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.
Since we want to combine scores from multiple heuristics, things get
complicated if the heuristics report their own individual channel sizes.
Therefore we change the NodeScores interface slightly, letting the agent
specify the wanted channel size, and let the heuristic score the nodes
accordingly.
This commit adds a new method NodeScores to the AttachementHeuristic
interface. Its intended use is to score a set of nodes according to
their preference as channel counterparties.
The PrefAttach heuristic gets a NodeScores method that will score the
ndoes according to their number of already existing channels, similar to
what is done already in Select.
In this commit, we modify the Node interface to return a set of raw
bytes, rather than the full pubkey struct. We do this as within the
package, commonly we only require the pubkey bytes for fingerprinting
purposes. Before this commit, we were forced to _always_ decompress the
pubkey which can be expensive done thousands of times a second.
In this commit, we fix a regression introduced by a recent change which
would allow the agent to detect a channel as failed, and blacklist the
node, promising faster convergence with the ideal state of the
heuristic.
The source of this bug is that we would use the set of blacklisted
nodes in order to compute how many additional channels we should open.
If 10 failures happened, then we would think that we had opened up 10
channels, and stop much earlier than we actually should.
To fix this, while ensuring we don’t retry to failed peers, the
NeedMoreChans method will now return *how* anymore channels to open,
and the Select method will take in how many channels it should try to
open *exactly*.
This commit modifies the Select method for the
ConstrainedPrefAttachment attachment heuristic slightly. Previously, it
was possible for an autopilot.Agent to go over the allotted number of
channels as it would unconditionally attempt to establish channel with
all returned Attachment Directives. To remedy this, we now assume that
we already have active, or pending channels to each of the nodes in the
set of skipNodes. Therefore, we now use the size of the skipNodes map
as an upper limit within the primary selection loop.
This commit introduces the initial implementation of the autopilot
mode. Autopilot is new mode within lnd that enables automatic channel
management. This means that if enabled lnd will attempt to
automatically manage channels according to a set of heuristic defined
within the main configuration for autopilot.Agent instance.
The autopilot.Agent implements a simple closed control loop. It takes
in external signals such as wallet balance updates, new open channel,
and channels that are now closed the updates its internal state. With
each external trigger it will consult the registered
AttachmentHeuristic to decide: if it needs to open any more channels,
and if so how much it should use to open the channels, ultimately
returning a set of recommended AttachmentDirectives. The
autopilot.Agent loop will then take those attempt to establish
connection, and go back in waiting for a new external signal.
With this first implementation the default heuristic is the
ConstrainedPrefAttachment implementation of AttachmentHeuristic. Given
a min and max channel size, a limit on the number of channels, and the
percentage of wallet funds to allocate to channels, it will attempt to
execute a heuristic drive by the Barabási–Albert model model in order
to attempt to drive the global graph towards a scale free topology.
This is commit implements a foundational layer for future simulations,
optimization, and additional heuristics.
