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International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-6 - December 2016
Enactment of dynamic programming tactic
Formation of virtual topology on optical fiber
Dr.A. Ravichandra Reddy
Post-Doctoral Researcher, Department of Computer Science & Technology
Sri Krishna devaraya university, Anantapur
ABSTRACT: Fiber optic networks- WDM
technology
Construction
of the
virtual
topology on the existing physical network-Dynamic
programming model has developed to produce
stage wise different traffic matrices for
conventionally finding the shortest path from each
source to different destinations- various number of
optical transceivers. Experimental study on14
node (NSFNET) traffic matrices
Keywords: Fiber Optics, Dynamic programming,
Virtual Topology, Transceivers, Stages
but also everlasting greenhouse gas emissions. So,
reducing the power consumption or improving the
energy efficiency of Internet has been an important
research topic. presently the hierarchy of networks
is becoming flatter; accordingly, the Internet is
evolving to the networks consisting of access
networks and core networks. Due to the traffic
aggregation from access networks, core networks
tend to suffer a rapider traffic growth and energy
consumption growth than access networks. [4]
I. INTRODUCTION
Now a day’s computer networks are in
constant
fruition
and
Internet
traffic
is swiftly growing due to the fact of the influx of the
latest styles
of internet access
technologies,
bandwidth-intensive network applications and
large-volume transmission traffic.
The
next
generation networks need to be accessible,
programmable, flexible and be adaptable to
innovative ideas. Researchers continue to work on
providing innovative solutions or modifications to
the current network infrastructure. But such
experiments cannot be just about carried out on
present operational networks as these could be
interrupt the operational traffic on networks. The
experimentation and testing of new innovations and
new protocols, which can also be solved by the
virtual network infrastructure. [1,2,3]
WDM
Optical
fiber
networking
technologies brought a revolution in high-speed
communication networks, which are now able to
meet the high bandwidth demands of current voice
and data traffic. The physical topology [5] consists
of optical WDM routers interconnected by point-topoint fiber links and nodes in an arbitrary topology.
In these types of networks, data transfer carried
from one node to another node using light paths. A
light path [6] is an all-optical path established
between two nodes in the network by the allocation
of same wavelength on all links of the path. The
virtual topology is established logically through
light paths, each identified by an independent
wavelength,
which
provides
end-to-end
connectivity for transmission over the optical
medium. The Virtual Topology is a logical
arrangement with nodes as routers in the physical
network topology and edges corresponding to the
light paths between them.
Internet traffic has been increasing
exponentially each year. From 2008 to 2012,
Internet witnessed over 50% average annual growth
for traffic. The increasing traffic also causes the
growth the of Internet energy consumption. It was
estimated that the Internet in 2007 consumed 1%of
the electricity in broadband enabled countries with a
30 Mb/s average access bandwidth, and this
proportion will exceed 4% when the average access
bandwidth goes up to 300 Mb/s. Per the current
upward trend, the network field in 2050 will
consume 13 times electricity of the year in 2006
averagely. The rapid rise in energy consumption of
Internet results in not only growing electricity bills
Dynamic programming model for finding
optimal solutions (minimum-cost paths) to network
problems. The network represents the possible
connections between decisions and expresses costs
of going from one to another. The approach
encompasses a value-iterative algorithm which
successively congregates to the solution. The
principle of optimality, which permits finding the
minimum-cost (shortest) path as a function of the
maximum number of arcs allowed, is: regardless of
how a state was arrived at, the remaining decisions
as to what path to take in route to the terminal must
themselves constitute an optimal solution. The costs
of traversing arcs may be input, recalled from other
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International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-6 - December 2016
files, or calculated by from input functions
approach flexibility allows for testing the sensitivity
of a decision process to changes in the terminal
point.
0
39
78

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
The term transceiver is a network device,
but rather an integrated technology embedded in
devices such as network cards. many types of
transceivers:
RF
transceivers,
fiber-optic
transceivers, Ethernet transceivers, wireless (WAP)
transceivers, and more. Though each of these media
types is different, the function of the transceiver
remains the same. Each kind of the transceiver used
has different characteristics such as many ports
available to connect to the network and full-duplex
communication is supported. Fiber optic
transmission systems (data links). Each one consists
of a Opto eclectics transmitter on one end of a fiber
and a receiver on the other end. Most systems
operate by transmitting in one direction on one fiber
and in the reverse direction on another fiber for full
duplex operation.
93
0
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5
26
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29
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96
14
0
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7
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54
26
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77
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0
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61
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12
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0
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61
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42
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39
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0
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49
57
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


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39
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0


97


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53


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
28
97

0
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47
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
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25

40
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0


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


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23

36
17
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0
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44
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0
67
81
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

88







41
0

4


98
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





28

0
58






42




87
3
0
In the above 14-Node(NSFNET) initialized traffic
matrix, cost is assigned randomly and represented
in a traffic matrix.
0
39
78
44
65
88
105
124
56
107
68
132
104
136
II. PROPOSED WORK
Dynamic programming approach is for finding the
shortest path between any two given two nodes. The
input for the algorithm in a network G = <V, E>,
and a set of weights w i,j for each (i, j) ∈ E. algorithm finds the shortest simple path from node s to
node t. input for the algorithm is an adjacency
matrix of the graph G. The cost (i, j) is computed as
follows initially, the matrix D (0) is initialized as
cost (i, j). Then, in subsequent iterations, up to fullfledged matrices computed
The shortest path between i and j is computed as
follows
Dij (k) = min {Dij (k−1), Dik (k−1) + D kJ
(k−1)}
93
0
96
5
26
57
66
85
17
68
29
96
103
100
96
14
0
89
84
7
100
56
64
103
54
54
26
58
107
77
91
0
40
52
61
99
12
82
43
110
117
114
170
98
173
82
0
134
78
61
94
42
106
173
134
131
135
96
39
82
77
0
132
49
57
96
93
93
65
97
146
53
130
39
79
91
0
114
51
97
82
140
56
53
198
105
136
110
28
97
83
0
122
47
134
190
139
136
132
39
79
25
65
40
86
89
0
107
68
133
105
137
182
89
153
75
23
114
36
17
87
0
118
176
92
89
137
44
140
49
70
101
110
129
61
112
0
67
81
71
172
79
88
84
105
95
46
144
96
143
41
0
7
4
165
72
98
77
98
105
100
54
89
140
28
95
0
58
168
75
101
80
101
108
42
66
92
139
31
87
3
0
In the above14-Node(NSFNET) traffic matrix is
added in all directions forms the complete shortest
traffic matrix
14-node
DP
stage1
node
trans
light
14
1
13
Wave
lengths
0
13
Hop
wei
860
Tot
hop
860
Avg
hop
1
Max
conges
2 -> 12 (98)
Min
conges
7 ->9(17)
14
2
27
1
27
1567
1567
1
2 -> 12 (98)
8 ->3(12)
14
3
40
3
40
2025
2025
1
8 -> 5 (114)
12 ->13(3)
14
14
4
53
5
53
2601
2601
1
0 -> 2 (192)
13 ->11(4)
5
64
7
64
3105
3105
1
0 -> 2 (192)
3 ->1(5)
14
6
74
9
74
3617
3617
1
0 -> 2 (288)
12 ->13(6)
14
7
80
12
80
3864
3864
1
0 -> 2 (288)
12 ->13(6)
14
8
86
15
86
4175
4175
1
0 -> 2 (288)
12 ->13(6)
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International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-6 - December 2016
Stage-2
Stage 3
Stage 4
Stage 5
14
9
87
18
87
4179
4179
1
0 -> 2 (288)
12 ->13(6)
14
10
88
23
88
4183
4183
1
0 -> 2 (288)
12 ->13(6)
14
1
13
2
33
1572
4178
2.658
1 -> 10 (314)
4 ->1(70)
14
2
27
6
66
3033
8102
2.671
3 -> 1 (506)
11 ->2(58)
14
3
40
12
94
4272
10754
2.517
10 -> 12 (645)
1 ->0(68)
14
4
53
18
120
5284
12742
2.411
1 -> 10 (773)
12 ->10(81)
14
5
66
24
139
6076
14024
2.308
1 -> 10 (843)
4 ->9(23)
14
6
79
32
159
6921
15313
2.213
1 -> 10 (964)
4 ->9(23)
14
7
95
41
180
7710
16407
2.128
1 -> 10 (964)
4 ->9(23)
14
8
109
51
197
8481
17745
2.092
1 -> 10 (1008)
10 ->11(41)
14
9
123
62
223
9492
20110
2.119
1 -> 10 (1173)
10 ->11(41)
14
10
135
75
248
10404
22809
2.192
1 -> 10 (1485)
10 ->11(41)
14
1
14
2
1627
4998
3.072
1 -> 4 (371)
1 ->0(56)
14
2
27
7
79
3258
9910
3.042
6 -> 9 (621)
2 ->11(95)
14
3
41
13
117
4813
14741
3.063
2 -> 5 (797)
12 ->10(81)
14
4
55
22
152
6184
18278
2.956
1 -> 10 (992)
12 ->10(81)
14
5
69
33
183
7475
21364
2.858
2 -> 5 (1316)
12 ->10(81)
14
6
82
47
215
8804
24627
2.797
1 -> 10 (1465)
12 ->10(81)
14
7
98
62
251
10016
27433
2.739
1 -> 10 (1602)
12 ->10(81)
14
8
111
79
282
11164
30106
2.697
1 -> 10 (1735)
12 ->10(81)
14
9
124
97
308
12330
32477
2.634
1 -> 10 (1784)
12 ->10(81)
14
10
139
116
330
13102
33693
2.572
2 -> 5 (1884)
12 ->10(81)
14
1
14
2
42
1648
5359
3.252
2 -> 5 (427)
1 ->0(56)
14
2
28
8
87
3282
11063
3.371
2 -> 5 (862)
1 ->0(68)
14
3
42
16
122
4763
15097
3.17
2 -> 5 (1108)
2 ->0(136)
14
4
56
25
159
6251
19155
3.064
2 -> 5 (1244)
7 ->4(129)
14
5
70
36
200
7716
23752
3.078
1 -> 10 (1558)
13 ->12(100)
14
6
83
50
231
9092
27272
3
1 -> 10 (1784)
13 ->12(100)
14
7
97
68
270
10503
31150
2.966
1 -> 10 (2013)
13 ->12(100)
14
8
110
88
300
11714
33993
2.902
1 -> 10 (2253)
13 ->12(100)
14
9
124
109
336
12933
36902
2.853
1 -> 10 (2333)
13 ->12(100)
14
10
138
131
364
13930
38931
2.795
1 -> 10 (2391)
13 ->12(100)
14
1
14
2
42
1648
5359
3.252
2 -> 5 (427)
1 ->0(56)
14
2
28
8
87
3282
11063
3.371
2 -> 5 (862)
1 ->0(68)
14
3
42
16
122
4763
15097
3.17
2 -> 5 (1108)
2 ->0(136)
14
4
56
25
159
6251
19155
3.064
2 -> 5 (1244)
7 ->4(129)
14
5
69
36
199
7711
23850
3.093
1 -> 10 (1570)
13 ->12(100)
14
6
82
50
231
9092
27392
3.013
1 -> 10 (1796)
13 ->12(100)
14
7
95
68
270
10456
31376
3.001
1 -> 10 (2042)
13 ->12(100)
14
8
109
89
303
11751
34472
2.934
1 -> 10 (2366)
13 ->12(100)
14
9
124
111
339
12977
37298
2.874
1 -> 10 (2441)
13 ->12(100)
14
10
139
135
372
14050
39674
2.824
1 -> 10 (2579)
13 ->12(100)
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International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-6 - December 2016
Table: 1 Dynamic programming on heuristics
The above table shows the several objective functions stage-1 to Stage-5 with comparison of the proposed
heuristic of Dynamic programming approach. The objective functions utilization of Light paths, Wave Lengths,
total number of Physical hops, Hop Weight, Total Hop weight, Average hop Weight, Maximum Congestion, and
Minimum Congestion at each stage after Implementation of the Dynamic Programming approach on 14-Node
(NSFNET) traffic matrix used up to 10 transceivers
light paths on 14-node
150
physical hops on 14 node
400
physical hops
300
light paths
100
200
50
100
0
0
0
1 stage-1
2
3
4 stagge-2
5
6
stage-4
stage-5
transceivers
7 stage-3
8
9
1 stage-1
2
3
stage-4
Fig: 1: Implementation of Dynamic programming traffic
matrix on14-Node(NSFNET) Network light paths VS
transceivers. makes difference in the utilization of light
paths when the transceivers are increased and complete
matrix better utilization of light paths
4 stagge-2
5
6
7stage-3
8
9
10
transceivers
stage-5
Fig: 3: Implementation of Dynamic programming traffic
matrix on14-Node(NSFNET) Network physical hops VS
transceivers. makes gradually difference in the usage of
physical hops when the transceivers are increased and
complete matrix better usage of physical hop weight
wave lengths on14 node
150
wave lengths
0
10
hop weight on 14-node
15000
100
hop weight
10000
50
5000
0
0
1
2
3
stage-1
stage-4
4
5
6
7
8
stagge-2
stage-3
transceivers
9
10
stage-5
0
0
Fig: 2: Implementation of Dynamic programming traffic
matrix on14-Node(NSFNET) Network wave lengths VS
transceivers. makes gradually difference in the utilization
of wave lengths when the transceivers are increased and
complete matrix better utilization of wave lengths
ISSN: 2231-5381
1 stage-1
2
3
4stagge-2
5
6
stage-4
stage-5
transceivers
7stage-3
8
Fig: 4: Implementation of Dynamic programming traffic
matrix on14-Node(NSFNET) Network hop weight VS
transceivers. makes gradually difference in the usage of
hop weight when the transceivers are increased and
complete matrix better usage of hop weight
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9
10
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-6 - December 2016
shows the utilization of light paths, wave lengths,
usage of physical hops, hop weight, total hop
weight, and average hop weight correspondingly.
Observation is made that in all cases stage-5
transceivers grow and slight differences are also
observed in effect on certain stages
total hop weight on 14-node
50000
total hop weight
40000
30000
ACKNOWLEDGMENT
20000
10000
0
0
1 stage-1
2
3
4stagge-2
5
6
transceivers
stage-4
7
8
stage-3
9
10
Dr.A.Ravichandra Reddy is a Post-Doctoral
Researcher. He is completed Ph.D. in the year of
2016, his master of sciene his Master of Science
(MSc) in the year 2009 Sri Krishnadevaraya
University, Anantapur
stage-5
REFERENCES
Fig: 5: Implementation of Dynamic programming traffic
matrix on14-Node(NSFNET) Network Total hop weight
VS transceivers. makes gradually difference in the usage
of Total hop weight when the transceivers are increased
and complete matrix better usage of Total hop weight
[1].
[2]
average hop weight on 14-node
4
[3]
average hop weight
3.5
3
[4]
[5]
2.5
2
1.5
[6]
1
0.5
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in
elastic
optical
networks,”
in
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2012.
Roberto Bifulco, Roberto Canonico, Marcus Brunner,
Peer Hasselmeyer, Faisal Mir, “A Practical Experience
in Designing an Open Flow Controller”, European
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International Journal of Communication Systems, 2015,
Rajesh M. Krishnaswamy, Kumar N. Sivarajan, “Design
of logical topologies: A linear formulation for
wavelength-routed optical networks with no wavelength
changers”, IEEE/ACM Transactions on Networking,
April 2001, vol. 9, no.2, pp. 186-198.
R.Ramaswami, K.N.Sivarajan, “Design of logical
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IEEE Selected Areasin Communication, June 1996, vol.
4, no.5, pp. 840-851.
0
0
1
stage-1
2
3
4 stagge-2
5
6
stage-4
transceivers
stage-5
stage-3
7
8
9
10
Fig: 6: Implementation of Dynamic programming traffic
matrix on14-Node(NSFNET) Network average hop
weight VS transceivers. makes gradually difference in the
usage of average hop weight when the transceivers are
increased and complete matrix better usage of average
hop weight
IV. CONCLUSION
the aim of this paper is to find shortest path by
using dynamic programming on stage wise traffic
matrices and formation of virtual topology using
proposed heuristics. Experimental is made on 14
node (NSFNET) up to 10 transceivers. fig 1 to 6
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