=Paper=
{{Paper
|id=Vol-2267/160-165-paper-29
|storemode=property
|title=On the subject of assessing the composition of IT services enterprise information systems via fuzzy sets theory
|pdfUrl=https://ceur-ws.org/Vol-2267/160-165-paper-29.pdf
|volume=Vol-2267
|authors=Alexandr B. Degtyarev,Gennady D. Dick,Alexander G. Dick
}}
==On the subject of assessing the composition of IT services enterprise information systems via fuzzy sets theory==
https://ceur-ws.org/Vol-2267/160-165-paper-29.pdf
Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
ON THE SUBJECT OF ASSESSING THE COMPOSITION OF
IT SERVICES ENTERPRISE INFORMATION SYSTEMS VIA
FUZZY SETS THEORY
A. Degtyarev 1, a, G. Dick 2, b, A. Dick 3, c
1
St. Petersburg State University, Russian Federation, 199034, St. Petersburg, University emb., 7/9,
doctor of technical Sciences, Professor
2
St. Petersburg State University, Russian Federation, 199034, St. Petersburg, University emb., 7/9,
doctor of technical Sciences, Professor, post-graduate student
3
St. Petersburg First Electrotechnical University "LETIβ, 197376, Russia, St. Petersburg, Prof. Popov
str., 5, bachelor
E-mail: a deg@csa.ru, b g.dick@prodick.ru, c alexdicks@mail.ru
One of the factors for the success of any enterprise on the modern market of production and services
makes the targeted use of mathematical and software computers, complexes and computer networks as
part of information systems (IS) of enterprises, as the basic principles of the Digital Economy of the
Russian Federation program states. This assumption is based on the fact that βthe effective
development of markets and industries (fields of activity) in the digital economy is possible only if
there are developed platforms, technologies, institutional and infrastructural environmentsβ [1].
Currently, the growing dependence of business processes on the quality and reliability of the
supporting information systems requires a systematic approach to automation, which is closely linked
to the issues of setting up both the enterprise IT architecture and the business architecture as a whole.
That allows us to consider the transition to service-oriented architecture (SOA). Such a method of
building up an IP complex is applied, then the support of the necessary business processes is carried
out by various combinations of IT services, which under this circumstance, in its turn, leads to the
need of creating diverse methods to evaluate and choose IT services (hereinafter, services) in
conditions of SOA [2].
Keywords: IT-service, composition, information systems, IT architecture, multi-criteria problem,
fuzzy sets theory, SOA.
Β© 2018 Alexandr B. Degtyarev, Gennady D. Dick, Alexander G. Dick
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
Studies have shown that the process of determining the necessary selection of services is
reduced to solving a multicriteria task of forming the composition of services according to specified
indicators, depending on their functional purpose [3]. In addition, usually the process of forming such
a composition adds a stage of the human-machine procedure for specifying preferences, which at the
same time would give sufficient flexibility in changing preferences when choosing services and
display understandable information about the causes and consequences of establishing certain
preferences, the so-called select [4].
At the same time, the choice of one or another composition of services necessary for the
functioning of an IP is significantly affected by various kinds of uncertainties that may be caused by:
1. Lack of information, with occasionally inaccuracy about alternative options for technical,
economic and other reasons.
2. Impossibility of conducting a large amount of research and evaluating the characteristics of
IP, which does not allow to establish a probabilistic model adequate to the chosen situation.
3. Various degree of expert confidence in assessing certain parameters of the considered
information systems, etc.
Such uncertainties significantly reduce the possibility of using deterministic or probabilistic
models. In this case, to evaluate the quantitative and qualitative characteristics of alternatives, it is
proposed to use the mathematical apparatus of fuzzy sets when choosing services. With the proposed
method of solving the above problem, the option of using linguistic scales based on trapezoidal fuzzy
numbers can provide advantages as follows:
1. Ability to take into account unclear requirements for the maintenance of business
processes.
2. The use of fuzzy sets allows decision makers and experts to conduct a flexible assessment
of quantitative characteristics in case of uncertainty considering errors or inaccuracies in
the assessment process.
3. The ability to reduce qualitative expert assessments to quantitative (fuzzy) making it
possible to simultaneously take into account quantitative and qualitative assessments in a
single model.
4. The use of a fuzzy linguistic approach, when the assessment is conducted in linguistic
terms, for example, "low quality", "acceptable quality", "high quality", etc., which are
generally accepted and more understandable by the decision maker
The basis for modeling the task of choosing IT services in the framework of the SOA for IP is
proposed to consider building a tuple in the form of:
, (1)
where πΏ = {ππ}, π = π, Μ
Μ
Μ
Μ
Μ
π, β set of business processes selected in the framework of an
enterprise strategy for automation using IP;
π = {ππ}, π = Μ
Μ
Μ
Μ
Μ
π, π, - set of software tools in the software (software) of the selected IP,
considered in the selection process as alternatives that implement the required functionality to
automate and support business processes X;
π = {ππ}, π = Μ
Μ
Μ
Μ
Μ
Μ
π, π, - set of services provided by various information systems Z, according to
the service-oriented approach;
G β set of criteria for assessing the quality of alternatives considered in the problem of
choosing services;
P β fuzzy estimates of IT services Y on a variety of criteria G;
R - set of rules defining the principles of comparing and ranking assessments of services Y
based on their assessments P;
W β restrictions reflecting the goals and preferences of decision makers in the task of
automating business processes X;
K β optimal decision criterion, which determines the rules for choosing services based on their
P estimates, subject to constraints W.
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
The formal formulation of the problem of choosing services under SOA conditions consists in
finding such a set of IT services Y* = {yj1, yj2, β¦, yjm }, which will provide the necessary support for
business processes
Μ
Μ
Μ
Μ
Μ
πΏ = {ππ}, π = π, π in accordance with the rules of comparison and selection of alternatives R
taking into account the determining optimality criterion K.
In this case, the problem of modeling the choice of IT services in the formulation (1) using the
theory of fuzzy sets can be formulated as follows - based on fuzzy requirements for the maintenance
of business processes X and fuzzy information about the parameters of IT services Y it is necessary to
develop fuzzy models for assessing the quality of IT services based on the accepted criteria structure G
and forming a model for choosing IT services based on the optimality criterion K.
When solving the problem of assessing the quality of services as an integral part of IP, quality
should be understood as the completeness of properties and characteristics that provide the ability to
meet the stated or implied needs of the enterprise (business functions).
Using the the structure of criteria for assessing the quality of information systems in
accordance with the standard βGOST R ISO / IEC 9126-93β is proposed [5]. This is where the
integrated assessment is based on six quality factors: functionality, reliability, usability, efficiency,
maintainability, portability (or mobility). Each of the six factors is defined in more detail by using
separate criteria and subcriteria that form the hierarchical structure of the quality criteria. At the lowest
level of the given criteria structure there are metrics by which the subcriteria of the lowest level are
measured..
To calculate the integral value of the quality criterion based on the values, it is proposed to use
the hierarchy analysis method proposed by T. Saati, which allows you to calculate the evaluation of
ππ π π π
the higher criterion ππ = βπ=π ππ ππ , as convolution of subordinate subcriteria ππ , l={1, 2, β¦ lj} and
π
their weights ππ , calculated by pairwise comparison of the significance (weight) of each of the
subcriteria [6].
At the same time, the proposed fuzzy model of quality assessment does not take into account
the fact that experts may not be sure which of the fuzzy values of the linguistic scale can accept
estimates of alternatives according to individual criteria, which we define as confidence factors. In this
case, as factors of confidence, we can consider the characteristics of utility, significance, availability,
performance, integration with other applications, fault tolerance, etc.
The most important feature of the problem under consideration is that if there is a certainty
factor π, for each alternative π³π€ there are several ratings for this criterion ππ(ππ, ππ), π ={1, 2, β¦ ππ },
under different conditions of the external environment or various external factors β ππ. Thus, the
choice of an alternative does not lead to an unambiguous result in the process of evaluating.
To evaluate alternatives based on confidence factor the method of calculating the generalized
value of the alternative estimate ππ on the confidence factor π is applied:
ππ
Μ ππ ππ (ππ, ππ) + (π β πΆ) π¦π’π§{ππ (ππ, ππ)} ,
ππππ (ππ) = πΆ β π (2)
π
π=π
where,
πΆ β [π; π] β coefficient reflecting the level of pessimism-optimism of the decision maker
regarding the development of the situation. When πΆ = π (pessimistic variant) the external
environment behaves in an antagonistic manner, and the criterion takes the minimum possible value in
the rating scale; case πΆ = π (optimistic variant) the point estimate of the generalized criterion is made
on the basis of the Bayes optimality criterion; whenπΆ β (π; π) estimated intermediate between fully
optimistic and completely pessimistic variant;
π¦π’π§π {ππ(ππ, ππ)} β - the minimum value in the rating scale by criterion π;
ππ (ππ, ππ) β possible values of the fuzzy scale of assessment of the confidence factor g,
depending on the state of the external environment ππ,
Μ π π β point a priori estimates of the "probability" that the evaluation of an alternative ππ will
π
take value ππ (ππ, ππ) on a criterion rating scale π.
To calculate the coefficient sπ Μ π π the method of constructing the weight coefficients of
Fishburn from the theory of utility is used. To reach this, on the basis of expert estimates, it is
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
necessary to streamline the values of the criterion assessment scale π according to which of the values
is more likely when each alternative is evaluated ππ . Point estimates of prior probabilities are defined
as follows:
πππ
Μ π π = ππ
π , (3)
βπ=π πππ
where
ππ , πf ππβπ β ππ
πππβπ = { ππ , πππ = π, π = ππ , (ππ β π), β¦ , π. (4)
ππ + π, πf ππβπ β» ππ
Attitude ππβπ β» ππ means that, according to experts, the probability of an event ππβπ higher ππ.
Attitude ππβπ β ππ means that, according to experts, events ππβπ and ππ equally likely.
Considering the optimality criterion K for building up a model of IT services, it is assume that
when making a decision on the choice of services, the decision maker is guided by the cost parameters
of the presented set of services. In this case, as an option, for solving the problem of estimating the
total cost of IT services, it is advisable to use the total cost of ownership model using fuzzy estimates,
since it most fully describes the cost structure associated with the acquisition and operation of IT
services in the enterprise [7]. In this case, as an option, for solving the problem of estimating the total
cost of IT services, it is advisable to use the total cost of ownership model using fuzzy estimates, since
it most fully describes the cost structure associated with the acquisition and operation of IT services in
the enterprise:
π»πππͺπππ(ππ ) = πΊπ»πͺπΆ(ππ) + β π»πͺπΆ(ππ)
(5)
ππ βππ
where,
πΊπ»πͺπΆ(ππ) β total cost of ownership of basic IP ππ , which includes IT services;
π»πͺπΆ(ππ ) β incremental costs related to IT service ππ.
Then, an algorithm based on the calculation of the extended Lee-Wong parameter is used to
compare and rank fuzzy trapezoidal estimates of the total costs of IT services. When determining the
Lee-Wong parameter for a set of fuzzy numbers A1, A2, β¦, An common carrier is determined sup(A),
so that βπ¨π: ππππ(π¨π) β ππππ (π¨) = {π¦π’π§π=πΓ·π(πππ ) ; π¦ππ±π=πΓ·π (πππ)}. The base is defined on
this carrier - fuzzy number V, with continuous convex membership function Β΅V(x). Next for each Ai
according to the formula (7) Lee-Wong parameter determined π³πΎ(π¨π > π½), based on which the
ordering of fuzzy numbers:
+β π
β«ββ β«ββ Β΅π¨π (π)Β΅π½(π)π
ππ
π
π³πΎ(π¨π > π) = +β +β (6)
β«ββ β«ββ Β΅π¨π (π)Β΅π½(π)π
ππ
π
Choosing a fuzzy number V when calculating the Li-Wong parameter suggests various
scenarios for the behavior of the external environment depending on a priori awareness of decision
makers.
In order to take into account a priori awareness of decision makers about the possible behavior
of the external environment during a fuzzy estimate of costs, the generalized Lee-Wong criterion
should be applied [8]
π³πΎπππ(π¨π) = πΆπ³πΎ(π¨π > π½πππ) + (π β πΆ)π³πΎ(π¨π > π½πππ ), (7)
where,
π³πΎ(π¨π > π½πππ) and π³πΎ(π¨π > π½πππ ) Lee-Wong coefficients for optimistic and pessimistic
estimates of fuzzy numbers π¨π, respectively;
Ξ± β coefficient reflecting the level of pessimism-optimism of the decision maker regarding the
development of the situation. When πΆ = π estimated pessimistic option, in case πΆ = π the optimistic
option is estimated, and in the case of πΆ β (π; π) estimated intermediate between fully optimistic and
completely pessimistic options.
The presented models for assessing the quality and total costs of IT services make it possible
to specify the task of forming the composition of services as follows.
As to statement (1) it is necessary to determine:
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
- a set of business processes πΏ = {ππ}, π = π, Μ
Μ
Μ
Μ
Μ
π, which should be automated as part of the
implementation of the IT strategy of the enterprise;
- a set of information systems π = {ππ}, π = Μ
Μ
Μ
Μ
Μ
π, π, considered in the selection process;
Μ
Μ
Μ
Μ
Μ
Μ
- a set of IT services π = {ππ }, π = π, π, provided by information systems Z;
- a set of fuzzy requirements for the quality of the implementation of business processes
πΎ = {ππ}, π = Μ
Μ
Μ
Μ
Μ
π, π.
As a result, it is necessary to form such a set of IT services Y* and information systems Z*,
within which these IT services will be implemented so as to provide the necessary level of quality
support for all business processes X, and also, that this set should have a minimum estimate of the total
cost of ownership of the selected IT services. It should be noted that the formulation of the problem of
choosing IT services leads to the construction of the display of many business processes X = {xi} to a
variety of information systems under consideration Z = {zk}.
Thus, considering the uncertainties arising in the process of evaluating and selecting IT
services for the use of the mathematical apparatus of fuzzy numbers makes it possible to:
Build up a fuzzy model for assessing the quality of IT services, taking into account the
preferences and a priori awareness of the decision maker of the possible behavior of the external
environment, when assessing the factors of confidence. The model is per standard βGOST R ISO / IEC
9126-93β alongside with the hierarchy analysis method.
Develop a fuzzy model for estimating the total cost of IT services, taking into account the
incremental costs of acquiring individual services. The use of this model in conjunction with the
developed algorithm for comparing and ranking fuzzy numbers allows for a more flexible and accurate
selection of IT services with minimal total cost, considering decision makersβ preferences.
Create a model for choosing IT services in terms of a service-oriented architecture, which,
based on fuzzy assessments of the quality of IT services and the total costs of their acquisition and
operation, allows you to define a set of IT services that meets the required quality levels of business
process support with minimum total cost estimate.
An example of the practical implementation of the research can serve as measures for the
selection and justification of IT services in the construction of the IP complex a number of logistics
companies. This process is based on a methodology consisting of the steps as follows:
Stage 1 β formation of a list of alternatives to the task of choosing IT services and determining
the structure of criteria for assessing the quality of IT services.
Stage 2 β fuzzy assessment of the integral quality provided by IT service providers. A fuzzy
assessment is made by an expert method based on the linguistic scale.
Stage 3 β definition of cost items and the calculation of the total cost of IT services, according
to (5).
Stage 4 β set of IT services that meets the required levels of business process support with a
minimum estimate of total costs.
Thus, the use of the considered models and tools allows to create a set of IT services meeting
the required levels of quality support for business processes with minimum estimate of total costs of
those IT services for various cost items (purchase of equipment, software licenses, cost of software
development, implementation and integration of software, staff training, updating and maintenance of
equipment and its software versions, training new employees, etc.). The result is presented in the form
of software developed for practical use for computers, complexes and as well as computer networks
used by the transport logistics system.
References
[1] Order of the Government of the Russian Federation of July 28, 2017 No. 1632-p βDigital Economy
of the Russian Federationβ.
[2] Heather Kreger Vince Branssen. Standards for service-oriented architecture // IBM Developer
Works. β 2013. β URL: http://www.ibm.com/developerworks/en/library/ws-soa-standards/.
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
[3] Reference Architecture Foundation for Service Oriented Architecture Version. - The Organization
for the Advancement of Structured Information Standards (OASIS), 2012. β URL: http://docs.oasis-
open.org/soa-rm/soa-ra/v1.0/soa-ra.html.
[4] Dick G.D., Degtyarev A.B. Application IT-services information system based on SOA to improve
transport logistics efficiency. //Proceedings of Saint-Petersburg Electrotechnical University Journal
(Izvestia SPbETU βLETIβ), N5, 2014, pp.17-24 (in Russian).
[5] GOST R ISO / IEC 9126-93. Information technology. Evaluation of software products. Quality
characteristics and guidelines for their use. Introduction 1994-07-01. Reprinted 2004. β M.:
Standardinform, 2004. β 12 p.
[6] Saaty T. Decision Making. Hierarchy analysis method. - M.: Radio and communication, 1993.
[7] Zates A.V. Fuzzy cost model in the framework of a service-oriented approach to the architecture of
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[8] Kwang H.L., & Lee J.H. (1999). A method for ranking fuzzy numbers and application to decision
making. IEEE Transactions on Fuzzy Systems, 7 (6), 677-685.
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