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Advances, Systems and Applications

Table 17 The genetic algorithm behavior in an environment with the periodic workload

From: Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker

Iteration no.

CR

CSLA

No. of rented VMs

No. of SLA breaches

Optimal Configuration

1

1

1

157

4

< 92, 68, 0, 3, 1, 0>

2

1

5

157

4

< 92, 68, 0, 3, 1, 0>

3

1

10

171

2

< 90, 65, 0, 4, 1, 0>

4

1

15

189

1

< 82, 59, 0, 4, 0, 1>

5

1

20

192

0

< 80, 55, 0, 4, 0, 1>

6

1

25

192

0

< 80, 55, 0, 4, 0, 4>

7

1

30

192

0

< 80, 55, 0, 4, 0, 4>

8

5

1

131

59

< 96, 49, 4, 0, 3, 0>

9

10

1

126

84

< 97, 56, 4, 0, 3, 0>

10

15

1

124

129

< 97, 88, 4, 0, 3, 0>

11

20

1

119

143

< 97, 88, 4, 0, 3, 0>

12

25

1

119

156

< 97, 88, 4, 0, 5, 0>

13

30

1

119

156

< 97, 88, 4, 0, 5, 0>