Network_Simulation/net_simulation.py at master · PanOrka/Network_Simulation · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import networkx as nx
from matplotlib import pyplot as plt
import numpy as np
from random import randint
from statistics import mean


packet_size = 512


def a_func_def(Net, matrix_flow, nodes):
    ''' Obliczanie funkcji przeplywu (wagi krawedzi w grafie podanym w parametrze) '''
    # Pierw czyscimy wagi
    for i, k in Net.edges:
        Net[i][k]['weight'] = 0
    for i in range(nodes-1):
        for k in range(i+1, nodes):
            try:
                path = nx.astar_path(Net, i, k, weight=None)
                for j in range(len(path)-1):
                    Net[path[j]][path[j+1]]['weight'] += matrix_flow[i][k]
                    Net[path[j]][path[j+1]]['weight'] += matrix_flow[k][i]
            except nx.NetworkXNoPath:
                print("Path not found\n")


def c_func_def(Net, nodes, multiplayer=packet_size*15): # mozna wyslac x razy wiecej pakietow
    ''' Obliczanie funkcji przepustowosci (wagi krawedzi w zwroconym grafie) '''
    c_func = Net.copy()
    weights = []
    # liczymy srednia z nie-zerowych wartosci wag, czyli przeplywu
    for i, k in Net.edges:
        if c_func[i][k]['weight'] != 0:
            weights += [c_func[i][k]['weight']]
    avg_weights = mean(weights)
    # mnozymy przez multiplayer wagi lub ustalamy jako srednia z nie-zerowych wartosci wag
    for i, k in Net.edges:
        if c_func[i][k]['weight'] == 0:
            c_func[i][k]['weight'] = multiplayer*int(avg_weights)
        else:
            c_func[i][k]['weight'] = max(c_func[i][k]['weight']*multiplayer, int(avg_weights)*multiplayer) # wyznaczam przepustowosc krawedzi jako max z tych 2 wartosci
    return c_func


def show_graph(graph):
    ''' Rysowanie grafu, odleglosci zachowane jako wagi '''
    pos = nx.kamada_kawai_layout(graph, dist=None)
    nx.draw_networkx(graph, pos)
    labels = nx.get_edge_attributes(graph, 'weight')
    nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)
    plt.show()


def create_network_graph(edges, nodes):
    ''' Generowanie grafu, kazdy wierzcholek ma przynajmniej dwie krawedzie '''
    edges_temp = 0
    Net = nx.Graph()
    for i in range(nodes):
        Net.add_node(i)
    if nodes <= edges:
        rand_perm = np.random.permutation(nodes)
        for i in range(nodes-1):
            edges_temp += 1
            Net.add_edge(rand_perm[i], rand_perm[i+1], weight=0)
        edges_temp += 1
        Net.add_edge(rand_perm[-1], rand_perm[0], weight=0)
    while edges_temp != edges:
        while True:
            i, j = randint(0, nodes-1), randint(0, nodes-1)
            if not Net.has_edge(i, j) and i != j:
                Net.add_edge(i, j, weight=0)
                edges_temp += 1
                break

    return Net


def T(Net, matrix_flow, c_func, m=packet_size):
    matrix_flow_sum = 0
    for row in matrix_flow:
        matrix_flow_sum += sum(row)
    r_sum = 0
    for i, k in Net.edges:
        if ((c_func[i][k]['weight']/m) - Net[i][k]['weight'] > 0):
            r_sum += Net[i][k]['weight']/((c_func[i][k]['weight']/m) - Net[i][k]['weight'])
        else:
            print("FAILURE")
            exit(1)
    return r_sum/matrix_flow_sum


def ts_testing(Net, matrix_flow, c_func, p=90, T_max=0.00115, iterations=1000):
    success = 0
    for i in range(iterations):
        temp_net = Net.copy()
        for i, k in temp_net.edges:
            if p < randint(1, 100) <= 100:
                temp_net.remove_edge(i, k)
                if not nx.is_connected(temp_net): # jesli go rozspojnimy to wracamy
                    temp_net.add_edge(i, k, weight=0)
        a_func_def(temp_net, matrix_flow, len(temp_net))
        actual_T = T(temp_net, matrix_flow, c_func)
        if actual_T < T_max:
            success += 1
    print("Prob =", success/iterations)


def delta_matrix_flow(Net, matrix_flow, c_func, iterations=10):
    temp_matrix_flow = matrix_flow.copy()
    for i in range(iterations):
        for _ in range(randint(5, 2*len(matrix_flow))):
            c1 = randint(0, len(matrix_flow)-1)
            c2 = randint(0, len(matrix_flow)-1)
            while c1 == c2:
                c2 = randint(0, len(matrix_flow)-1)
            delta_packet = randint(1, 10)
            matrix_flow[c1][c2] += delta_packet
        print("////////////////////////////////////////////")
        print("Changed matrix_flow #" + str(i+1))
        print(np.matrix(temp_matrix_flow))
        print("After change #" + str(i+1))
        ts_testing(Net, temp_matrix_flow, c_func)
        print("////////////////////////////////////////////")


def delta_c_func(Net, matrix_flow, c_func, iterations=10):
    temp_c_func = c_func.copy()
    for j in range(iterations):
        for i, k in temp_c_func.edges:
            if randint(0, 1) == 1:
                delta_packet = randint(5, 100)
                temp_c_func[i][k]['weight'] += temp_c_func[i][k]['weight']//delta_packet
        print("////////////////////////////////////////////")
        print("Changed c_func #" + str(j+1))
        ts_testing(Net, matrix_flow, temp_c_func)
        print("////////////////////////////////////////////")


def delta_topology(Net, matrix_flow, c_func, iterations=10):
    avg_weight = 0.0
    for i, k in c_func.edges:
        avg_weight += c_func[i][k]['weight']
    avg_weight /= len(c_func.edges)
    temp_net = Net.copy()
    temp_c_func = c_func.copy()
    for i in range(iterations):
        e1 = randint(0, len(temp_net)-1)
        e2 = randint(0, len(temp_net)-1)
        while e1 == e2 or temp_net.has_edge(e1, e2): # tutaj zmieniam obydwa w razie gdyby nie dalo sie poprowadzic krawedzi z zadnego z nich
            e1 = randint(0, len(temp_net)-1)
            e2 = randint(0, len(temp_net)-1)
        temp_net.add_edge(e1, e2, weight=0)
        temp_c_func.add_edge(e1, e2, weight=avg_weight)
        print("////////////////////////////////////////////")
        print("Changed toplogy #" + str(i+1))
        print("Added edge (" + str(e1) + ", " + str(e2) + ") with c_func avg = " + str(avg_weight))
        ts_testing(temp_net, matrix_flow, temp_c_func)
        print("////////////////////////////////////////////")


def net(edges=29, nodes=20):
    matrix_flow = [[randint(0, 10) if i != k else 0 for k in range(nodes)] for i in range(nodes)] # max 10 pakietow
    print("Starting matrix_flow")
    print(np.matrix(matrix_flow))
    Net = create_network_graph(edges, nodes)
    a_func_def(Net, matrix_flow, nodes) # przeplyw jako wagi grafu
    c_func = c_func_def(Net, nodes) # przepustowosc
    show_graph(Net)
    show_graph(c_func)
    ts_testing(Net, matrix_flow, c_func)
    delta_matrix_flow(Net, matrix_flow, c_func)
    delta_c_func(Net, matrix_flow, c_func)
    delta_topology(Net, matrix_flow, c_func)


if __name__ == "__main__":
    net()