An Approach for Automated Service Selection and Ranking using
Services Choreography
Firmino Silva, Claudia-Melania Chituc and Paul Grefen
Eindhoven University of Technology, Eindhoven, The Netherlands
Keywords: Service Selection, Service Ranking, Service Choreography, SOC, Adaptive System.
Abstract: In today’s highly competitive market, it is critical to provide customers services with a high level of
configuration to answer their business needs. Knowing in advance the performance associated with a
specific choreography of services (e.g., by taking into account the expected results of each component
service) represents an important asset that allows businesses to provide a global service tailored to
customers’ specific requests. This research work aims at advancing the state-of-the-art in this area by
proposing an approach for service selection and ranking using services choreography, predicting the
behavior of the services considering customers’ requirements and preferences, business process constraints
and characteristics of the execution environment.
1 INTRODUCTION
Changing market conditions, increased competitive
pressure, and growing demands and expectations of
clients make companies rethink the way they
perform business to provide customers with services
that offer a high level of configuration in order to
parameterize their particular needs. Allowing
customers to configure the requested service(s) and
providing an accurate estimation of the results (e.g.,
in terms of cost, time to complete the service, level
of quality) is of great importance to them. However,
designing, developing, deploying and monitoring a
system to support such characteristics is challenging.
Service-oriented computing (SOC) and cross-
organizational business processes provide the means
to build and run dynamic business environments
addressing the constantly evolving customers’
requirements (Papazoglou et al., 2006) (Di Nitto et
al., 2009) (Wetzstein et al., 2010).
The work-in-progress presented in this paper
aims at advancing the state-of-the-art in this area by
proposing a method for service selection and ranking
using services choreography, predicting the behavior
of the services considering customers’ requirements
and preferences, business process constraints and
characteristics of the execution environment. Based
on previous work (Silva and Chituc, 2013), the
proposed approach relies on a mechanism for
monitoring different metrics measured at different
levels of the choreography, considering the prospect
of satisfaction of the customer and provider. We
enhance our existing framework by including a
control mechanism based on closed life cycles
(Hellerstein et al., 2004) derived towards productive
responses. This mechanism enriches the historical
collection of results of past events to estimate,
through decisional elements, the behavior of the
choreography. The control mechanism follows a
hierarchical model that consists of three different
levels of control responsibility: strategic, tactical and
operational level, considering the functional scope of
each framework element (Kaplan and Norton, 2008).
This work contributes to provide a conceptual
adaptive (Landau et al., 2011) framework of services
that ensures a high degree of predictability for the
services’ choreography
This research project started with an extensive
literature analysis where relevant topics for this
research project were selected. In-line with the
results of the analysis pursued, a framework was
designed, which aims at supporting the selection of
the most suitable set of services available at a given
time to answer customers’ requirements and
preferences, taking into consideration business
process constraints and the characteristics of the
execution environment. The paper is organized as
follows. The adaptive framework for automated
service selection and ranking using service
choreography is presented in Section 2. The matrix
259
Silva F., Chituc C. and Grefen P..
An Approach for Automated Service Selection and Ranking using Services Choreography.
DOI: 10.5220/0005461202590266
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 259-266
ISBN: 978-989-758-106-9
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Adaptive framework schema - adapted from (Silva and Chituc, 2013).
The matrix of services used for ranking the services
is described in Section 3. Related work is discussed
next. The paper concludes with a section
summarizing the results and addressing the needs for
future research.
2 ADAPTIVE FRAMEWORK
The framework presented in this paper (Figure 1) is
an enhancement of the approach presented in (Silva
and Chituc, 2013). It consists of four main modules
and two levels of repositories:
Basic Application Setup module. This
module prepares and defines the basic structure of
the information introduced by a customer. The
interpretation of the criteria and preference
parameters chosen by the customer, the
identification of the needed services and metrics
requirements are initially mapped for use by other
modules.
Core module. This module centralizes a set of
core elements for handling the information collected
by customer input and conjugates it with existing
knowledge in order to subsequently trigger actions
for the implementation of the requested service. It
consists of four sub-modules that process guidelines:
customer, services, SLA and metrics.
Choreography Engine Setup module. After
the identification and selection of the services better
positioned in the ranking matrix, this module will
gather the data required to assemble and instantiate
the choreography.
Monitoring and Assessment System module.
The definition and mounting of the dynamic event
based monitoring and assessment mechanism, in
order to measure the selected metrics upon the
service acquired by the customer, is instantiated by
the elements of this module.
Central Operational Repository. It stores
information that allows the daily management of all
the modules of the framework, such as: customer
data management (e.g., criteria and preferences),
service metrics mapping for a specific customer
request, or the data structure composition for
monitoring and assessment system assembly.
Knowledge repository. It stores information
reflecting results from various runs from customer
requests, e.g., metrics assessments and choreography
execution results. This data is collected and
organized to enrich the knowledge in the framework.
2.1 The Hierarchical Control Model
As the basis of the control model, we use the
hierarchical structure of three levels of control:
strategic, tactical and operational (STO) (Mintzberg
et al., 1996) (Ackoff, 1990) and the Balanced
Scorecard (BSC) (Kaplan and Norton, 1992, 1996,
2008). The BSC model is an example of a closed-
loop controller applied to the management of the
implementation of a strategy. Basically, a closed-
loop control (Hellerstein et al., 2004) is where actual
performance is measured, the measured value is
compared to an expected value and based on the
difference between the two actions are taken as
required.
Based on the STO principles and the BSC
approach, a central hierarchical structure has been
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identified that is favoured by a closed-circuit of
interactions between policy development and
practical implementation. Isolating the central core,
the scope to evolve this research work was defined,
creating an analogy with overlapping layers of
responsibility in a pyramidal shape, allowing to
compare the reference STO and BSC models with
the approach described in (Silva and Chituc, 2013).
Figure 2 illustrates the layers of the proposed
hierarchical pyramid.
Figure 2: Adopted control model.
The basic execution of this hierarchical structure
is driven by a closed life-cycle that adds knowledge
and autonomy each time a choreography is invoked.
The life-cycle is based on two steps: firstly, by
executing elements at the operational level, a
response to customer requirements is produced with
the performance desired by a customer; secondly, by
considering the results of past events, the behaviour
of the choreography that will address customers’
expectations is estimated, considering the decisional
elements residing in the strategic and tactical levels.
2.2 Control Levels and Role Definition
This section describes the roles of each control level
for the proposed framework, given its functionality
and contribution to the overall performance of the
framework. The alignment chosen in order to
address the roles of responsibility is the hierarchical
model of Figure 2.
Hierachical levels differentiate from each other
according the dimensions: content (level of detail of
information), time extension (decision influence in
time) and scope (impact of the decision) (Mintzberg
et al., 1996) (Ackoff, 1990). Elements belonging to
the Strategic block have greater range both in terms
of scope of influence as in terms of duration of that
impact. In order to prepare structures and guidelines
for future implementation, elements of the Tactical
block are focused on more detailed information to
identify the module or specific area of competence,
what needs to be implemented and how the
implementation is made. The elements of the
Operational block require a critical level of
information detail to process and execute services
considering customer requirements and preferences.
Strategy tends to flow from the top down and is
concerned with long-range objectives and ways of
pursuing them that affect the system as a whole. The
strategy formulation requires examining where and
how the status is now, determining where we want
to go, and then determining how to get there
(Ackoff, 1990). The main focus of strategic
configurations is to growth and enrich the
knowledge. Thus, the definition of objectives, rules
and basic principles (policies and orientations)
resides at this level of the pyramid. The elements of
the framework that allow configurations that affect
the behaviour of the whole framework belong to this
level. Each of the sub-modules of Core module
addresses each of the most relevant area of the
framework: Customer, Metrics, Services and SLA.
These areas of competence are crucial in managing
configurations that influence the performance of the
framework.
The tactical segment is concerned with shorter-
run goals and means for reaching them. Tactics
involve the intermediary steps needed to achieve the
strategy vision. In this case, the role of these
elements is focused on converting the strategic
settings (at the highest level) in each of the areas of
the framework, either at the level of the services
modules (choreography), as at the level of the
metrics modules (monitoring and assessment). All
the elements which enable the setups and settings
oriented to customer requests based on the
configurations of the elements of strategic level
should belong to this level. The elements included
here (e.g., criteria and preferences parameters
management, metrics tree definition, service ranking
matrix, dynamic choreography setup) are related to
the preparation of the conditions, based on the
strategic definitions, to be implemented at the
operational segment.
The operational level focuses on the systems and
procedures to provide the immediate response to the
definitions and configurations previously aligned
and become operational in this segment of the
pyramid. All the elements linked with the
instantiation of actions involving the implementation
AnApproachforAutomatedServiceSelectionandRankingusingServicesChoreography
261
of a specific request received from a customer, at the
level of each service, as the evaluation metric of
each service, as well as the instantiation of a
choreography should belong to this segment, e.g.,
service requirements identification, metrics
definition and assessment. The duration of each
activity is sized by the instantiation of the service or
choreography and ends when the activity is
completed (different compared to previous layers).
2.3 Life-cycle Controls
By linking internally and externally the decisional
and operational group of elements (as portrayed in
Figure 3), cycles are designed to support the flow
the information generated in each instance of each
choreography and contributes to increase the
knowledge associated with the framework, making it
autonomous and adaptable, with higher reliability to
future requests.
Decisional blocks comprise elements which can
affect (e.g., with their configurations and setups) the
whole or part of the framework behaviour.
Operational blocks include operational elements of
the framework related to specific actions. Each
block (Strategic; Tactical; Operational) comprises
input data, decisions and target actions, and output
results.
Input data. Each framework elements receive
information to be processed internally. Input data
is received by two types of cycles: internal and
external. The internal data flows are those between
elements belonging to the same block, which allow
feeding different competences. The external life
cycles derive from interactions between blocks.
They feed the results of the elements of the blocks
positioned hierarchically below and contribute to
the development of new processing at higher and
adjacent blocks.
Decisions => Target actions reflect decisions
and resulting actions, e.g., the definition of a
specific metrics tree for a given customer request
is decided upon criteria and preferences, and as a
result of that a structured metrics tree is build
according to customer needs.
Output results. Each block receives, as input
data, output results from lower blocks. In addition
to the information they produce (internal output
results), they support the creation of new
information, e.g., a specific metrics tree structure
which responds to a given customer request,
should empower the knowledge of each block so
that subsequent interactions are optimized.
Figure 3: Control dynamic life-cycles.
3 POOL OF SERVICES
3.1 Elements of the Services Oriented
Sub-Module
This section describes how services are organized in
individual sets (functional pools), the procedures to
manage those pools, and the selection of a service to
be integrated in a specific choreography.
The Services Oriented sub-Module (Figure 4)
supports these activities. It includes elements of
relevance for the composition of services and their
integration in the choreography. A brief description
of each element is below:
Generic Services Manager: This element
identifies the needed characteristics as
catalogues of each of the services according to
the definitions and requirements of the
business process rules. Those characteristics
will be fundamental to build specific pools
where services with the same characteristics
will compete.
Services Procurement: Searching and
identification of services needed to meet the
objective outlined by the client are managed
by this element.
Services Ranking Matrix: The functional
scope of each service is defined to add each
service in the same pool to "compete" in terms
of performance within the same type of
functionality. Each pool of services is
associated to a matrix that stores the ranking
of services. Scoring algorithms are then
executed to calculate the rating for each
service, according to customer preferences.
Selection of Services: The needed services
with the best performance indicators are
chosen from the ranked pools databases. The
algorithm to classify services’ performance in
previously interactions support the service
choreography engine so that it can
dynamically build the services better ranked.
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This sub-Module actually compiles a list of
generic services necessary to address customers’
requirements and preferences, manages services and
calculates a ranking matrix, based on pools which
collect features and services with identical
functionalities; identifies and locates services that
match the needs of the choreography requested by
the client (this function of procurement is crucial to
match the characteristics of the services to be
integrated in choreography); identifies and selects
services whose indices ranking are better positioned
to integrate the choreography as potentially giving
the best response to the customers’ request.
In the Basic Application Setup Module, the basic
structure of the information inserted by customer is
prepared and defined, to be later used by other
modules. Based on the Business Process Rules (from
the Core module) and the available Generic Services
Directory (from the Core module), the element
Services Requirements Identification is responsible
to create and identify a Generic Services
Requirements List. This list will be used to identify
the set of services that will be mapped to the matrix
in order to select the best ranked services with those
characteristics.
Figure 4: Services oriented sub-Module schema.
An activity of a business process may
correspond to a sub-process, with a specific set of
attributes and requirements for which a pool of
services is identified.
The business process to be assembled (e.g.,
considering pre-defined business rules) includes all
the services that will be executed and contribute to
accomplish the customer’s request. Thus, as the
customer selects the overall service - composed of
services provided by business partners, a reference
to each of these services is added to the global
business process with a set of requirements to
identify the specific pool of services.
3.2 Ranking of Services
Each pool of services has associated a matrix
(Figure 5) that is built to determine the ranking of
the services for a specific function. The matrix
accumulates over time the result of measurement of
metrics, from all the iterations resulting from their
use in choreographies, and is managed so it can be
adapted to the customer request by assigning
weights (Silva and Chituc, 2013).
The weights correspond to the customer's
preferences. Each service has a set of metrics to
assess its performance. The weights allow these
assessments to be measured at a given time. Thus,
the matrix rank can be oriented to what the customer
business needs. Different scenarios may be
constructed generating different matrices.
In Figure 5, three services are illustrated
(services A, B, R), and metrics associated to each
pool of services are referred (e.g., m1, m2, m3, mp).
Each metric supports a set of values which
represents the domain of values that can result from
the evaluation at a given time for a service - m
represents the minimum value and M the maximum.
Measured values are registered in the matrix for
each of its iterations in choreography (iter 1, iter 2,
iter n). Each service has a number of iterations that
match its selection for integrating the choreography,
and the number of iterations of services in the same
pool (examples: n, u and v).
3.2.1 Performance Coefficient
Figure 5 shows a pool of services of type ɸ, with
four metrics whose range of values is indicated in
parentheses. For each metric, a natural average of
values from previous iterations is obtained.
The expressions for calculating each scoring rule
is presented using the service A as an example.
(1)
n: number of runs of service A in
choreographies;
iter.Value
(i)
: is the assessment value measured
on each iteration of service A;
S(A
m1
): is the average of occurrences (n) of
measurements values (iter.Value (i)) for a
metric m
1
for service A.
AnApproachforAutomatedServiceSelectionandRankingusingServicesChoreography
263
Figure 5: Matrix structure overview (Source: adapted from (Silva and Chituc, 2013)).
The performance coefficient (pc) is than
obtained by the expression:
(2)
p: number of metrics associated to the service;
M(
j
): domain maximum value that a
assessment can achieve for each metric;
S(A
mj
): natural average of assessments of each
metric for service A;
pc
A
: the average value of each metric for each
service is addressed to the maximum possible
value that can be achieved and therefore is
obtained a proportional coefficient of
performance.
3.2.2 Weighted Performance Averaging
Figure 5 shows four generic metrics, with four
weights used (w1%, w2%, w3% and wp%).
Weighted averaging activity (wAct) reflects that
each client has different demands. The matrix results
are constructed according to client preferences. wAct
allows customer to configure the pool service to the
levels customer wants to get.
(3)
p: number of metrics in the pool
S(A
mj
): metric average value of the service A;
W
(j)
: the weight for each metric defined by
client;
wAct
A
: is the sum of the average values
obtained for each metric, weighted by weights
assigned directly by the client for service A.
3.2.3 Service Ranking Rules
This section refers to service ranking rules that
allow, from the data reflecting the behaviour of
services, to create a rating for each service by adding
other variables that are important to assure that the
global service offered is in accordance to customer
requirements and preferences.
A) SC1: Oriented Coefficient of Performance
This rule uses the “penalty / benefit” parameter
to determine the Oriented Performance Coefficient.
The "penalty / benefit" column of the matrix (Figure
5) shows the value that characterizes the service
behaviour of the last execution and superimposes an
index, according to a table (that is configured by the
owner) which aims to penalize – if the behaviour
was below the expected performance coefficient of
the service, or benefit – if it was above, adjusting the
ranking of each service. The positive or negative
deviation (resulting from its last behaviour) is
classified by ranges with therefore a corresponding
value which allows to benefit or to penalize the
classification of the service ranking.
(4)
SC
1A
: adjusted value form performance
coefficient and weighted averaging with the
benefit or penalty parameter;
wAct
A
: weighted performance averaging;
pc
A
: performance coefficient;
pb
A
: penalty or benefit value according to last
run performance.
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B) SC2: Service Utility
The number of times that a service is called to be
integrated in choreographies is relevant and the
scoring algorithm uses a factor that enhances the
ranking according to the number of times the service
is used. In addition to calculating the performance
coefficient and the weighted average of each metric,
evaluating the importance degree in terms of the
utility of the service for the various choreographies
instantiations is addressed by this rule. The utility
factor table which supports this rule is parameterized
according to the strategy of the global service
provider. SC2 is given by the expression:
SC
2A
= SC
1A
+ SC
1A
* utilityFactor
(5)
SC
1A
: adjusted value form performance
coefficient and weighted averaging with the
benefit of penalty parameter;
utilityFactor: by the number of times the
service integrates choreographies a factor is
returned.
C) SC3: Service Participation in High Performed
Choreographies
Each time a service is called in a High
Performed Choreography is important in order to
add that contribution of the service performance for
the high ranked choreography. SC3 follows the same
principle of the previous rule and is given by the
expression:
SC
3A
= SC
2A
+ SC
2A
* bestChorUtilityFactor
(6)
SC
2A
: contains the value from previous
calculations which gives the ranked value for
the service;
bestChorUtilityFactor: by the number of times
the service integrates high performed
choreographies a factor is returned.
D) SC4: Ratio between Service Participation in
High Performed Choreographies and Service Utility
This rule depends of a ratio between the number
of times the service integrates a high performed
choreography (with results in practice above the
initially expected) and the total number of times a
service was selected to common. Choreography.
SC4 follows the same principle of the previous rules
and is given by the expression:
SC
4A
= SC
3A
+ SC
3A
* ratioFactor
(7)
SC
3A
: contains the value from previous
calculations which gives the ranked value for
the service ;
ratioFactor: is the ratio between the number of
times the service was called by a high
performed choreography and the total number
of times a service was invoked in a
choreography, a factor is returned.
4 RELATED WORK
Several approaches for service monitoring and
assessment exist. (Garg et al., 2013) address the
issue of monitoring services in the cloud through a
framework that supports SMI attributes (Service
index measurement). It Consists of a set of business-
relevant KPIs that provide a standardized method for
measuring and comparing business services.
(Whaiduzzaman et al., 2013) focus on a
theoretical work about service selection for cloud
computing in multicriteria decision analisys
(MCDA) situations. They describe the MCDA types
and characteristics and present a taxonomic
categorization. Also summarize several of the
advantages and disadvantages, and present several
applications of these MCDA methods in the
selection of cloud services.
A monitoring, predicting and adaptation
approach for preventing KPI violations of business
process instances is presented in (Wetzstein et al.,
2012). A decision tree learning to construct
classification models (which are then used to predict
the KPI value of an instance while it is still running)
is also discussed.
(Baresi et al., 2005) advance an approach
towards monitoring WS-BPEL processes focusing
on runtime validation, focusing on the identification
of services delivering unexpected results, and not on
monitoring process performance metrics.
Different from previous approaches, the
monitoring and assessment approach described in
this article focuses on service ranking rules related
to service choreography. The scientific contribution
of this research lies on the conceptual framework
that supports the selection of the most suitable set of
services available at a given time to answer
customers’ requirements and preferences, taking into
consideration business process constraints, and the
characteristics of the execution environment.
Featuring a high level of learning acquired based on
historical data, solutions with a high degree of
predictability of the behavior of the overall service
in terms of time, cost and quality could be
developed.
AnApproachforAutomatedServiceSelectionandRankingusingServicesChoreography
265
5 CONCLUSIONS AND FUTURE
WORK
Nowadays, a complex service is developed and
managed by a consortium of companies that jointly
contribute to its development, sharing costs and
risks. Different parts of the process are carried out at
different organizations. Each company has its own
responsibility of the part of the choreography of
processes in which it participates. A choreography
approach is than used as coordination across many
domains of control/visibility is required. In this,
assessment and monitoring of cross-organizational
business processes are fundamental. However, an
extensive literature review has revealed that the
combination of SOC, complex adaptive systems and
adaptive control systems has not been addressed in a
conceptual and systematic way. Closing this
research gap is a focal point in this paper.
Based on complex adaptive systems theory, an
adaptive framework of services has been
constructed, including hierarchical levels of control
to enable predictability considering cost, time and
quality characteristics. The closed loops for
controlling the behaviour of the framework are
supported by the measurements of metrics at
different levels allowing establishing individual
pools of rankings of services. A choreography is
then assembled with the most adequate available
services according to customers’ requirements and
preferences.
The proposed framework and the approach for
services selection allow to dynamically identify the
appropriate set of services to target customer
requirements and preferences, making this offer
available to the customer before (s)he decides to
acquire the whole service. This approach will benefit
the relationship between customer and the provider
in the sense that will assign to the relationship a
favorable degree of reliability, facilitating new
interactions. Thus, this research work contributes to
the state-of-the-art by advancing a conceptual
adaptive framework of services that will ensure a
high degree of predictability for the services’
chorography.
Future work will focus on the overall assessment
of the choreography and validation of the proposed
framework. The scalability and adaptability of the
framework will also be targeted in future work.
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