Information Systems Conﬁguration Analysis using

Event-driven Computer Simulation

Tomasz Walkowiak and Katarzyna Michalska

Institute of Computer Engineering, Control and Robotics

Wroclaw University of Technology

Janiszewskiego 11-17, 50-372 Wroclaw, Poland

Abstract. The paper presents a method of analyzing dependability aspects of

service oriented information systems. The system analysis is based on functional-

dependability model suitable for system simulation. The model is described by

XML Domain Modelling Language (XDML). The model is automatically trans-

form into an input model for computer simulation. For event-driven simulation

modiﬁed SSFNet simulator is used. Based on simulation results some depend-

ability metrics are calculated: availability and response time of a business ser-

vice. The paper presents service oriented information system model, method of

its analysis with support of created tool that was tested on a exemplary system.

1 Introduction

Service oriented complex information system [3] infrastructure is nowadays the core of

a large number of companies. Everybody expects that product, service or information

will be available to each request no matter where they are. That is why service conti-

nuity is particularly important for each company since clients do not tolerate service

unavailability - in case of service problems, they go to another company. This cause

a strong trend to create complex information systems more safe, efﬁcient and depend-

able. Still those systems have to be very ﬂexible in case of economic factors. It is very

important to control company’s costs and maximal usage of technical infrastructure re-

sources. To do that there is a need of analyzing various system conﬁguration and chose

the optimal solution taking both aspect (technical and economical one) into considera-

tion.

In this paper we propose a technique of using system simulation and modelling lan-

guage [2], to face business service continuity problem and specify conﬁgurations that

can be optimal for the company.

2 Modelling

Service oriented complex information system (SOCIS) aims to fulﬁll user needs in

case of its request. In this kind of systems, user request mainly perform by business

service level of the system [5]. In this case we can speak about speciﬁed queue for

Walkowiak T. and Michalska K. (2009).

Information Systems Conﬁguration Analysis using Event-driven Computer Simulation.

In Proceedings of the 7th International Workshop on Modelling, Simulation, Veriﬁcation and Validation of Enterprise Information Systems, pages

162-167

DOI: 10.5220/0002202601620167

 SciTePress

the task realization (choreography) since each task is done using one or more service

components that interact with each other. Service is being done on a technical ground

of the system - technical infrastructure. We can describe service oriented system as a:

task specify by the user, technical infrastructure of the system, service working on the

system, system user, time parameters of the system and task.

Since in the service oriented complex information system we have to focus on many

different and complicated aspect of the system to model it, we propose one common de-

scription language that is called XDML - XML Domain Modelling Language. Proposed

language is a representation the SOCIS model based on XML format. It consist of: sys-

tem topology,network conﬁguration, static and dynamic part of the service description,

description of the users and their behaviours, hardware/software failures, the security

functionality aspects of each node (i.e. ﬁrewall rules).

The XDML is deﬁned by XML schema. Moreover, an Integrated Analysis Envi-

ronment (IAE) tool was developed by authors to allow a graphical way of modelling

information system. A user is able to model any system topology, having a graphical

view of its hardware and software components. Each parameter of XDML model could

be is accessed by the IAE interface. XDML service model realize two kinds of task

(local and external). The main purpose of designing of XDML was to able to perform a

analysis of the modelled information system using simulation approach.

3 System Analysis

There are various method for system analysis with concern of different observation of

the system. One of this methods is computer simulation [2], [6] that performs system

behaviour using its model (represented as a input model). Simulation has also some

disadvantages e.g. labour intensity of mode creation, simulation results can be hard to

interpretation. Still this solution seams to be appropriate to analyze complex informa-

tion systems that are based on event driven behaviour. Simulation tool for this kind of

events are mostly computer network simulators.

Experiments reported in this paper were performed using the SSFNet simulation

environment [4]. SSFNet is a network simulator developed by the Renesys Corporation

with support from DARPA. We have selected SSFNet among other network simulators

since models of protocols and network elements are open-source. Therefore, it allows

modiﬁcation of the code. Moreover, we use Java based version of SSFNet, because it

allows much faster development.

SSFNet simulator consists of three major parts: SSF engine, Domain Modelling

Language (DML) [4], and SSFNet models. The SSF (Scalable Simulation Framework)

is public-domain standard for discrete-event simulation. Scalable Simulation Frame-

work is a base for higher level - the SSFNet. SSFNet module is a collection of Java

packages for modelling and simulation of networks and Internet protocols. Moreover

SSFNet uses public-domain standard called DML to conﬁgure simulation scenarios.

An extension to the simulator was developed It includes support for trafﬁc genera-

tion (models of user behaviour with randomness), simulation of business level services,

implementation of resource consumption and fault model. Errors were introduced in

different levels (link, network adapter and software component failures). It was as-

163

sumed that a failed element ceases to operate completely. The results of component

failing are observable at the service level. For example some requests/responses are

being delayed or lost. Adding new functionality required the extension of DML used

in standard SSFNet version. Moreover, SSFNet was extended to perform Monte-Carlo

approach. Results of simulation are recorded in XML based output ﬁle. It includes in-

formation (name of the service, time) of user requests and service responses. It is a base

for calculating dependability metrics.

4 Dependability Metrics

In [1] authors described basic set of dependability attributes. This is a base of deﬁning

different dependability metrics used in dependability analysis of computer systems and

networks. In this paper we would like to focus on more functional metrics which could

be used by the operator of the information system.

For the needs of this research we provided two metrics of information system de-

pendability: business service availability and response time. Due to a randomness of

a user behaviour the calculation of these metrics was done based on Monte-Carlo ap-

proach by repeating simulation of the same system N times over analyzed period T .

Therefore, all deﬁned below metrics are calculated as an average over all batches of

simulation.

Since availability in mostly understood as a probability that request send from user

to the systems are being served properly, therefore, the business service availability can

be computed on the basis of observed system events, taking into consideration N probes

of simulation with analyzed period T :

SA =

i=1

(1)

whereas t

is a the time of business service being working in i-th simulation (since

we are looking on the system form the client perspective, therefore, we assume that

business service is working if and only if system responds to the client in a proper

way).

The second proposed metric is the business service response time. It is calculated

as an average delay between the starting time of user response (t

i request

) and getting

answer (t

i response

) from the business service (i.e. only requests that were properly

answered are taken into account).

SRP =

N requests

i=1

i response

− t

i request

(2)

This metric is not directly inﬂuenced by failures of any system hardware or software

component. In case of long failures of the service the calculated metric could become

random due to a low number of properly answered requests, for service being failed

over all simulated time the sub service response time is not deﬁned. In case of SO-

CIS systems SRP value is strongly bonded with computational power, resources and

overload of the system technical infrastructure.

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Fig.1. Testbed - system view.

Client ApacheComponent DNSComponent DBComponent

GetMainPage(req)

GetMainPage(resp)

GetDnsAddress(req)

GetDnsAddress(resp)

JobsList(req)

JobsList(resp)

NewsList(resp)

NewsList(req)

GetJobsData(req)

GetJobsData(resp)

GetNewsData(req)

GetNewsData(resp)

ShowMainPage

Fig.2. Testbed - scenario view.

5 Test Case System

For the case study analysis we propose an exemplar system illustrated in Fig. 1. The

system is composed of tree networks: one is a client network (marked as intranet), other

are service provider networks (one primary and second backup one). Essentially the

test-bed system implements tree main service components: ”GetMainPage”, ”JobsList”

and ”NewsList” that can interact with each other as shown on Fig. 2.

The described system was modelled in XDML using IAE tool (ﬁgures 1 and 2 are

screenshots from IAE application). Two conﬁguration were analysed. The only differ-

ence between them is allocation of technical service realising ”DNSComponent” (see

scenario on Fig. 2).

In the ﬁrst conﬁguration (named ”conﬁg1”) the ”DNSComponent” is allocated on

”DNS-Server” (see Fig. 1) whereas in second (”conﬁg2”) the service component is

allocated on ”BackupDNSServer” which is assumed to be a much slower host then

”DNS-Server”. The achieved results, i.e. business service response time, for a different

number of concurrent clients are presented on Fig. 3. As it was expected the response

165

Fig.3. Business service response time in a function of number of clients for two different conﬁg-

urations.

time in a second conﬁguration is longer than in the ﬁrst one. For one client the difference

in response times of these two conﬁgurations is equal to a difference in an execution

of ”DNSComponnet” on the ”DNS-Server” and ”BackupDNSServer” host. It could be

estimated without any simulation tool. However, when the number of clients enlarges

the situation becomes harder for a human being to be analysed. Enlarging a number of

clients enlarges a number of concurrent executions of a given service component, what

causes the enlarge of service component execution time. The effect of enlarging the

execution time is not linear in a function of number of clients since longer execution

times cause a larger number of concurrent executions what enlarges execution times

again. This is a kind of a feedback effect. However it has a limitation. As it could be

noticed on Fig. 3 the execution time for a very large number of clients (500-1000) stops

to increase so fast as for a medium number of clients (50-500). The reason of this effect

is due to a limitations of number of concurrent executions on each host (maximum

number of threads in technical service) and a time-out effect (each execution of service

component has a time-out after each the execution of a given service component is

assumed to be failed). The effect is more understandable looking at on Fig. 4, which

presents the estimation of availability of the business service in a function of number of

clients for two analysed conﬁgurations. The mentioned before time-out and a maximum

number of thread effect results in dropping some requests and therefore decreases the

availability parameter.

6 Conclusions

We have presented a simulation approach to an analysis of service oriented complex in-

formation system. Developed software allowsto analyze different dependability metrics

of the system in a function of model parameters, like for example a number of clients or

different conﬁgurations (allocations of service component on different hosts). The pre-

sented results - business service availability and response time in a function of a number

of clients for two different conﬁguration show the possibilities of presented approach

and developed software (Integrated Analysis Environment with extended SSFNet simu-

166

Fig.4. Business service availability in a function of number of clients for two different conﬁgu-

rations.

lator). The tool could be used for example for a selection of the best (according to some

metric) conﬁguration among a large set of them. Moreover, a proposed approach helps

a human being to understand the complex information system behaviour. A usage of

graphical interface (IAE) integrating whole modelling and analysis process makes the

tool useful, therefore, the presented approach allows to improve a process of designing

and administration of service oriented information system.

References

1. Aviienis, A., Laprie, J., Randell, B.: Fundamental Concepts of Dependability. 3rd Information

Survivability Workshop (ISW-2000), Boston, Massachusetts, USA (2000)

2. Birta L., Arbez G.: Modelling and Simulation: Exploring Dynamic System Behaviour.

Springer London (2007)

3. Gold N., Knight C. , Mohan A., Munro M.: Understanding service-oriented software, IEEE

Software, vol. 21 (2004) 71– 77.

4. Nicol, D., Liu, J., Liljenstam, M., Guanhua Y.: Simulation of large scale networks using SSF.

Proceedings of the 2003 Winter Simulation Conference, Volume 1, 7-10 December 2003,

(2003) 650–657.

5. Michalska K., Walkowiak T.: Hierarchical approach to dependability analysis of information

systems by modeling and simulation. The Second International Conference on Emerging Se-

curity Information, Systems and Technologies (SECURWARE 2008), Cap Esterel, France,

25-31 August 2008, IEEE Computer Society [Press], (2008) 356–361.

6. Sheikh A., Ajeeli A., Abu-taieh E.: Simulation and Modeling: Current Technologies and Ap-

plications. IGI Global (2007)

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