This PR fixes an issue that happens when adding a new channel edge
between two nodes in a memChannelGraph. Originally a channel edge held a
node value which made the graph different when iterating from the two
endpoints of an edge. This is simply fixed by holding pointers instead.
This commit adds a new signal to the autopilot agent, meant to signal
when any of the available heuristics has gotten an update.
We currently use this to trigger a new channel opening after the
external scores have been updated.
When appending to a slice, there is no guarantee the slice won't be
modified. So instead of appending to the global slice
availableHeuristics, we create a temporary local one.
Previously we waited only for the number of channels to become what we
expected, but this wasn't enough. Sometimes the agent had't yet updated
its internal balance, causing the test to fail with an unexpected
balance.
To make the autopilot able to account for fees, we let it use the
subtractFees option when opening channels.
This makes sure that each channel we attempt to open will eat at most
Amt out of our budget. Previously fees would eat into our funds in
addition, causing us to deplete our funds more than expected on each
channel opening.
This commit moves the logic querying the available heuristics out of the
autopilot agent and into the autopilot manager. This lets us query the
heuristic without the autopilot agent being active.
If called without the agent being active, the current set of channels
will be considered by the heuristics. If the agent is active also the
pending channels will be considered.
This commit fixes a regression in how we allocate funds to attempted
channels. We would earlier stay within the channel size limits, but we
wouldn't account for funds consumed by other channels being opened in
parallel.
We fix this by introducing a loop which greadily tries to distribute the
funds among the channels to open, and reduces the number of channels to
open in case not enough funds are available to satisfy the channel size
limits.
ScoreSettable is an interface that let caller set external scores for
the heuristic. The ExternalScoreAttachment and WeightedCombAttachment
heuristics implement this interface.
This commit adds a method SetNodesScores to the WeightedCombAttachment
heuristic.
Since the heuristic keeps a list of sub-heuristics, it will attempt to
recursively apply the scores to the sub heuristics.
This commit adds a new autopilot heuristic that is scoring based. It is
a simple heuristic that will keep a list of pubkeys and scores, and will
try opening channels with the nodes with the largest score first.
To ensure a call to ConnectToPeer doesn't block the agent from
shutting down, we'll launch it in a non-waitgrouped goroutine, that
will signal when a result is returned.
In this commit:
* we partition lnwire.ChanUpdateFlag into two (ChanUpdateChanFlags and
ChanUpdateMsgFlags), from a uint16 to a pair of uint8's
* we rename the ChannelUpdate.Flags to ChannelFlags and add an
additional MessageFlags field, which will be used to indicate the
presence of the optional field HtlcMaximumMsat within the ChannelUpdate.
* we partition ChannelEdgePolicy.Flags into message and channel flags.
This change corresponds to the partitioning of the ChannelUpdate's Flags
field into MessageFlags and ChannelFlags.
Co-authored-by: Johan T. Halseth <johanth@gmail.com>
This commit defines a new heuristic WeightedCombAttachment that takes a
set of sub-heuristics, and produces a final node score by querying the
sub-heuristics and combining the scores from them according to their
weights.
This way it will look like a regular, single heuristic to the autopilot
agemnt, but can be a more complex combination of several.
To prepare for combinning scores from multiple heuristics, we require the
scores returned from the NodeSores API to be in the range [0.0, 1.0].
The prefAttach heuristic is altered to scale the returned scores such
that the most connected node in the grpah is given a score of 1.0.
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.
We create a new type NodeScore which is a tuple (NodeID, score). The
weightedChoice and chooseN algorithms are altered to expect this type.
This is done in order to simplify the types we are using, since we were
only using a subset of the fields in AttachmentDirective.
