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 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.
