Merge pull request #124 from weaveworks/tweak-coefficients

Tweak some coefficients

gossip_test.go

@@ -294,7 +294,7 @@ func TestRandomNeighbours(t *testing.T) {
 			// Run randomNeighbours() several times, and count the distribution
 			for trial := 0; trial < nTrials; trial++ {
 				targets := r.randomNeighbours(ourself)
-				expected := int(math.Min(math.Log2(float64(test.nPeers)), float64(test.nNeighbours)))
+				expected := int(math.Min(2*math.Log2(float64(test.nPeers)), float64(test.nNeighbours)))
 				require.Equal(t, expected, len(targets))
 				total += len(targets)
 				for _, p := range targets {

router.go

@@ -24,8 +24,8 @@ var (
 const (
 	tcpHeartbeat     = 30 * time.Second
 	maxDuration      = time.Duration(math.MaxInt64)
-	acceptMaxTokens  = 100
-	acceptTokenDelay = 100 * time.Millisecond // [2]
+	acceptMaxTokens  = 20
+	acceptTokenDelay = 50 * time.Millisecond
 )
 
 // Config defines dimensions of configuration for the router.

routes.go

@@ -128,15 +128,15 @@ func (r *routes) lookupOrCalculate(name PeerName, broadcast *broadcastRoutes, es
 	return <-res
 }
 
-// RandomNeighbours chooses min(log2(n_peers), n_neighbouring_peers)
+// RandomNeighbours chooses min(2 log2(n_peers), n_neighbouring_peers)
 // neighbours, with a random distribution that is topology-sensitive,
 // favouring neighbours at the end of "bottleneck links". We determine the
 // latter based on the unicast routing table. If a neighbour appears as the
 // value more frequently than others - meaning that we reach a higher
 // proportion of peers via that neighbour than other neighbours - then it is
 // chosen with a higher probability.
 //
-// Note that we choose log2(n_peers) *neighbours*, not peers. Consequently, on
+// Note that we choose 2log2(n_peers) *neighbours*, not peers. Consequently, on
 // sparsely connected peers this function returns a higher proportion of
 // neighbours than elsewhere. In extremis, on peers with fewer than
 // log2(n_peers) neighbours, all neighbours are returned.
@@ -152,7 +152,7 @@ func (r *routes) randomNeighbours(except PeerName) []PeerName {
 			weights[dst]++
 		}
 	}
-	needed := int(math.Min(math.Log2(float64(len(r.unicastAll))), float64(len(weights))))
+	needed := int(math.Min(2*math.Log2(float64(len(r.unicastAll))), float64(len(weights))))
 	destinations := make([]PeerName, 0, needed)
 	for len(destinations) < needed {
 		// Pick a random point on the distribution and linear search for it