-
Notifications
You must be signed in to change notification settings - Fork 594
/
phantom.go
187 lines (168 loc) · 5.53 KB
/
phantom.go
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
178
179
180
181
182
183
184
185
186
187
package coordinate
import (
"fmt"
"math"
"math/rand"
"time"
)
// GenerateClients returns a slice with nodes number of clients, all with the
// given config.
func GenerateClients(nodes int, config *Config) ([]*Client, error) {
clients := make([]*Client, nodes)
for i, _ := range clients {
client, err := NewClient(config)
if err != nil {
return nil, err
}
clients[i] = client
}
return clients, nil
}
// GenerateLine returns a truth matrix as if all the nodes are in a straight linke
// with the given spacing between them.
func GenerateLine(nodes int, spacing time.Duration) [][]time.Duration {
truth := make([][]time.Duration, nodes)
for i := range truth {
truth[i] = make([]time.Duration, nodes)
}
for i := 0; i < nodes; i++ {
for j := i + 1; j < nodes; j++ {
rtt := time.Duration(j-i) * spacing
truth[i][j], truth[j][i] = rtt, rtt
}
}
return truth
}
// GenerateGrid returns a truth matrix as if all the nodes are in a two dimensional
// grid with the given spacing between them.
func GenerateGrid(nodes int, spacing time.Duration) [][]time.Duration {
truth := make([][]time.Duration, nodes)
for i := range truth {
truth[i] = make([]time.Duration, nodes)
}
n := int(math.Sqrt(float64(nodes)))
for i := 0; i < nodes; i++ {
for j := i + 1; j < nodes; j++ {
x1, y1 := float64(i%n), float64(i/n)
x2, y2 := float64(j%n), float64(j/n)
dx, dy := x2-x1, y2-y1
dist := math.Sqrt(dx*dx + dy*dy)
rtt := time.Duration(dist * float64(spacing))
truth[i][j], truth[j][i] = rtt, rtt
}
}
return truth
}
// GenerateSplit returns a truth matrix as if half the nodes are close together in
// one location and half the nodes are close together in another. The lan factor
// is used to separate the nodes locally and the wan factor represents the split
// between the two sides.
func GenerateSplit(nodes int, lan time.Duration, wan time.Duration) [][]time.Duration {
truth := make([][]time.Duration, nodes)
for i := range truth {
truth[i] = make([]time.Duration, nodes)
}
split := nodes / 2
for i := 0; i < nodes; i++ {
for j := i + 1; j < nodes; j++ {
rtt := lan
if (i <= split && j > split) || (i > split && j <= split) {
rtt += wan
}
truth[i][j], truth[j][i] = rtt, rtt
}
}
return truth
}
// GenerateCircle returns a truth matrix for a set of nodes, evenly distributed
// around a circle with the given radius. The first node is at the "center" of the
// circle because it's equidistant from all the other nodes, but we place it at
// double the radius, so it should show up above all the other nodes in height.
func GenerateCircle(nodes int, radius time.Duration) [][]time.Duration {
truth := make([][]time.Duration, nodes)
for i := range truth {
truth[i] = make([]time.Duration, nodes)
}
for i := 0; i < nodes; i++ {
for j := i + 1; j < nodes; j++ {
var rtt time.Duration
if i == 0 {
rtt = 2 * radius
} else {
t1 := 2.0 * math.Pi * float64(i) / float64(nodes)
x1, y1 := math.Cos(t1), math.Sin(t1)
t2 := 2.0 * math.Pi * float64(j) / float64(nodes)
x2, y2 := math.Cos(t2), math.Sin(t2)
dx, dy := x2-x1, y2-y1
dist := math.Sqrt(dx*dx + dy*dy)
rtt = time.Duration(dist * float64(radius))
}
truth[i][j], truth[j][i] = rtt, rtt
}
}
return truth
}
// GenerateRandom returns a truth matrix for a set of nodes with normally
// distributed delays, with the given mean and deviation. The RNG is re-seeded
// so you always get the same matrix for a given size.
func GenerateRandom(nodes int, mean time.Duration, deviation time.Duration) [][]time.Duration {
rand.Seed(1)
truth := make([][]time.Duration, nodes)
for i := range truth {
truth[i] = make([]time.Duration, nodes)
}
for i := 0; i < nodes; i++ {
for j := i + 1; j < nodes; j++ {
rttSeconds := rand.NormFloat64()*deviation.Seconds() + mean.Seconds()
rtt := time.Duration(rttSeconds * secondsToNanoseconds)
truth[i][j], truth[j][i] = rtt, rtt
}
}
return truth
}
// Simulate runs the given number of cycles using the given list of clients and
// truth matrix. On each cycle, each client will pick a random node and observe
// the truth RTT, updating its coordinate estimate. The RNG is re-seeded for
// each simulation run to get deterministic results (for this algorithm and the
// underlying algorithm which will use random numbers for position vectors when
// starting out with everything at the origin).
func Simulate(clients []*Client, truth [][]time.Duration, cycles int) {
rand.Seed(1)
nodes := len(clients)
for cycle := 0; cycle < cycles; cycle++ {
for i, _ := range clients {
if j := rand.Intn(nodes); j != i {
c := clients[j].GetCoordinate()
rtt := truth[i][j]
node := fmt.Sprintf("node_%d", j)
clients[i].Update(node, c, rtt)
}
}
}
}
// Stats is returned from the Evaluate function with a summary of the algorithm
// performance.
type Stats struct {
ErrorMax float64
ErrorAvg float64
}
// Evaluate uses the coordinates of the given clients to calculate estimated
// distances and compares them with the given truth matrix, returning summary
// stats.
func Evaluate(clients []*Client, truth [][]time.Duration) (stats Stats) {
nodes := len(clients)
count := 0
for i := 0; i < nodes; i++ {
for j := i + 1; j < nodes; j++ {
est := clients[i].DistanceTo(clients[j].GetCoordinate()).Seconds()
actual := truth[i][j].Seconds()
error := math.Abs(est-actual) / actual
stats.ErrorMax = math.Max(stats.ErrorMax, error)
stats.ErrorAvg += error
count += 1
}
}
stats.ErrorAvg /= float64(count)
fmt.Printf("Error avg=%9.6f max=%9.6f\n", stats.ErrorAvg, stats.ErrorMax)
return
}