5b1e72a019
Since NodeScores no longer returns fully populated AttachmentDirectives, we make this explicit by defining a new type NodeScore that includes a subset of what the AttachmentDirective does.
140 lines
4.3 KiB
Go
140 lines
4.3 KiB
Go
package autopilot
|
||
|
||
import (
|
||
prand "math/rand"
|
||
"time"
|
||
|
||
"github.com/btcsuite/btcd/btcec"
|
||
"github.com/btcsuite/btcutil"
|
||
)
|
||
|
||
// PrefAttachment is an implementation of the AttachmentHeuristic interface
|
||
// that implement a non-linear preferential attachment heuristic. This means
|
||
// that given a threshold to allocate to automatic channel establishment, the
|
||
// heuristic will attempt to favor connecting to nodes which already have a set
|
||
// amount of links, selected by sampling from a power law distribution. The
|
||
// attachment is non-linear in that it favors nodes with a higher in-degree but
|
||
// less so than regular linear preferential attachment. As a result, this
|
||
// creates smaller and less clusters than regular linear preferential
|
||
// attachment.
|
||
//
|
||
// TODO(roasbeef): BA, with k=-3
|
||
type PrefAttachment struct {
|
||
}
|
||
|
||
// NewPrefAttachment creates a new instance of a PrefAttachment heuristic.
|
||
func NewPrefAttachment() *PrefAttachment {
|
||
prand.Seed(time.Now().Unix())
|
||
return &PrefAttachment{}
|
||
}
|
||
|
||
// A compile time assertion to ensure PrefAttachment meets the
|
||
// AttachmentHeuristic interface.
|
||
var _ AttachmentHeuristic = (*PrefAttachment)(nil)
|
||
|
||
// NodeID is a simple type that holds an EC public key serialized in compressed
|
||
// format.
|
||
type NodeID [33]byte
|
||
|
||
// NewNodeID creates a new nodeID from a passed public key.
|
||
func NewNodeID(pub *btcec.PublicKey) NodeID {
|
||
var n NodeID
|
||
copy(n[:], pub.SerializeCompressed())
|
||
return n
|
||
}
|
||
|
||
// NodeScores is a method that given the current channel graph and
|
||
// current set of local channels, scores the given nodes according to
|
||
// the preference of opening a channel of the given size with them.
|
||
//
|
||
// The heuristic employed by this method is one that attempts to promote a
|
||
// scale-free network globally, via local attachment preferences for new nodes
|
||
// joining the network with an amount of available funds to be allocated to
|
||
// channels. Specifically, we consider the degree of each node (and the flow
|
||
// in/out of the node available via its open channels) and utilize the
|
||
// Barabási–Albert model to drive our recommended attachment heuristics. If
|
||
// implemented globally for each new participant, this results in a channel
|
||
// graph that is scale-free and follows a power law distribution with k=-3.
|
||
//
|
||
// The returned scores will be in the range [0.0, 1.0], where higher scores are
|
||
// given to nodes already having high connectivity in the graph.
|
||
//
|
||
// NOTE: This is a part of the AttachmentHeuristic interface.
|
||
func (p *PrefAttachment) NodeScores(g ChannelGraph, chans []Channel,
|
||
chanSize btcutil.Amount, nodes map[NodeID]struct{}) (
|
||
map[NodeID]*NodeScore, error) {
|
||
|
||
// Count the number of channels in the graph. We'll also count the
|
||
// number of channels as we go for the nodes we are interested in.
|
||
var graphChans int
|
||
nodeChanNum := make(map[NodeID]int)
|
||
if err := g.ForEachNode(func(n Node) error {
|
||
var nodeChans int
|
||
err := n.ForEachChannel(func(_ ChannelEdge) error {
|
||
nodeChans++
|
||
graphChans++
|
||
return nil
|
||
})
|
||
if err != nil {
|
||
return err
|
||
}
|
||
|
||
// If this node is not among our nodes to score, we can return
|
||
// early.
|
||
nID := NodeID(n.PubKey())
|
||
if _, ok := nodes[nID]; !ok {
|
||
return nil
|
||
}
|
||
|
||
// Otherwise we'll record the number of channels.
|
||
nodeChanNum[nID] = nodeChans
|
||
|
||
return nil
|
||
}); err != nil {
|
||
return nil, err
|
||
}
|
||
|
||
// If there are no channels in the graph we cannot determine any
|
||
// preferences, so we return, indicating all candidates get a score of
|
||
// zero.
|
||
if graphChans == 0 {
|
||
return nil, nil
|
||
}
|
||
|
||
existingPeers := make(map[NodeID]struct{})
|
||
for _, c := range chans {
|
||
existingPeers[c.Node] = struct{}{}
|
||
}
|
||
|
||
// For each node in the set of nodes, count their fraction of channels
|
||
// in the graph, and use that as the score.
|
||
candidates := make(map[NodeID]*NodeScore)
|
||
for nID, nodeChans := range nodeChanNum {
|
||
|
||
_, ok := existingPeers[nID]
|
||
|
||
switch {
|
||
|
||
// If the node is among or existing channel peers, we don't
|
||
// need another channel.
|
||
case ok:
|
||
continue
|
||
|
||
// If the node had no channels, we skip it, since it would have
|
||
// gotten a zero score anyway.
|
||
case nodeChans == 0:
|
||
continue
|
||
}
|
||
|
||
// Otherwise we score the node according to its fraction of
|
||
// channels in the graph.
|
||
score := float64(nodeChans) / float64(graphChans)
|
||
candidates[nID] = &NodeScore{
|
||
NodeID: nID,
|
||
Score: score,
|
||
}
|
||
}
|
||
|
||
return candidates, nil
|
||
}
|