[VOL-2538] Logging - Implement dynamic log levels in ofagent
Change-Id: I9582230d9d3c34ea84339fddf2b2f3b3d2804808
diff --git a/vendor/github.com/hashicorp/serf/coordinate/phantom.go b/vendor/github.com/hashicorp/serf/coordinate/phantom.go
new file mode 100644
index 0000000..6fb033c
--- /dev/null
+++ b/vendor/github.com/hashicorp/serf/coordinate/phantom.go
@@ -0,0 +1,187 @@
+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
+}