Commit Graph

4 Commits

Author SHA1 Message Date
Olaoluwa Osuntokun
8d88f98297
autopilot: update graph.go to match recent API changes 2017-08-22 00:52:31 -07:00
Olaoluwa Osuntokun
09521b9c2a
autopilot: fix linter error 2017-08-15 18:26:59 -07:00
Olaoluwa Osuntokun
8034780ca4
autopilot: fix chanID collision test flake by instead using atomic counter
This commit fixes a prior occasional test flake caused by the collision
of the randomly selected 64-bit integers. In order to get around this,
we now instead have a atomic monotonically increasing counter for each
channel ID used within the tests.
2017-08-15 17:56:24 -07:00
Olaoluwa Osuntokun
306c4aef8e
autopilot: "Look ma no hands!", introducing autopilot mode
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.
2017-08-10 21:14:55 -07:00