This commit was previously split into the following parts to ease
review:
- 2d746f68: replace imports
- 4008f0fd: use ecdsa.Signature
- 849e33d1: remove btcec.S256()
- b8f6ebbd: use v2 library correctly
- fa80bca9: bump go modules
To further separate the channel graph from the channel state, we
refactor the AddrsForNode method to use the graphs's public methods
instead of directly accessing any buckets. This makes sure that we can
have the channel state cached with just its buckets while not using a
kvdb level cache for the graph.
At the same time we refactor the graph's test to also be less dependent
upon the channel state DB.
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.
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>
In this commit, we implement an optimization to the autopilot agent to
ensure that we don't spin and waste CPU when we either have a large
graph, or a high max channel target for the agent. Before this commit,
each time we went to read the state of a channel from disk, we would
decompress the EC Point each time. However, for the case of the instal
ChannlEdge struct to feed to the agent, we only actually need to obtain
the pubkey, and can save the potentially expensive point decompression
for each directional channel in the graph.
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.
The commit ensures that for every channel, there will always
be two entries in the edges bucket. If the policy from one or
both ends of the channel is unknown, it is marked as such.
This allows efficient lookup of incoming edges. This is
required for backwards payment path finding.
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.
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.
