towards describing the current state of an IT system. In this paper, we propose to go a step ahead

and turn capacity management into a proactive discipline for managing IT infrastructure. To this end, we

discuss requirements on a platform-independent capacity data base and present a case study which shows

how to simplify its performance evaluation through an approximation approach. Finally, we draw a couple

of practical conclusions and describe further steps towards a capacity management which allows to deal

proactively with potential problems and bottlenecks.

1 INTRODUCTION

Due to increasing cost pressure and complexity, it

has become more and more important to proactively

manage the IT service landscape in order to identify

and potentially avoid expensive bottlenecks and/or

corresponding outages of IT operation in advance.

To this end, the complexity of enterprise processes is

reduced to create a joint standard for an integrated,

simplified and unified IT service management. In

this context, the establishment of a common data-

base, the so-called Configuration Management Da-

tabase (CMDB), allows all users and processes to

have access to identical information, and thus has

become one of the central concepts within the

framework of the IT Infrastructure Library (ITIL).

Among the different ITIL disciplines, capacity

management is of specific importance, as an

efficient and comprehensive capacity management

can lead to early recognizing and effectively

counteracting future bottlenecks in the IT. This

allows to use scarce budget resources more

efficiently and to increase the satisfaction of the end

customers also on a long-term timescale.

This paper discusses several innovative aspects

of ITIL capacity management. After a brief

overview on fundamental ITIL concepts and

capacity management in general, we first deal with

the requirements on the CMDB and propose a

concept for a system- and platform-independent

capacity management database. We then describe a

characteristic reference service comprising two tiers

of components, and derive requirements on

corresponding workload models. We then use this

service for a case study discussing capacity-related

performance evaluation. Having indicated the

standard approach, we then demonstrate how to

simplify this quite complex task by using a

queueing-theoretic approximation approach based

on Little’s Law. In the final section, we summarize

our work, before we draw some conclusions and

point out several directions for further work.

320

Jirku T. and Reichl P. (2008).

PROGNOSTIC CAPACITY MANAGEMENT FROM AN IT SERVICE MANAGEMENT PERSPECTIVE.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - ISAS, pages 320-325

DOI: 10.5220/0001710603200325

 SciTePress

2 A BRIEF HISTORY OF ITIL

The IT Infrastructure Library (ITIL) has been devel-

oped almost 30 years ago as an initiative of the Brit-

ish Central Computing and Telecommunications

Agency (CCTA, today Office of Government Com-

merce, OGC). Actually, work focuses on the final

version 3 (ITIL Refresh) whose framework has been

published on June 1, 2007 (Poizat 2007).

ITIL has been compiled in order to facilitate the

planning, monitoring and controlling of high-quality

IT services, and over the years has become the de

facto standard best practice in this field. Thus, today

ITIL is the only one comprehensive non-proprietory

process library focusing on provision and

compliance of IT services according to a process-

oriented model.

The fundamental idea of ITIL is related to a

central and jointly used data base, the Configuration

Management Database (CMDB) which contains all

relevant configuration information of the so-called

Configuration Items (CI) of the system (like

software, hard disks etc.). The CMDB consolidates

information which otherwise would be spread

between e.g. problem, change and process data

bases, and thus allows an efficient and transparent

access to these data.

Based on functionality, two main parts of ITIL

are distinguished. Service Support deals with IT

services on an operational level, including service

desk, incident management, problem management,

configuration management, change management and

release management. On the other hand, Service

Delivery is concerned with planning and operating

processes for a professional provision of IT services.

Corresponding issues include service level

management, financial management, capacity

management, continuity management and

availability management. Among these, we will

concentrate on capacity management as a key

discipline for the resolution of incidents and pre-

identification of capacity-related problems.

Note that the detailed understanding of business

requirements and corporate processes is an essential

prerequisite for capacity management to be capable

of dealing with current and future developments

both in economics and technology. Capacity

management processes target the complete hardware

infrastructure, peripherical systems, the complete

software infrastructure and to some extent even

human resources. In this framework, capacity

management provides information about the current

(and ideally also future) resource usage of individual

components and services in order to enable well-

founded and fact-based decisions for the enterprises.

3 REQUIREMENTS FOR A

SYSTEM - AND

PLATFORM-INDEPENDENT

CAPACITY DATABASE

It has already been mentioned that the capacity data-

base is among the most important ideas to be found

at the heart of ITIL. In order to comply with the

model of a comprehensive and system- and plat-

form-independent capacity database which is suited

to provide short- and long-term predictions of IT

utilization and performance of capacity, the follow-

ing requirements have to be met:

• Java-like independence of lower layer systems

and platforms;

• High scalability and availability of the data

base with respect to size and storage capacity

(including concepts of federated data base ar-

chitectures and data staging);

• Compliance with existing standards for data

import and export (in the sense of a data ware-

house);

• Free choice of Key Performance Indicators

(KPI) of the performance models;

• Conducting “what if”-scenarios through di-

rected change of certain data sets for simulation

of developments and changes within the enter-

prise;

• Rapid identification of “root causes” through

correlation with other incidents and problems to

allow precise predictions of future develop-

ments and potential problems;

Note that most widely used commercially avail-

able data bases, like Microsoft SQL, ORACLE,

DB/2 and Ingres, heavily depend on the operating

system underneath. As a very prominent example

consider Microsoft SQL, where the SQL database

server may only be installed on the windows operat-

ing system. Therefore, a data base system would be

desirable which may be installed regardless of the

operating system and then runs without constraints.

There is already a clear and well established ana-

logue to this idea within the area of programming

languages, i.e. Java with its notorious virtual ma-

chine concept. In a similar way, the functionality of

a platform-independent data base could be specified

according to Figure 1.

PROGNOSTIC CAPACITY MANAGEMENT FROM AN IT SERVICE MANAGEMENT PERSPECTIVE

321

Hardware / OS

Database Virtual

Machine

Requests

Database

Figure 1: Concept of a Database Virtual Machine.

For further details on the Database Virtual Machine

we refer to (Jirku 2008).

4 PROGNOSTIC CAPACITY

MANAGEMENT – A CASE

STUDY SCENARIO

Having discussed properties and requirements of the

central capacity data base, we now introduce prog-

nostic performance evaluation, focusing on the fol-

lowing reference service as an interesting initial case

study.

4.1 Definition of a Reference Service

Following the current strong trend towards web ap-

plications in enterprises which more and more re-

place traditional mainframe applications and/or clas-

sical client/server applications, we consider the fol-

lowing rather generic scenario for our subsequent

performance evaluation:

• An end user stays at the edge of the network,

together with a router and several client PCs.

The customer router connects to the WAN, the

corporate router relates the WAN and the cor-

porate LAN of the central site. The client and

the corporate networks are a 100MBit Ethernet

network.

• The end users access a portal from their client

PCs on a web server in the central site, in order

to receive data. As a protocol they use http 1.1

over TCP/IP.

• The users demand data from the web server to

generate a report. To this end, texts and pictures

of different size are transmitted.

• The web server prepares the report requested

and makes it accessible to the user. Finally, the

report is shown on the client PC screen.

Figure 2: Architecture of the Reference Process.

Figure 2 illustrates this “two tier models”, based

on the information flow from client PC to the web

server and back. Note that the clients involved can

be classified as “thin” and “fat” clients, where the

thin clients (a.k.a. network computers) lack of com-

puting power as well as local hard disks and thus

depend on a server and cannot operate on their own.

In contrast, fat clients are computer systems dispos-

ing of significant computing power and permanent

storage of their own.

Additionally, we include the possibility of cach-

ing sought-after data e.g. on the system of the end

user or in between, in order to prevent resource

wasting because the client has to access the server(s)

for every individual request. For further details on

cacheing strategies we refer to (Zeng 2004).

4.2 Workload Concepts

In order to achieve reliable capacity predictions and

to be able to calculate and understand the behaviour

of IT services under different load scenarios, we

need precise workload models, i.e. the mapping of

real load models characterised e.g. by increasing

user demands. This includes not only metrics like

CPU or hard disk usage, but also busy hours, public

holidays or marketing campaigns which may influ-

ence the user behaviour and thus the workload sig-

nificantly.

Summarizing, workload concepts have to take

the following requirements and steps into account:

• The location of the equipment measuring re-

sponse and transaction times has to be speci-

fied;

• The relevant parameters for prognosis have to

be specified;

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• The system has to be monitored for a certain

time t to collect historical data as a basis for

modelling;

• Aggregation of data for reducing the amount of

data to a necessary minimum;

• Compilation of a workload model correspond-

ing to reality;

• Plausibility-check of all parameters and metrics

characteristic for the model to guarantee preci-

sion.

The load e.g. of a client-server has to be de-

scribed from different perspectives. With respect to

the reference process described in section 4.1, three

different perspectives can be derived: The business

model refers to metrics like the number of available

items which can be ordered in a web store, or bills

per customer generated by an ordering system. On

the functional layer, parameters which describe how

requests are processed by applications are described,

meaning that how many transaction can be proc-

essed by the billing application itself. Finally, the

resource-oriented lowest layer includes resources

whose usage may lead to long transaction times or

high CPU usage. Figure 3 sketches the resulting

layered workload model. For a more detailed expla-

nation on different workload models we refer to

(Menascè 1994).

Business

Model

Functional

Model

Resource

Model

Business Properties

Programs, Functions,

Applications,

IT Architecture and

Service Demands

External

Metrics

Internal

Metrics

Business

Perspective

Technical

Perspective

Business

Model

Functional

Model

Resource

Model

Business Properties

Programs, Functions,

Applications,

IT Architecture and

Service Demands

External

Metrics

Internal

Metrics

Business

Perspective

Technical

Perspective

Figure 3: Layered Workload Model.

5 HOLISTIC PERFORMANCE

ANALYSIS

In a strict sense, ITIL and the general concept of IT

services only allow evidence about the current state

of IT services. As an IT service usually consists of

several individual components communicating with

and depending on each other (see section 4.1), we

consider a holistic perspective on the performance of

the IT service as appropriate, which is of course

composed of the individual components and their

performance.

5.1 Parametrization

Based on the reference scenario described in the

previous chapter, we now demonstrate how to calcu-

late the performance, based on the following couple

of assumptions: Users in the customer LAN access a

portal page at the web server. The clients’ network

as well as the network of the central office is a

100MBit Ethernet network, the WAN connection

between both networks is an FDDI network with

2MBit bandwidth. The documents requested from

the web server are assumed to have two different

sizes, i.e. 10kByte and 100kByte. During one hour,

1000 requests arrive (i.e. 0.27 requests per sec), 20%

of them asking for large documents. The request

itself sent from client to server has length 300 Byte.

Moreover we assume that the block length on the

web server’s hard disk is 2,048 kByte, with a mean

access time of 9ms, mean latency of 4.17ms and

mean transfer rate of 20MB/s (note that all values in

this paragraph correspond to actual specifications or

other real data). Requests and responses have to pass

two routers each, which currently are able to process

400,000 packets per sec. Finally, our observation

shows that the web server needs 5ms CPU time on

average per request and is able to process 10 re-

quests in parallel.

5.2 Standard Performance Evaluation

A straightforward calculation of the time which cli-

ent requests and web server responses need to cross

the network, taking the assumed technologies into

account, yields the following results: the 300 Byte

request sent by the client including IP headers and

Ethernet overhead requires a minimum of 6.529ms

before arriving at the web server. The network time

of the web server’s responses are different for the

two types of responses: the short response requires a

total of 0.024ms, whereas long responses need

0.069ms. Note that the main proportion of this re-

sponse time is caused in the WAN between cus-

tomer and corporate router, as is demonstrated in

Figure 4. Hence, the WAN between the central of-

fice and the branches can be identified as the bottle-

neck of the system. Further analysis shows that in-

creasing the FDDI bandwidth to a value of 8MBit/s

or more would resolve this problem.

As far as the service time caused by the client

request during access to the disk of the web server is

concerned, we assume that data on the hard disk are

PROGNOSTIC CAPACITY MANAGEMENT FROM AN IT SERVICE MANAGEMENT PERSPECTIVE

323

split into blocks of 2048 Bytes each. Furthermore,

the specification of an off-the-shelf IDE (Integrated

Drive Electronics) hard disk reveals that the mean

access time is 9ms, the mean rotation latency 4.17ms

and the data transmission rate equals 20MB/s at a

rotational speed of 7,200 rotation/min; the controller

time is 0.1ms. Based on these numbers, a straight-

forward calculation shows that the time required for

reading the data blocks from the hard disk equals

13,3 ms. In reality, we often find hard disks of the

type SCSI (Small Computer System Interface) oper-

ating at a rotational speed of 15,000 rotations per

minute yielding an average access time of only 3ms.

Figure 4: Response Times. Note that LAN1 refers to the

Customer LAN, LAN3 to the Corporate LAN and LAN2

to the WAN in between.

Summing up these values, we end up with a total

service time of 108.46ms for short responses and

806.76ms for long responses. Under the initially

assumed distribution of 80% vs. 20%, this finally

yields an average expected total service time of

457.61ms. Figure 5 demonstrates how this time is

distributed between disk access, network, routers

and CPU. For more details on the calculation we

refer to (Jirku 2008).

Figure 5: Total Service Times for Request Sizes of

10kByte, 100kByte and Average Size ( 80% vs. 20%).

5.3 Approximation with Little’s Law

The exact calculation of the total service time as

sketched in section 5.1 may become very compli-

cated and time-consuming in the case of large real-

life data sets. Therefore, we propose to use Little’s

Law for simplifying this process:

⋅

(5.1)

for an average number of requests N, average arrival

rate

and average processing time R.

According to (Haverkort 1998), Little’s Law can

be applied to a vast variety of queueing theory sce-

narios. We know that the web server handles an av-

erage number of 1,000 requests per hour, and that

the server is processing one request at a time. There-

fore, in this case (5.1) results in

350

360010000

===

(5.2)

as web server response time.

Both routers in our example have an average

throughput of

= 400,000 packets per second and

may process 2 packets in parallel, therefore we end

up with a total router time for the 2 routers in our

system of

400000

22 =⋅=⋅=

(5.3)

For the network, we can assume an arithmetic

average message length of 55kByte and thus end up

with

140

400000

55000

===

(5.4)

Finally, the corresponding CPU time is

27.0

===

(5.5)

Summing up these values, we derive the average

service time for a mean request size of 55kByte as

msRRRRR

CNRWtot

527=

(5.6)

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Figure 6: Total Service Times: Average vs Little’s Law.

On the other hand, averaging the precise values

for short and long requests as discussed in section

5.1 yields a mean service time of approx. 457ms

which is reasonably close to the approximation value

in (5.6). Therefore, we may safely conclude that

using Little’s Law for capacity calculations can lead

to very good results at a significantly lower com-

plexity than conventional calculation.

Figure 6 summarizes both approaches and dem-

onstrates that the approximation also depicts the

distribution of the total service time correctly. For

further details we refer to (Jain 1991) and (Jirku

2008).

6 CONCLUSIONS AND FUTURE

WORK

This paper discusses how to integrate the idea of

proactive capacity management into the current ITIL

framework. We have presented a list of requirements

both concerning the CMDB and the workload mod-

elling which have been derived from our long-time

practical experience with issues of IT Service Man-

agement in various companies. Our second contribu-

tion deals with efficiency of the required perform-

ance analysis, where we illustrate that “Little’s

Law”, which is well known from queueing theory,

can lead to a significant reduction of computational

complexity while still leading to sufficiently precise

results and predictions.

Knowing the capacity not only of single IT-

infrastructure related items but also external factors

like promoting new products, which may result in

more requests to ones IT resources, became more

and more vital to the role of today’s CIO (Schubert

2004). Being able to predict the capacity of networks

and/or systems, and relate different measures, errors

and faults to IT services their overall performance

will become a key part in IT capacity planning.

While the operational part of capacity planning will

remain to be quite computationally intensive, it even

will be a bigger challenge to translate the business

needs to measurable performance indicators, like if

the IT infrastructure is able to handle increased user

access due to a promotional campaign Therefore,

our main focus now is on algorithms and strategies

that relate the user experience of a slow IT service to

a poorly performing router or a slow hard disk re-

sponse because of fragmented disks, which have to

be developed and continuously improved to reflect

the real world. However, we believe that it is worth

this effort due to the huge resource saving potential

which ITIL-compliant prognostic capacity manage-

ment will provide especially in the areas of system

virtualization and “green IT”.

ACKNOWLEDGEMENTS

The authors would like to thank Thomas Sommer

and Christian Kaufmann for their continuous and

valuable support. Part of this work has been funded

in the framework of the Austrian Government’s

Kplus/COMET competence center program.

REFERENCES

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http://itil.technorealism.org/index.php

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frames to Client-Server Systems, Prentice Hall, Upper

Saddle River, New Jersey, 1994.

Jain, Raj, 1991: The Art of Computer Systems Perform-

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D. Zeng; Fei-Yue Wang; Mingkuan Liu, 2004: Efficient

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325