Balancing between Local and Global Optimization of Web Services

Composition by a Fuzzy Transactional-aware Approach

Fatma Rhimi

1,2

, Saloua Ben Yahia

and Samir Ben Ahmed

Université de Carthage, INSAT, LISI Laboratory, Tunis, Tunisia

ESPRIT, School of Engineering, Tunis, Tunisia

Keywords: Fuzzy Logic, Optimization, Quality of Service, Skyline, Transactional Selection.

Abstract: The tremendous growth in the amount of available web services due to the proliferation of paradigms such

as Big Data and Cloud Computing has raised many challenges in service computing. When there are

multiple web services that offer the same functionalities, we need to select the best one according to its non-

functional criteria (e.g. response time, price, reliability) while guaranteeing a global optimization. Many

approaches have been introduced to tackle this problem. However, most of them neglected users

preferences, which can be very vague and imprecise, in the selection process. Besides, transactional

properties that can insure a reliable achievement of the composition are rarely considered. This paper

suggests a solution to this challenge by modelling users uncertain preferences with fuzzy sets. We then

compute the set of Skyline services which are the best candidates in the search space with fuzzy dominance

relationship and fuzzy similarity measures. Finally we inject transactional properties in order to guarantee a

global optimization with a successful achievement of the composition process. Experimental evaluation

demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.

1 INTRODUCTION

Web Services are growing in popularity as an

efficient solution to enhance the interoperability for

machine-to-machine interaction among different

applications and different platforms. This is why

business structures are moving today towards the

service-oriented architecture as web services seem to

be the best solution to allow the exchanges between

them. Service composition is a process that

combines multiple atomic web services in order to

create value-added web services. Hence, it is arising

as an effective solution to deliver customized

services to the different users.

Quality-of-Service (QoS) is widely employed to

represent the non-functional characteristics of web

services and has been considered as the key factor in

service selection. QoS is defined as a set of

properties including response time, throughput,

availability, reputation, etc. However, today with the

prevalence of paradigms such as Big Data, Cloud

Computing and XAAS (everything as a service), the

number of available web services had exploded.

This is why it has become difficult to choose the

best candidates that would insure an optimal

composition. By optimal composition we mean a

composition that corresponds the most to the

functional and non-functional criteria provided by

the user.

Skyline is a technique that comes as a solution

that helps reducing the search space based on a

dominance relationship to preselect the best services

and prune the others. Intuitively, a skyline query

selects the “best” or most “interesting” points with

respect to all dimensions. Thus, it can be very

effective for reducing the number of candidates and

enhancing the optimization.

On another hand, transactional properties of web

services are crucial to insure the coherence of the

composition process. In fact, delivering reliable

service composition is very important for the overall

quality of the service. Invoking distant atomic web

services which interoperate with each other can be

affected by failures, throughput, availability etc.

Hence, insuring a reliable service becomes as

important as delivering a service which meets users

functional and non-functional requirements.

In this study, we argue that users preferences as

well as transactional behavior of services should be

considered in the selection process. Thus, we

Rhimi, F., Yahia, S. and Ahmed, S.

Balancing between Local and Global Optimization of Web Services Composition by a Fuzzy Transactional-aware Approach.

DOI: 10.5220/0005941200750082

In Proceedings of the 11th International Joint Conference on Software Technologies (ICSOFT 2016) - Volume 2: ICSOFT-PT, pages 75-82

ISBN: 978-989-758-194-6

suggest an approach that will recommend to the user

the appropriate service according to his preferences

using fuzzy techniques. Then, we will inject

transactional properties in order to insure the

reliability of the delivered service.

1.1 Contributions

This paper aims to present a new approach for web

services selection which balances between local

optimization by selecting services that correspond to

the users preferences, and global optimization by

respecting transactional properties. The first step is

the computation of the set of Skyline services.

Traditional Skyline computation methods rely on

Pareto dominance relationship: a service p

dominates another service q if p is at least as good as

q in all the dimensions and strictly better in at least

one dimension. Such strict dominance relationship

suffers from looseness and does not accord an

importance to the user’s preferences. Besides, users

behavior is subjective, vague and imprecise. We

suggest a two steps approach:

- We will in first place select the services with the

best matching degrees with the user’s

preferences based on fuzzy similarity measures.

- We will inject transactional properties in order to

guarantee a reliable successful composite

service.

Considering all this, our main contributions may be

summarized in the following points:

- We will address the problem of computing

service Skyline with a consideration of user’s

preferences by making use of fuzzy preference

relationships rather than Pareto dominance

relationships.

- We compute the matching degree between web

services and users requirements using fuzzy

similarity measures.

- We proceed to optimizing the overall

composition using transactional properties of

web services.

- We evaluate the efficiency and the effectiveness

of the proposed method with a theoretical study

and an experimental evaluation.

1.2 Outline

Section II presents the related work of this study .In

section III we present the background of this work

so we can advance the followed approach. Section

IV will describe the different steps of the proposed

approach. In Section V, we present the experimental

evaluation of the approach. Finally, section VI will

conclude the paper.

2 REALTED WORK

2.1 Web Services Composition and

Skyline Computation

The problem of QoS-based web service selection

and composition has received a lot of attention

during the last years. Local selection methods using

techniques such as Simple Additive Weighting

(SAW) were conducted to select services that ensure

an optimal composition. However, local selection

could not satisfy global constraints on the

composition as it treats each service class

individually. Zeng et al., (2003) tackled this problem

using a global planning composition based on mixed

integer Programming technique for dynamic and

quality-driven selection. However, the costs of this

approach are exponential in a large space. Linear

programming methods are very effective when the

size of the problem is small, but suffer from poor

scalability due to the exponential time complexity of

the applied search algorithms. In their work, Alrifai

and Risse (2009) proposed a hybrid selection

approach that combines local selection with global

selection by decomposing global constraints into

local constraints in order to find close-to optimal

solutions. Canfora (2005) proposed a genetic

algorithm to the QoS-based composition. Genetic

algorithms are based on the evolution theory and in

opposition to linear programming algorithms, the

input data doesn’t need to be linear. Besides, genetic

algorithms are related to the number of service

classes and not to the number of candidate web

services, so they are more effective in a large space

context. However, linear programming is proved to

be faster than genetic algorithms and is preferred

hence in a small space. Yu and Keiw-Jay (2004)

proposed heuristic algorithms that can come as an

alternative to exact solutions. The authors modelled

the problem as combinatorial problem and proposed

a heuristic Branch and Bound algorithm (WS HEU)

and a heuristic graph model (MCSP-K). The two

algorithms are proved to be more efficient than exact

algorithms. Ardagna and Pernici (2007) tried to

overcome the shortcomings of both local and global

service composition by proposing an approach that

addresses optimization problems under severe QoS

constraints.

Skyline is a mechanism that interferes as a filter

in the search space that would select only the

ICSOFT-PT 2016 - 11th International Conference on Software Paradigm Trends

interesting points. Hence, by discarding all the

irrelevant data points, the number of possible

combinations would be dramatically reduced. The

analysis of the Skyline was originally considered as

a mathematical problem. It was then introduced in

the first place in the field of database. Given a set of

points in d-dimensional space, the Skyline is defined

as the subset containing the points which are not

dominated by another point. Paradigms like Block

Nested Loops (BNL) and Divide to Conquer are

among the first attempts to solve the computing of

Skyline. BNL algorithm uses a nested loop to

compare the points. Divide to Conquer algorithm

recursively divides the whole space into small sets

(regions), calculates the Skyline for each region

separately, and merge them in the final Skyline.

BNL has proposed the Sort Filter Skyline (SFS)

algorithm, which adopts a pre-sorting to improve the

efficiency. SFS has also been improved by linear

Elimination Sort Skyline (LESS), which operates on

a small set of best data objects to remove other

objects in the original passage of the external sort.

The index structures such as B-trees have also been

utilized to improve the performance of analyzing the

Skyline. Nearest Neighbour (NN) and Branch and

Bound Skyline (BBS) are two representative

algorithms that can progressively address the

Skyline based on R-tree structure. In recent works,

many researchers focused on computing skyline

services in the context of service composition.

However, the majority of these works relied on

Pareto dominance relationship for this purpose

Alrifai et al., (2010), Chen (2014), Abourezk and

Idrissi (2014). Pareto dominance has the

shortcoming of neglecting the smoothness and

fuzziness of human preferences. Benouaret et al.

(2011) addressed this problem with a fuzzy

dominance relationship. Fuzzy logic was addressed

in the optimization techniques for service

composition in many contributions such as those of

Almulla et al., (2010), Torres et al., (2011), Xuan

and Tsuji (2008) and Wang (2006).

2.2 Transactional-aware Service

Selection

Due to the characteristics of web services including

loosely coupled and heterogeneous nature, insuring a

reliable execution of services becomes very

challenging. Liu et al., (2006) proposed rules for

ensuring transactional services in parallel and

sequential compositions. Hadded, Manoeuvrier and

Rukoz proposed in their leading study (2010) an

algorithm combining QoS optimal web services

selection with transactional constraints to generate

transactional composite services. Bhiri et al., (2006)

proposed an algorithm to check the validity of a

composition according to the set of acceptable

termination states of a transactional composite web

service.

2.3 Fuzzy Similarity Measures

Fuzzy sets were introduced by Lotfi Zadeh (1965) as

an extension of the classical notion of sets.

Fuzzy similarity measures are measures used to

compute the degree of similarity between two fuzzy

sets. The concept of similarity is interpreted in

different ways depending on the context. The

interpretation of similarity in everyday language is

“having characteristics in common”. This

interpretation of similarity differs from the one we

use. We define similarity between fuzzy sets as the

degree to which the fuzzy sets are equal. This

definition is related to the concepts represented by

the fuzzy sets. Fuzzy sets are considered similar if

they are defined by overlapping membership

functions that assign approximately the same values

of membership to the elements in their universe of

discourse. Their similarity is the degree to which

they can be considered as equal.

3 DESCRIPTION OF THE

PROPOSED APPROACH

In this work, in order to tackle the different

challenges of service selection, we propose a system

composed by mainly five steps as shown in Fig.1:

- First of all, users requirements on functional

criteria and non functional criteria are collected.

An initial set containing the deployed services

that offer the required functionality with the

required Quality of Service is given.

- We compute the Skyline services of our search

space using fuzzy dominance relationship.

- We refine the output by determining the set of

services that correspond the most to the user's

preferences using Fuzzy Similarity Measures.

- Finally, we will select from the top-K services

for each service class, the services that will

guarantee a coherent execution according to

transactional properties.

Balancing between Local and Global Optimization of Web Services Composition by a Fuzzy Transactional-aware Approach

Figure 1: System Architecture.

3.1 Computing Service Skyline

The first step in our proposed approach is computing

the set of skyline points from the search space. This

step will enormously reduce the candidate services

as it will select only the non-dominated services.

Skyline can be formally defined as follows:

Given a set of points S in a space with D

dimensions, Skyline points are the points who are

not dominated by any other point in the search space

according to those dimensions. A definition of the

dominance concept is then crucial to the

understanding of the skyline concept.

Pareto Dominance

Definition

Given

the number of dimensions in the space

and

two web services in the space, we say that

dominates

denoted by

s

iff

is at least

as good as

in all the dimensions and strictly

better in at least one dimension.

Illustrative Example

Let’s consider the common example in the literature

that selects the set of interesting hotels in a

reservation service represented in Fig.2. The hotels

are represented by two criteria: their prices and their

distances from the beach. It is obvious that a hotel

with a low price and a small distance from the beach

is preferred in this case. According to this, the

Skyline points are

,al

and

as they are the only

points that are not dominated by any other point in

the search space.

Figure 2: Example of Skyline Set.

Fuzzy Dominance

Given two points in a space with

d dimensions, we

can define the dominance relationship as follows:

(((),())

deg ( )

qsqs















(1)

With:

d : The space dimensions (i.e. the QoS attributes in

our context)

: two points in the search space

(), ( )

mi m j

qsqs

: The values of the

attribute for

and

respectively.







: A fuzzy membership function that is defined

as follows:

0 if x-y

(, ) 1 if x-y +

otherwise































(2)

With



0



0.

In this paper, we will compute Skyline services with

fuzzy dominance relationship by the Fuzzy Branch

and Bround Algorithm proposed in Rhimi, Ben

Yahia and Ben Ahmed approach (2015). The

proposed algorithm is a two-phase algorithm. The

first phase consists in transforming the data points of

the search space into a consistent fuzzy model.

Branch and Bound Skyline is an algorithm suggested

by Papadias (2003) based on R-Tree structure

known for its efficiency and effectiveness in large

spaces. It is widely used to reduce the search space.

The second step is determining the Skyline points

with a Branch and Bound algorithm according to the

fuzzy dominance relationship.

ICSOFT-PT 2016 - 11th International Conference on Software Paradigm Trends

3.2 Computing Similarity between

Users Preferences and Services

using Fuzzy Similarity Measures

In our work, we chose to model user’s preferences

with fuzzy sets as they are highly appropriated for

expressing vagueness and smoothness in human

linguistic terms. In fuzzy set and possibility

framework, similarity of items is computed based on

the membership functions of the fuzzy sets

associated to the item features. Many Fuzzy

Similarity Measures were introduced in literature

Wu Zhang and Lu (2014), Zenebe and Norcio

(2010). In our study, for services

and

, the

following similarity measures between

and

denoted by

(I , I )

and are defined as:

min( ( ), ( ))

(I , I )

max( ( ), ( ))

ik i j







(3)

()* ()

(I , I )

((()))((()))

ik i j









(4)

(() ())

(I , I )

{(), ()}

ik i j

iik ij

Max I I









(5)

(I , I ) 1 * ( ( ) ( ))

(2* ( ) 1) (2* ( ) 1)

kj ik i j

ik ij

SII



 

 





(6)

Where

()



and

()



are respectively the fuzzy

membership degrees of the i

QoS attribute of

services

and

. These fuzzy set based similarity

measures are: fuzzy set theoretic in (3), cosine in (4),

proximity in (5), and correlation-like in (6).

The outcome of this step is a set of services that

correspond the most to the user’s constraints.

3.3 Transactional-aware Selection

In this section we will no more consider individual

service classes. We will look at the whole service

composition and try to insure a global optimal

service selection.The description of the behavior of a

web service composition is how atomic web services

can be realized in terms of interactions with each

other. In a composition where several WS

components interact, unexpected behavior of a

component WS can not only lead to failure but also

can bring negative impact on all participants of the

composition. The execution of a composite web

service therefore requires transactional properties so

that the overall coherence is insured. Based on the

transactional properties and transactional rules of

web services that have been defined in Hadded et al.,

(2010), we adopt the following definitions:

A web service is said to be retriable and we note

it 'r' if it is sure to complete after a finite number of

activations. It is said to be compensatable and we

note it 'c' if it offers compensation policies to

semantically undo its effects. Finally it is said to be

pivot and we note it 'p' if once it successfully

completes, its effects remain and cannot be

semantically undone.

Now, we give the generalization of these

properties for a composite web service (CWS) as

defined by the authors.

A CWS is atomic if once all its component WSs

complete successfully, their effect remains forever

and cannot be semantically undone. Besides, if one

component WS does not complete successfully, then

all previously successful component WSs have to be

compensated. we will note 'a' an atomic CWS . A

CWS is compensatable and we note it 'c' if all its

component WSs are compensatable.. An atomic or a

compensatable CWS is retriable 'r' if all its

components are retriable.

Finally, we define a Transactional Composite

Web Service as a CWS whose transactional

behavioral property is in {a, ar, c, cr.}

Hence, in order to insure a transactional

Composite Web Service, the following rules should

be considered in the selection process:

- A 'p' or 'a' WS can only be sequentially

composed with a 'pr', 'ar', or 'cr' WS and can only

be executed in parallel with a 'cr' WS.

- A 'pr' or 'ar' WS can only be executed in

sequential or

in parallel with a 'pr', 'ar', or 'cr' WS.

- A 'c' WS can be sequentially composed with any

transactional WS but can only be executed in

parallel with a 'c' or 'cr' WS.

- A 'cr' WS can be executed in sequential or in

parallel with any transactional WS.

In our approach, rules for insuring a successful

termination of web services will be injected in the

selection algorithm after the initial phases of local

user-based optimization. Depending on the

transactional properties of the WS we will select the

candidates that insure a successful termination of the

composition according to the rules. For sake of

simplicity we will only consider a sequential

Balancing between Local and Global Optimization of Web Services Composition by a Fuzzy Transactional-aware Approach

workflow for our algorithm. We will let the parallel

workflow for future work.

The description of the selection approach is

given in algorithm 2.

Algorithm 2: TPAC (Transactional Preference- Aware

Composition).

Input:

- Lists of top-k ranked concrete services

for each abstract service class computed by

Algorithm 2.

- composition workflow

Begin

// IAC: initial abstract class

1. Select the first element of the list

of IAC

2. found=0; i=0;

4. For all the AC in the workflow:

5. If ( current WS is 'p')

6. While (list_ next_ AC not empty and

found==0)

7.if (element i of list is 'p' or

'a')

8. found=1;

9. Else if ( current WS is 'pr'

or 'ar')

10. While (list_ next_ AC and

found==0)

11. if (element i of list

is 'pr' or 'ar' or 'cr')

12. found=1;

13. Else if ( current WS

is 'cr' or 'c')

14.Select the first

element of list_ next_ AC

// can be composed in sequence with any

transactional WS

15. END TPAC.

4 EXPERIMENTAL

EVALUATION

4.1 Dataset and Experimental Setup

In our experiments, we adopt a real-world Web

service dataset: WSDream dataset which is designed

for QoS prediction approaches for Web service

recommendation and adopted in many papers such

as the works Zheng et al., (2014)

(http://wsdream.github.io/dataset/wsrec_dataset1).

The dataset contains QoS records of service

invocations on 5825 Web services from 339 service

users, which are transformed into two user-service

matrices: a response-time user-item matrix and a

throughput user-item matrix. For studying the

prediction accuracy, we divide the dataset into two

parts, one part as training set and the other part as

testing set. In order to carry the predictions, we

randomly remove entries from the training user-item

matrices. Different methods are employed for

predicting the QoS values of the removed entries.

The original values of the removed entries are used

as the expected values to study the prediction

accuracy. Each experiment is evaluated by 10 times

10-fold cross validation. The 339 service users are

divided into two groups: 300 randomly selected

training users and the rest as test users. In 10-fold

cross validation, the training users are randomly

partitioned into 10 subsets. The process is repeated

10 times to predict the missing QoS values of test

users based on QoS values of the corresponding

training users. For studying the time execution, we

suggest a scenario of a sequential workflow with

eight service classes, but we vary the number of

services and the transactional properties of the web

services. We measure the average execution time

required to solve the composition problem of twenty

executions.

4.2 Evaluation Metrics

The following metrics have been used in this study:

Statistical Accuracy Metric

We use Mean Absolute Error (MAE) metric to

measure the prediction quality of our method with

the different fuzzy similarity measures. MAE is

defined as the deviation of recommendations from

their true user specified values and given by:







(7)

being the predicted rating of the service,

the

actual rating of the service and

the number of

predicted ratings.

Recall, Precision and F1 Metrics

Recall is defined as the fraction of preferred items

that are recommended. Precision is defined as the

fraction of recommended items preferred by the

user. The F1-measure, which combines precision

and recall, is the harmonic mean of precision and

recall.

In this experiment, a preferred rating threshold is

predefined. The preferred services are the services in

the test set whose actual ratings are greater than the

preferred rating threshold. The recommended

services are the services whose predicted ratings are

greater than the preferred rating threshold. The

recall, precision and F1 are defined as follows.

ICSOFT-PT 2016 - 11th International Conference on Software Paradigm Trends

preferred recommended

precision

recommended





preferred recommended

recall

preferred





2* *

recall precision





4.3 Experimental Evaluation

Effect of Varying Fuzzy Similarity Measures on

Prediction Accuracy:

We propose to study the effect of the similarity

measure on prediction accuracy. For this purpose,

we will compare the precision, recall, F1 and MAE

of our approach when using fuzzy set theoretic,

cosine, proximity and correlation-like measures.

Figures 3 and 4 show that for precision and

recall, correlation-like method has the highest

prediction accuracy. However Fig. 6 show that it has

a high MAE. Proximity and cosine have stable

results for all metrics, but cosine has the lowest

MAE which proves its efficiency.

Figure 3: Precision of the different similarity measures.

Figure 4: Recall of the different similarity measures.

Effect of Varying Number of Services Per Class on

Time Execution:

We notice that the computation cost increases when

the number of candidate services increases which

can be explained by the number of possible

combinations. However, we notice that time

execution is still reasonable as there are different

steps to reduce the number of possible candidates.

Figure 5: F1 of the different similarity measures.

Figure 6: MAE of the different similarity measures.

Thus, it is clear that the utilization of user's

preferences for pruning the services in the search

space eases time-consumption during the generation

of match results in the injection of transactional

properties.

Figure 7: Execution time varying number of services.

5 CONCLUSIONS

In this paper, we proposed a new method for

combining local and global web services

Balancing between Local and Global Optimization of Web Services Composition by a Fuzzy Transactional-aware Approach

optimization. Local optimization is given by

including users preferences in the selection process.

In fact, we suggest an approach that firstly computes

the set of Skyline services using fuzzy dominance

relationships. Then we refine the result according to

the user's expressed preferences. Our approach first

computes the similarity between the skyline services

and the user request with fuzzy similarity measures.

Finally, global optimization represented in our

study by successful termination of the composition

is injected. We make sure that in the selection

process, only candidates that guarantee transactional

properties of web services are chosen. The results

showed improvements in accuracy. We also proved

that the approach is not time-consuming as many

steps are used to prune that candidates.

In future work, we will study the injection of

transactional properties in a parallel workflow with

all possible combinations. We will also try to

include global constraints on the QoS in order to

insure global optimization from another perspective.

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