This document proposes a flocking-based routing algorithm for offline blockchain channels to balance load. It converts channels to nodes in a network and assigns coordinates. Channels move away from overused channels (repulsion zone) and toward underused channels (attraction zone). The coordinate updating process aims to evenly distribute channel usage. Evaluation on Bitcoin Lightning Network data shows the algorithm maintains low standard deviation of channel balances compared to shortest path routing and completes more payments with shorter paths on average.

1. Subhasis Thakur and John Breslin
Data Science Institute
National University of Ireland Galway
A balanced routing
algorithm for blockchain
offline channels using
flocking

2. -
- SFI Research Centre
- Precision pasture-based
dairying (agtech)
- Blockchain applications
in the dairy supply chain
2

3. Scalability problem
3

4. Offline channels
4

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Load balancing
problem in the
Lightning Network

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Privacy-preserved
load balancing
problem
How can we ensure uniform
usage of channels without
revealing channel balances?

21. Flocking-based routing21
https://commons.wikimedia.org/wiki/File:S
ort_sol_pdfnet2.jpg (Public Domain)

22. Step 1: Convert channels to nodes of a network
Step 2: Assign coordinates to each channel
22
(Vj
,Vb
)

23. Flocking of channels
1. A set of channels
moves away from each
other if used in a PBT
2. The set of mirrored
channels moves close
to each other
23

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Flocking of channels
Zone of Repulsion
Zone of Alignment
Zone of Attraction

25. Angle of the channel
Coordinate of each channel
is in positive numbers
Hence, the angle of each
channel is in the range
[0,90]
25

26. Coordinate updating
26
Angle1
= 180 degrees + mean angle of the channels in the
repulsion zone (opposite of channels in the repulsion zone)
Angle2
= Mean angle of the channels in the alignment zone
Xi
= Xi
+ 0.2(Cos(Angle1
) + Cos(Angle2
))
Yi
= Yi
+ 0.2(Sin(Angle1
) + Sin(Angle2
))

27. Routing protocol
27
● Updated coordinates are regularly announced
● Routing as per the shortest path

28. 28
Bitcoin Lightning Network
Data used = three subgraphs of BLN
# of
nodes
Average
degree
Average
channel
balance
(simu-
lated)
Average
PBT
value
(simu-
lated)
400,
600,
800
11 7 0.5

29. Network with 400
nodes
The standard deviation of
channels remain low for
balanced routing
29

30. Network with 600
nodes
The standard deviation of
channels remain low for
balanced routing
30

31. Network with 800
nodes
The standard deviation of
channels remain low for
balanced routing
31

32. The number of completed
PBTs for balanced routing
is higher than the same for
shortest path routing
Average path length for
PBTs is lower for balanced
routing
32

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Thank you:
any questions?
subhasis.thakur@nuigalway.ie
john.breslin@nuigalway.ie