TELKOMNIKA ISSN: 1693-6930
A Reliable Web Services Selection Method for Concurrent Requests Guiming Lu 1059
5. Simulations 5.1 The experiment design
Suppose there are 300 candidate Web services. Consider the five common QoS attributes: Cost, Response Time, Availability, Reliability, Load, and QoS values of candidates
Web services are generated randomly within a certain range. The range of them as follows:
3 0 0 C
,
3 0 T
s e c
,
0 .7 1
A va
,
0 .7 5 1
R e l
,
3 0 0 L o a
. Meanwhile set 6 concurrent users, and its QoS values are fixed as shown in Table 5.1, the attribute weights
were set to:
0 . 2 , 0 . 1 , 0 . 1 5 , 0 . 2 5 , 0 . 3 W
; The experiment achieves the ISCO algorithm through C + + programming language , and the configuration of the PC which is run by the
algorithm is: CPU PentiumR4 2.66GHZ
，
Memory 4G OS Microsoft Windows 7
。
Table 1. The QoS constraints of concurrent users
UserID C T Ava Rel
Loa
1 50 10
0.75 0.80
200 2 150
20 0.85
0.90 100
3 80 15
0.80 0.85
150 4 200
10 0.95
0.80 250
5 75 25
0.90 0.95
50 6 150
10 0.85
0.75 100
5.2 The determination of ISCO algorithm parameters
The values of key parameters in the ISCO algorithm have a great influence on the performance of the algorithm, and so the appropriate values should be set in the algorithm
parameters before applying ISCO to solve Web service selection problem in order to give full play to the algorithms ability. The key parameters of ISCO algorithm include: population size,
namely the number of initial solutions
POP
N
, the number of learning agents
c
N
, maximum number of iterations
MAX
N
, the number of optimal solutions what are draw in the imitation learning competition
λ. 1 Determine the parameters: Population size
P O P
N
Population size
P O P
N
is the number of initial solutions. In the experiment, set the number of excellent solutions presented as
λ = 3; Because
3
P O P c
N N
in ISCO algorithm, so
P O P
N
,
c
N
are considered setting different values of the following: 1
1 0 0 , 3 0;
P O P c
N N
2
1 5 0 , 5 0;
P O P c
N N
3
2 0 0, 7 0;
P O P c
N N
4
2 5 0 , 8 0 ;
P O P c
N N
5
3 0 0 , 1 0 0 ;
P O P c
N N
The maximum numbers of iterations
M A X
N
are taken respectively 10,100,1000,10000, then execute the algorithm and record the searched solution in algorithm. The results are shown
in Figure 5.4, abscissa axis represents different values of
P O P
N
, vertical axis represents the evaluation values of solutions that are searched by algorithm when
P O P
N
have different values. It is found in experiments that regardless what is the value of maximum number of
iterations of the algorithm
M A X
N
, when
2 0 0 , 7 0 ;
P O P c
N N
the algorithm search ability is strongest, that is to say
MAX
N
has no effect on the selection of
P O P
N
.
ISSN: 1693-6930
TELKOMNIKA Vol. 12, No. 4, December 2014: 1053 – 1063
1060
Figure 3. The influence of the number of initial solutions on the performance of the algorithm 2 Determine the parameters: The number of excellent solutions proposed
λ At the initial moment, set the amount of the population, namely the number of initial
solutions
2 0
P O P
N
, the number of learning agent
7 0 ;
c
N
the maximum number of iterations of the algorithm
m a x
5 0 0 0 N
.In observational learning, each time do crossover on 2 nodes and then do variation on that 2 nodes where crossover and variation points are randomly
generated, in order to improve the algorithms search ability and convergence speed, it need to determine the value of
λthe number of excellent solutions extracted.λ is set from 1 to 10, execute the algorithm and record the solutions that are searched by the algorithm. The
experimental results are shown in Figure 4,, and the abscissa axis represents different values of λ, the ordinate axis represents the evaluation values of the solutions that are searched by the
algorithm. As can be seen from Figure 4, when λ = 7 the algorithm has the strongest searching
capabilities, and therefore determine λ= 7.
Figure 4. The influence of λthe number of solutions extracted on the performance of the
algorithm
3.0 3.2
3.4 3.6
3.8 4.0
4.2 4.4
4.6 4.8
100 150
200 250
300
ev aluating
v alue
the number of initial solutions
iterating 10 iterating 100
iterating 1000 iterating 10000
3.2 3.3
3.3 3.4
3.4 3.5
3.5
1 2
3 4
5 6
7 8
9 10
evaluating value
the number of solutions extracted λ
TELKOMNIKA ISSN: 1693-6930
A Reliable Web Services Selection Method for Concurrent Requests Guiming Lu 1061
5.3 Comparison with other algorithms
