Web Service Selection based on Parallel Cluster Partitioning and
Representative Skyline
Sahar Regaieg
1,2
, Saloua Ben Yahia
1
and Samir Ben Ahmed
1,2
1
LISI Laboratory, INSAT Institute, Carthage University, Tunis, Tunisia
2
Faculty of Mathematical, Physical and Natural Sciences of Tunis (FST), Tunis El Manar University, Tunis, Tunisia
Keywords: Web Service Composition, Quality of Service, Skyline, Representative Skyline, Cluster, Partition.
Abstract: Optimizing the composition of web services is a multi-criteria optimization problem that consists in
selecting the best web services candidates from a set of services having the same functionalities but with
different Quality of Service (QoS). In a large scale context, the huge number of web services leads to a great
challenge: how to find the optimal web services composition while satisfying all the constraints within a
reasonable execution time. Most of the solutions dealing with large scale systems propose a parallel Skyline
phase performed on a partitioned data space to preselect the best web services candidates. The Global
Skyline is computed after the consolidation of all the Local Skylines and, eventually the optimization
algorithm is applied. However, these partitioning approaches are only based on pure geometric rules and do
not classify the web services according to their real contribution to the optimal or sub-optimal solution
search area. We will propose in this paper an intelligent partitioning approach using a cluster based
algorithm combined with the representative Skyline.
1 INTRODUCTION
Web services Composition is a complex process that
involves several activities such as discovery,
composition, selection or execution. Generally, the
composition is considered, initially, as an abstract
workflow. The selection phase consists of looking
for concrete instances (concrete services) that can
replace abstract nodes. These services should meet
QoS requirements and user preferences as much as
possible.
Optimizing the Web Services (WS) Composition
in terms of QoS consists in selecting the concrete
WS for each task while satisfying the global
constraints and the user preferences. The best overall
QoS for the global composition according to a utility
or fitness function will hence be reached (Zeng et
al., 2003). As such, this optimization process
consists in identifying the best services among a
group of services with similar functions but different
QoS.
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 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
quality-driven dynamic selection.
However, the cost of this approach is very high
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
complexity of the applied search algorithms. All
these contributions focused on service selection in a
small space but for large scale systems, a further
reduction of the number of candidates is required.
The rest of this paper is organized as follows:
Section 2 presents the related work of the web
services composition problem. In sections 3 and 4,
we present the background of this work with a
reminder of Skyline and Representative Skyline
concepts. Sections 5 will describe the different steps
of our proposed approach. Section 6 contains an
experimental study of our approach and analysis of
the obtained results and we will eventually conclude
this paper.
648
Regaieg, S., Yahia, S. and Ahmed, S.
Web Service Selection based on Parallel Cluster Partitioning and Representative Skyline.
DOI: 10.5220/0006904106480655
In Proceedings of the 13th International Conference on Software Technologies (ICSOFT 2018), pages 648-655
ISBN: 978-989-758-320-9
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
2 RELATED WORK
Many researchers have used Skyline requests to
reduce the search space. The Skyline operator was
first introduced by Börzönyi et al., (2001). Then
many efficient skyline algorithms have been
proposed. Algorithms like Block Nested Loop
(BNL), Divide-and-Conquer (D&C), Sort Filter
Skyline (SFS), Bitmap and Nearest Neighbor (NN)
can process skyline query in the datasets without
indexes.
These algorithms can eliminate the low-quality
web services among large amounts of candidates and
return a much smaller and a higher quality set to the
user. But the obvious limitation of these studies is
that they can only compute Skyline on one
combination of QoS parameter.
However, in practice, different users may be
interested in different combinations of QoS
parameters and choose different preferences. In
order to tackle this issue Yang et al., (2015)
introduced the service SkyCube which consists in
applying the Skyline on all possible combinations of
QoS parameters. As it is computed previously in an
off-line manner, using SkyCube method can speed
up the response time in real-time web service
selection. Unfortunately, the current SkyCube
computation solutions suffer from the issue of
dimension scalability. For this reason, the
researchers moved towards the parallel Skyline
approach.
All of the aforementioned scientific publications
point the readers to one of the major difficulties
when mashing up cloud services, namely: the large
amount of services in the service pool to choose
from, the diversity set of services representing
different QoS, and the extended attribute dimensions
to treat.
As such, some works introduced Skyline
algorithms by incorporating the MapReduce
paradigm to exploit distributed parallelism in a cloud
mashup. A good evaluation of various MapReduce
(MR) in Skyline processing was given by Zhang et
al., (2015).
The grid-partitioning MapReduce is introduced
by Zhang et al., (2011) and the MR-angular
partitioning of Skyline space is studied in the
research of Vlachou et al., (2008). With such
partitioning methods, sometimes one partition may
contain data that does not satisfy a specific request.
Then, as an important variant of Skyline, the K-
Representative Skyline is a useful tool if the size of
the full Skyline is large (Bency, 2014). Bai et al.,
(2016) introduced the distance-based representative
Skyline (k-DRS). This method is based on a 2-step
approach to solve the k-DRS problem efficiently.
Step 1 divides the full Skyline set into k clusters. In
step 2, a point in each cluster is selected as the
representative Skyline point. The k selected Skyline
points consist of the k-DRS. So, the concept of
Skyline used in the selection of web services
according to their QoS applied to allow the
reduction of the search space in the local phase, and
subsequently, ensure a considerable time saving
during the overall optimization process.
In this paper, we present a parallel Skyline
service selection method designed to improve the
efficiency of the existing partitioning approaches
and propose a more intelligent and reduced search
space for the web services selection. This approach
is based on a cluster partitioning based method is
employed with representative Skyline for web
service selection.
3 SKYLINE
Skyline is a mechanism that acts as a filter in the
search space that will select only the interesting
points. Skyline can be formally defined as follows:
Given a set of points P in a space with q
dimensions, Skyline points are the points who are
not dominated by any other point in the search space
according to those dimensions.
Given a data space D defined by a set of q
dimensions {d
1
, ..., d
q
} and a dataset P on D with
cardinality n, a point p
∈
P can be represented as
p = {p
1
, ..., p
q
} where p
i
is a value on dimension d
i
.
Without loss of generality, let us assume that the
value p
i
in any dimension d
i
is greater or equal to
zero (p
i
≥ 0) and that for all dimensions the
minimum values are more preferable
Definition 1 (Dominate). A point p
∈
P is said to
dominate another point
q
∈
P, denoted as p
≺
q, if (1) on every dimension
d
i
∈
D, p
i
≤ q
i
; and (2) on at least one dimension
d
j
∈
D, p
j
<q
j
.
Definition 2 (Skyline Point). The Skyline is a set of
points SKY
P
⊆
P which are not dominated by any
other point in P. The points in SKY
P
are called
Skyline points (H. Köhler, J. Yang, and X. Zhou,
2011).
3.1 Parallel Skyline
Due to the complexity of the computation on the
large datasets, parallel approaches have been studied
Web Service Selection based on Parallel Cluster Partitioning and Representative Skyline
649
to calculate the Skyline. A data set can be divided
into subsets of data according to a partitioning
strategy. For each subset located on a server, a Local
Skyline is computed. These Local Skylines are then
consolidated on a single server and a Global skyline
is eventually computed. In parallel Skyline
solutions, an intuitive data partitioning scheme is
based on random partitioning, in which objects in a
partition are randomly selected. Since a dataset can
be evenly divided across partitions by a random
partitioning method, the number of Skyline objects
in each partition is expected to be the same under a
uniform data distribution.
This approach does not minimize the output of
Local Skyline computation, which would
significantly increase the communication cost
between Local and merging Skyline computation
components (Köhler et al., 2011).
A grid partitioning scheme that partitions the
data space into multiple grids was employed in
parallel and distributed computing environments
(see figure 1). By utilizing the domination relation
among partitions, objects in a partition that is
dominated by another partition can be discarded
without Skyline calculation.
To minimize the Local Skyline results, an angle
based partitioning method maps datasets from a
Cartesian coordinate space to a hyper spherical
space and groups objects based on their angles to the
origin (see figure 1)
Figure 1: Partitioning example (Vlachou et al., 2008).
3.2 Problem Statement
It has been found that the angle-based partitioning
method is effective in terms of execution time.
However the main drawback is that some partitions
do not contribute to the Global Skyline and do
contain relevant points that will be considered as
good candidates for optimal or sub-optimal solution
of our problem. As such some servers are running
inefficiently and Local Skyline results are sent over
the network to the central server without really
contributing to the final solution causing extra
bandwidth consumption.
Moreover most of the existing contributions
consider partitioning the data in a two-dimensional
space while in reality we have D-dimensions each
dimension for a specific QoS. We must also consider
the dynamic aspect of the data since new web
services are regularly added as candidates and we
must position them on the right partition or the right
server.
Even though the parallel computing Skylines
became a major solution to recover results in
reasonable response times, still in a large scale
context, such methods are effective but have a
certain number of disadvantages:
Some partitions do not contribute neither to the
Global Skyline nor to the optimization phase
Data are inefficiently partitioned according to a
pure geometrical aspect (angular, grid) without
taking into account the efficiency of each
partition according to the utility function.
A large amount of data is generated dynamically,
The attribute values of the objects do not cease to
change.
User preferences change continuously.
Existence of irrelevant service areas.
Existing partitioning methods are limited to two
dimensions.
The optimality aspect of the composition is not
introduced into the Skyline calculation phase.
As aforementioned, partitioning method for Skyline
computation have been widely studied in a large
scale environment. However, these approaches have
their own deficiencies as analyzed above.
In the next part, top-k representative skyline will
be presented. This method will provide a new way to
overcome some of the aforementioned deficiencies
of ranking multi-criteria applications (Nguyen and
Cao, 2015).
4 REPRESENTATIVE SKYLINE
In the service selection process, the more constraint
conditions are considered the more services will be
on the Skyline (Gang and Chunli, 2015). It is also
difficult for users to identify the desirable services
from Skyline.
In order to deal with this problem, the top-k
approach can return fewer Skyline services which
will significantly reduce the overall processing time.
ICSOFT 2018 - 13th International Conference on Software Technologies
650
However, this approach may potentially ignore
the tradeoff of QoS required by users. In many
candidate services, different tradeoff of QoS
information gives user a summary of the
performance distribution of services and offers an
efficient way for user to identify desirable services.
The top-k representative Skyline is recently
studied to offer few set Skyline services with
different tradeoffs for the users.
The Representative Skyline can give a concise
summary of the entire Skyline and allows the
selection of the most appropriate web services for
service composition with end-to-end constraints.
In the literature, the main challenge is how to
identify a set of representative Skyline services that
best represent the trade-offs between different
qualities of service parameters so that a solution can
be found that satisfies users constraints and also has
a high service score (Bency, 2014). This is
essentially a compromise on the number of
representatives to choose.
A representative skyline is a set of k points that
are considered as a representation of Full Skyline.
Representative skyline fits very well the multi-
criteria decision making. Up to now, Representative
Skyline calculation algorithms are designed for
Static data.
Mei Bai et al., (2016) proposed distance-based
Representative Skyline query (DRS) which applies a
distance metric to measure the “Representativeness”
of a chosen set.
Given a set K with k Skyline points from the full
Skyline set S, Er(K, S) = max
p
∈
S−K
{min
p′
∈
K
|p, p′|
where |p, p′| is the Euclidean distance between p and
p’. The DRS query will select a set K, k Skyline
points, that minimizes Er(K, S). The DRS performs
well only if Skyline points follow a cluster
distribution. Therefore, in data stream environments,
the DRS cannot guarantee a high representativeness,
because the dominance ability of the chosen set is
neglected Skyline based on the significance and
diversity.
5 PROPOSED APPROACH
In this section, we will explain our approach to
compute the Skyline of a web services class in a
parallel and representative way.
Our method consists of three steps. The main
purpose of the first step is to realize an intelligent
partitioning in order to compute the local skylines.
We can consider two main classes of partitioning
approaches for the Web Services DataSet. The first
one is based on a pure geometrical approach like
Grid, Angular and Random Partitioning (Vlachou et
al., 2008). These techniques have already been
applied on the problematic of Web Services Skyline
computation. In this study, we considered a second
class of clustering techniques widely used in the
literature and inspired from the DataMining area like
K-Means, hierarchical Clustering, Partitioning
Around Medoids…. In this paper, we choose not to
use the geometrical approach and to base our work
on K-Means algorithm. This algorithm has been
selected because of its low complexity as well as its
good performances when applied on a big data set
which is our case. The second Step allows the
parallel Skyline computation since the Local Skyline
is evaluated on each partition that is on each cluster
identified during Step 1. The third step consists in
the Representative Skyline calculation on each
partition. The proposed solution is detailed in figure
2. We can notice that in this figure the cluster 2 (C2)
is not a good candidate since the score of its centroïd
is among the weakest ones. Indeed, the average of
all scores is computed and all the clusters whose
centroïds have a score better that this average are
considered as being good candidates.
Figure 2: ¨Proposed approach.
5.1 Step 1: Clustering
In this step, we will focus on the clustering of
services using the K-Means algorithm. Concretely,
Web Service Selection based on Parallel Cluster Partitioning and Representative Skyline
651
this can be done as follows: suppose a set of WS of
the same class of service (WS having the same
functionality but different QoS).
A WS is characterized by a set of QoS criteria
represented as an attribute vector. The goal is to
group in the same cluster, WS considered similar.
K-Means is an iterative clustering algorithm. It
starts with a set of K reference individuals randomly
selected. The data are partitioned in K groups; an
individual belongs to a group if the center of this
cluster is the most close to him (in terms of
distance). Updating of centroïds and assigning
individuals to clusters of data are performed during
the successive iterations. K-Means works to
optimize the inertia criteria in order to make the data
close to each other in the same cluster and widely
separated among clusters.
To measure the similarity or dissimilarity
between web services, we adopt the distance from
Manhattan:
,
=
..
(1)
Where i, j are two web services each defined by its p
QoS; i = (
,
,..,
), j = (
,
,..,
) and
n=1.
The intra-class inertia I
A
is used to evaluate the
heterogeneity within classes. The clustering is even
better than its I
A
is small.
=
∑
(2)
When P is a data set consisting of N objects,
P = {I
1
, I
2
, ... I
N
} and B
p
their center then I
T
of a
Cluster k, I
T
(C
k
) is given by:
=
∑
,
(3)
Where N
K
is the number of objects in the cluster k
and g is the number of the generated clusters.
The inter-class inertia I
E
evaluates the similarity
between classes. More the I
E
is great, the better
clusters are separated, so heterogeneous and
therefore the clustering is better.
I
,
(4)
Where B
Ck
is the center of the group k and
dB
,B
is the Manhattan distance as we defined
previously.
5.2 Step 2: Parallel Skyline
All the calculation done in step 2 and step 3 is done
in a parallel way. Indeed, each server is responsible
of a cluster identified in step 1. In this step, we are
interested in extracting Skyline services from each
cluster. Thus, each server does the calculation of the
Skyline relative to its cluster. To calculate the
Skyline, we adopt the Block Nested Loop Skyline
(Borzsonyi et al., 2001).
The BNL algorithm consists of comparing all the
points of the search space two-by-two and returning
as Skyline all points that are dominated by no other
point in space. This algorithm fits well small and
medium sized databases. This is why we adopt this
algorithm in our approach since we have already
partitioned the search space into several smaller
sized search spaces.
5.3 Step 3: Representative Skyline
For each cluster identified in Step 1, a virtual point
is created in the multidimensional space of the QoS,
whose coordinates are calculated and this virtual
point is considered as the centroïd of the cluster.
The representative Skyline services of each
cluster are the Skyline services that have a higher
score than the cluster centroid. This score is
calculated by a F
ij
which is also called the Utility
Function.
Suppose there are x QoS attributes to be
maximized and y QoS attributes to be minimized.
The utility function for service S
ij
is defined as
follows
∗
∑
*
∗
+
∑
*1
∗
)
(5)
Where w
α
and w
β
are the weights for each QoS
attribute, (0 < w
α
, w
β
<1),
∑
∑
1.
μand δ are the average and standard deviation of the
QoS values for all candidates in a service class (M.
Alrifai, D. Skoutas and T. Risse, 2010)..
In the utility function definition, all QoS
attributes are weighted by their importance. They are
also normalized by their averages and standard
deviations so that the utility function will not be
biased by any attribute with a large value. At the end
of this step, each server gives the Local
Representative Skyline services for the cluster in
which it is responsible. The master server merged all
Local Skylines to give a representative Global
Skyline.
ICSOFT 2018 - 13th International Conference on Software Technologies
652
6 EXPERIMENTAL
EVALUATION
6.1 Experimental Setup
In this section, we will study the performance of our
method. In each experiment, a dataset of 150 web
services characterized by 4 QoS is split to N
partitions, so that each point is assigned to one
partition. Then, for each partition the Local Skyline
is computed. In our method, each server may use
any Skyline algorithm, thus the choice of Skyline
algorithm is not restrictive in general.
In a single experiment, we use the same
algorithm for all partitions and all partitioning
methods, in order to present comparable results. The
algorithm employed is BNL. Each experimental
setup is repeated 10 times and we report the average
values. First, we performed a number of tests on the
set of web services to find the optimal number of
clusters.
Table 1 shows us the classification results of web
services by the K-Means method. We know that a
good classification is obtained when we find a
minimum intra-class inertia value and a maximum
inter-class inertia value. Test 3 is the one with the
best results (intra-class inertia and inter-class
inertia), hence the optimal number of cluster is equal
to 4.
Table 1: Test for the validation of the optimal number of
cluster.
Test
numbe
r
k
Intra-class
inertia
Inter-class
inertia
Test 1 2 3.88 0.23
Test 2 3 2.22 14.75
Tes
t
3 4 1.06 29.5
Test 4 5 1.62 18.96
Test 5 6 1.69 29.4
In the second experiment, we compare the
compactness value (C) of these different values of k
(A. Vlachou, C. Doulkeridis and Y. Kotidis, 2008).
We define this metric as:
100∗
∩
(6)
Where
is the Skyline of the partition i,
is the Skyline of the union of all Skyline partitions.
The compactness expresses how many points of
the local result set of a partition, belong also to the
global result set.
In the best case scenario, all data points that do
not belong to the Global Skyline set, would be
discarded during Local Skyline computation.
Therefore, high values of compactness indicate that
the local result sets are more compact and contain
less redundant data points.
Table 2: Test of compactness value.
Method Compactness
2-Means 38%
3-Means 63%
4-Means 88%
5-Means 69%
6-Means 76%
4-Means partitioning method had compactness
88%, showed remarkable compactness gain
compared to remaining methods. This is an expected
result since we have found best partitioning with 4-
Means (presented in Table1).
In order to validate our approach, we have tested
our approach on different data bases. We took 3
databases which are randomly generated. Each
database is characterized by Number of Web
Services, we refer to it by WS, and Number of QoS
attributes, we refer to it by QoS.In order to
experiment the impact of our method on WS and
QoS. We generated the following databases:
DB1: WS=400 and QoS=4.
DB2: WS=800 and QoS=4.
DB3: WS=400 and QoS=8.
If the size of the database increases, in term of
Number of Web Services, our Best Clustering will
have better gain compared to remaining clustering
methods.
In order to compare different databases, we will
try to use the difference between the best
compactness and the second best compactness:
DeltaCompactness (DC).
Figure 3: Data Base 1: WS=400 QoS=4.
Web Service Selection based on Parallel Cluster Partitioning and Representative Skyline
653
Figure 4: Data Base 2: WS=800 QoS=4.
Figure 5: Data Base 3: WS=400 QoS=8.
So, when we take DB1, Figure 3, The DC is
equal to 13%. And with DB 2 Figure 4, the DC is
equal to 23%. High DC means that the best
partitioning method is selected with less error
probability. This proves that with our method we
will select the best partitioning method in a Big Data
context.
If we take DB1 (WS=400 and QoS=4) and DB3
(WS=400 and QoS=8), the first database showed
DC=13% and the second database showed
DC=26%. The more QoS attributes we have the
more reliable our partitioning method selection will
be. This result is a real plus in a Big Data context.
Based on the different databases, Figure 3, 4 and 5
we may notice that compactness results and inertia
results are correlated. In future work, we may rely
on Inertia calculation when deciding about
partitioning to be used.
6.2 Parallel Cluster Partitioning and
Representative Skyline Vs Angle
Partitioning Method
In this section, we are comparing the proposed
method based on Parallel Cluster Partitioning and
Representative Skyline with the Angle Partitioning
method (Vlachou et al., 2008). We have selected the
latter because it showed better performances
compared to Grid Partitioning and Random
Partitioning (Vlachou et al, 2008). We took the same
data sets used in previous experiments. Then we
have applied the Angular Partitioning with different
space partitioning: from 2 partitions up to 6
partitions. The purpose is to compare the
compactness metric obtained with the K-Means
clustering and with the Angular method given the
same number of partitions.
Angular partitioning showed a better
compactness only when 2 partitions are considered.
Otherwise the compactness is always better when
the proposed clustering approach is used. Still, it is
clear that the higher the number of partitions, the
lower the compactness value is for both methods.
This demonstrates that with angular partitioning,
local skyline computation will be executed on
irrelevant areas which will lead to extra computation
time and extra bandwidth consumption since more
local skyline points are send to the central server
node while not contributing to the global skyline.
With the cluster based method associated with
representative skyline the risk of extra load and
inefficient local skyline computation is lowered
leading to an intelligent and efficient parallel
approach.
Figure 6: Data Base 1: WS=400 QoS=4.
Figure 7: Data Base 2: WS=800 QoS=4.
With DB1 (see figure 6), we can for example
observe that the compactness value with K-means
clustering is 81% (K=3) while its value equals 66%
for angular partitioning. For the second experiment
using DB2 (see figure 7) we obtained mainly the
ICSOFT 2018 - 13th International Conference on Software Technologies
654
same observations. For example when using K-
means the value of the compactness is C=83% given
5 partitions which is higher than the angular based
method where the obtained compactness is C=37%.
7 CONCLUSION
In this work, we presented a new parallel approach
for selecting web services based on clusters and
representative Skyline. This method consists of three
steps: Clustering, Parallel Skyline computation and
eventually Representative Skyline evaluation.
Intelligent partitioning of the data set and
parallelization allows us to minimize the
computation time and the extra bandwidth load
(better compactness) and eventually improves the
Global Skyline quality since only the efficient points
are gathered by Local Skylines. During this study we
mainly focused on the compactness metric, we will
conduct further evaluations in a future work. The
experiments carried out on different data bases
showed the effectiveness of our method. The
proposed partitioning approach gave higher values
of compactness which indicates that the local result
sets are more compact and contain less redundant
data points.
Our method which relies on representative
skyline services with intelligent clustering appeared
to be a more efficient approach. The optimization
algorithm that will be executed after this first stage
will then look for the optimal or sub-optimal
solution that will satisfy the global QoS constraints
on a more efficient data set of web services.
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