Move the historical bucket tracker to 32 unequal sized buckets
Currently we store our historical estimates of channel liquidity in
eight evenly-sized buckets, each representing a full octile of the
channel's total capacity. This lacks precision, especially at the
edges of channels where liquidity is expected to lie.
To mitigate this, we'd originally checked if a payment lies within
a bucket by comparing it to a sliding scale of 64ths of the
channel's capacity. This allowed us to assign penalties to payments
that fall within any more than the bottom 64th or lower than the
top 64th of a channel.
However, this still lacks material precision - on a 1 BTC channel
we could only consider failures for HTLCs above 1.5 million sats.
With today's lightning usage often including 1-100 sat payments in
tips, this is a rather significant lack of precision.
Here we rip out the existing buckets and replace them with 32
*unequal* sized buckets. This allows us to focus our precision at
the edges of a channel (where the liquidity is likely to lie, and
where precision helps the most).
We set the size of the edge buckets to 1/16,384th of the channel,
with the size increasing exponentially until it approaches the
inner buckets. For backwards compatibility, the buckets divide
evenly into octets, allowing us to convert the existing buckets
into the new ones cleanly.
This allows us to consider HTLCs down to 6,000 sats for 1 BTC
channels. In order to avoid failing to penalize channels which have
always failed, we drop the sliding scale for comparisons and simply
check if the payment is above the minimum bucket we're analyzing and
below *or in* the maximum one. This generates somewhat more
pessimistic scores, but fixes the lower bound where we suddenly
assign a 0% failure probability.
While this does represent a regression in routing performance, in
some cases the impact of not having to examine as many nodes
dominates, leading to a performance increase.
On a Xeon E3-1220 v5, the `large_mpp_routes` benchmark shows a 15%
performance increase, while the more stable benchmarks show an 8%
and 15% performance regression.