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rgb-cln/plugins/renepay/mcf.c

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#include "config.h"
#include <assert.h>
#include <ccan/list/list.h>
#include <ccan/lqueue/lqueue.h>
#include <ccan/tal/str/str.h>
#include <ccan/tal/tal.h>
#include <common/pseudorand.h>
#include <common/type_to_string.h>
#include <common/utils.h>
#include <math.h>
#include <plugins/renepay/dijkstra.h>
#include <plugins/renepay/flow.h>
#include <plugins/renepay/mcf.h>
#include <stdint.h>
/* # Optimal payments
*
* In this module we reduce the routing optimization problem to a linear
* cost optimization problem and find a solution using MCF algorithms.
* The optimization of the routing itself doesn't need a precise numerical
* solution, since we can be happy near optimal results; e.g. paying 100 msat or
* 101 msat for fees doesn't make any difference if we wish to deliver 1M sats.
* On the other hand, we are now also considering Pickhard's
* [1] model to improve payment reliability,
* hence our optimization moves to a 2D space: either we like to maximize the
* probability of success of a payment or minimize the routing fees, or
* alternatively we construct a function of the two that gives a good compromise.
*
* Therefore from now own, the definition of optimal is a matter of choice.
* To simplify the API of this module, we think the best way to state the
* problem is:
*
* Find a routing solution that pays the least of fees while keeping
* the probability of success above a certain value `min_probability`.
*
*
* # Fee Cost
*
* Routing fees is non-linear function of the payment flow x, that's true even
* without the base fee:
*
* fee_msat = base_msat + floor(millionths*x_msat / 10^6)
*
* We approximate this fee into a linear function by computing a slope `c_fee` such
* that:
*
* fee_microsat = c_fee * x_sat
*
* Function `linear_fee_cost` computes `c_fee` based on the base and
* proportional fees of a channel.
* The final product if microsat because if only
* the proportional fee was considered we can have c_fee = millionths.
* Moving to costs based in msats means we have to either truncate payments
* below 1ksats or estimate as 0 cost for channels with less than 1000ppm.
*
* TODO(eduardo): shall we build a linear cost function in msats?
*
* # Probability cost
*
* The probability of success P of the payment is the product of the prob. of
* success of forwarding parts of the payment over all routing channels. This
* problem is separable if we log it, and since we would like to increase P,
* then we can seek to minimize -log(P), and that's our prob. cost function [1].
*
* - log P = sum_{i} - log P_i
*
* The probability of success `P_i` of sending some flow `x` on a channel with
* liquidity l in the range a<=l<b is
*
* P_{a,b}(x) = (b-x)/(b-a); for x > a
* = 1. ; for x <= a
*
* Notice that unlike the similar formula in [1], the one we propose does not
* contain the quantization shot noise for counting states. The formula remains
* valid independently of the liquidity units (sats or msats).
*
* The cost associated to probability P is then -k log P, where k is some
* constant. For k=1 we get the following table:
*
* prob | cost
* -----------
* 0.01 | 4.6
* 0.02 | 3.9
* 0.05 | 3.0
* 0.10 | 2.3
* 0.20 | 1.6
* 0.50 | 0.69
* 0.80 | 0.22
* 0.90 | 0.10
* 0.95 | 0.05
* 0.98 | 0.02
* 0.99 | 0.01
*
* Clearly -log P(x) is non-linear; we try to linearize it piecewise:
* split the channel into 4 arcs representing 4 liquidity regions:
*
* arc_0 -> [0, a)
* arc_1 -> [a, a+(b-a)*f1)
* arc_2 -> [a+(b-a)*f1, a+(b-a)*f2)
* arc_3 -> [a+(b-a)*f2, a+(b-a)*f3)
*
* where f1 = 0.5, f2 = 0.8, f3 = 0.95;
* We fill arc_0's capacity with complete certainty P=1, then if more flow is
* needed we start filling the capacity in arc_1 until the total probability
* of success reaches P=0.5, then arc_2 until P=1-0.8=0.2, and finally arc_3 until
* P=1-0.95=0.05. We don't go further than 5% prob. of success per channel.
* TODO(eduardo): this channel linearization is hard coded into
* `CHANNEL_PIVOTS`, maybe we can parametrize this to take values from the config file.
*
* With this choice, the slope of the linear cost function becomes:
*
* m_0 = 0
* m_1 = 1.38 k /(b-a)
* m_2 = 3.05 k /(b-a)
* m_3 = 9.24 k /(b-a)
*
* Notice that one of the assumptions in [2] for the MCF problem is that flows
* and the slope of the costs functions are integer numbers. The only way we
* have at hand to make it so, is to choose a universal value of `k` that scales
* up the slopes so that floor(m_i) is not zero for every arc.
*
* # Combine fee and prob. costs
*
* We attempt to solve the original problem of finding the solution that
* pays the least fees while keeping the prob. of success above a certain value,
* by constructing a cost function which is a linear combination of fee and
* prob. costs.
* TODO(eduardo): investigate how this procedure is justified,
* possibly with the use of Lagrange optimization theory.
*
* At first, prob. and fee costs live in different dimensions, they cannot be
* summed, it's like comparing apples and oranges.
* However we propose to scale the prob. cost by a global factor k that
* translates into the monetization of prob. cost.
*
* k/1000, for instance, becomes the equivalent monetary cost
* of increasing the probability of success by 0.1% for P~100%.
*
* The input parameter `prob_cost_factor` in the function `minflow` is defined
* as the PPM from the delivery amount `T` we are *willing to pay* to increase the
* prob. of success by 0.1%:
*
* k_microsat = floor(1000*prob_cost_factor * T_sat)
*
* Is this enough to make integer prob. cost per unit flow?
* For `prob_cost_factor=10`; i.e. we pay 10ppm for increasing the prob. by
* 0.1%, we get that
*
* -> any arc with (b-a) > 10000 T, will have zero prob. cost, which is
* reasonable because even if all the flow passes through that arc, we get
* a 1.3 T/(b-a) ~ 0.01% prob. of failure at most.
*
* -> if (b-a) ~ 10000 T, then the arc will have unit cost, or just that we
* pay 1 microsat for every sat we send through this arc.
*
* -> it would be desirable to have a high proportional fee when (b-a)~T,
* because prob. of failure start to become very high.
* In this case we get to pay 10000 microsats for every sat.
*
* Once `k` is fixed then we can combine the linear prob. and fee costs, both
* are in monetary units.
*
* Note: with costs in microsats, because slopes represent ppm and flows are in
* sats, then our integer bounds with 64 bits are such that we can move as many
* as 10'000 BTC without overflow:
*
* 10^6 (max ppm) * 10^8 (sats per BTC) * 10^4 = 10^18
*
* # References
*
* [1] Pickhardt and Richter, https://arxiv.org/abs/2107.05322
* [2] R.K. Ahuja, T.L. Magnanti, and J.B. Orlin. Network Flows:
* Theory, Algorithms, and Applications. Prentice Hall, 1993.
*
*
* TODO(eduardo) it would be interesting to see:
* how much do we pay for reliability?
* Cost_fee(most reliable solution) - Cost_fee(cheapest solution)
*
* TODO(eduardo): it would be interesting to see:
* how likely is the most reliable path with respect to the cheapest?
* Prob(reliable)/Prob(cheapest) = Exp(Cost_prob(cheapest)-Cost_prob(reliable))
*
* */
#define PARTS_BITS 2
#define CHANNEL_PARTS (1 << PARTS_BITS)
// These are the probability intervals we use to decompose a channel into linear
// cost function arcs.
static const double CHANNEL_PIVOTS[]={0,0.5,0.8,0.95};
static const s64 INFINITE = INT64_MAX;
static const u64 INFINITE_MSAT = UINT64_MAX;
static const u32 INVALID_INDEX = 0xffffffff;
static const s64 MU_MAX = 128;
/* Let's try this encoding of arcs:
* Each channel `c` has two possible directions identified by a bit
* `half` or `!half`, and each one of them has to be
* decomposed into 4 liquidity parts in order to
* linearize the cost function, but also to solve MCF
* problem we need to keep track of flows in the
* residual network hence we need for each directed arc
* in the network there must be another arc in the
* opposite direction refered to as it's dual. In total
* 1+2+1 additional bits of information:
*
* (chan_idx)(half)(part)(dual)
*
* That means, for each channel we need to store the
* information of 16 arcs. If we implement a convex-cost
* solver then we can reduce that number to size(half)size(dual)=4.
*
* In the adjacency of a `node` we are going to store
* the outgoing arcs. If we ever need to loop over the
* incoming arcs then we will define a reverse adjacency
* API.
* Then for each outgoing channel `(c,half)` there will
* be 4 parts for the actual residual capacity, hence
* with the dual bit set to 0:
*
* (c,half,0,0)
* (c,half,1,0)
* (c,half,2,0)
* (c,half,3,0)
*
* and also we need to consider the dual arcs
* corresponding to the channel direction `(c,!half)`
* (the dual has reverse direction):
*
* (c,!half,0,1)
* (c,!half,1,1)
* (c,!half,2,1)
* (c,!half,3,1)
*
* These are the 8 outgoing arcs relative to `node` and
* associated with channel `c`. The incoming arcs will
* be:
*
* (c,!half,0,0)
* (c,!half,1,0)
* (c,!half,2,0)
* (c,!half,3,0)
*
* (c,half,0,1)
* (c,half,1,1)
* (c,half,2,1)
* (c,half,3,1)
*
* but they will be stored as outgoing arcs on the peer
* node `next`.
*
* I hope this will clarify my future self when I forget.
*
* */
/*
* We want to use the whole number here for convenience, but
* we can't us a union, since bit order is implementation-defined and
* we want chanidx on the highest bits:
*
* [ 0 1 2 3 4 5 6 ... 31 ]
* dual part chandir chanidx
*/
struct arc {
u32 idx;
};
#define ARC_DUAL_BITOFF (0)
#define ARC_PART_BITOFF (1)
#define ARC_CHANDIR_BITOFF (1 + PARTS_BITS)
#define ARC_CHANIDX_BITOFF (1 + PARTS_BITS + 1)
#define ARC_CHANIDX_BITS (32 - ARC_CHANIDX_BITOFF)
/* How many arcs can we have for a single channel?
* linearization parts, both directions, and dual */
#define ARCS_PER_CHANNEL ((size_t)1 << (PARTS_BITS + 1 + 1))
static inline void arc_to_parts(struct arc arc,
u32 *chanidx,
int *chandir,
u32 *part,
bool *dual)
{
if (chanidx)
*chanidx = (arc.idx >> ARC_CHANIDX_BITOFF);
if (chandir)
*chandir = (arc.idx >> ARC_CHANDIR_BITOFF) & 1;
if (part)
*part = (arc.idx >> ARC_PART_BITOFF) & ((1 << PARTS_BITS)-1);
if (dual)
*dual = (arc.idx >> ARC_DUAL_BITOFF) & 1;
}
static inline struct arc arc_from_parts(u32 chanidx, int chandir, u32 part, bool dual)
{
struct arc arc;
assert(part < CHANNEL_PARTS);
assert(chandir == 0 || chandir == 1);
assert(chanidx < (1U << ARC_CHANIDX_BITS));
arc.idx = ((u32)dual << ARC_DUAL_BITOFF)
| (part << ARC_PART_BITOFF)
| ((u32)chandir << ARC_CHANDIR_BITOFF)
| (chanidx << ARC_CHANIDX_BITOFF);
return arc;
}
#define MAX(x, y) (((x) > (y)) ? (x) : (y))
#define MIN(x, y) (((x) < (y)) ? (x) : (y))
struct pay_parameters {
/* The gossmap we are using */
struct gossmap *gossmap;
const struct gossmap_node *source;
const struct gossmap_node *target;
/* Extra information we intuited about the channels */
struct chan_extra_map *chan_extra_map;
/* Optional bitarray of disabled channels. */
const bitmap *disabled;
// how much we pay
struct amount_msat amount;
// channel linearization parameters
double cap_fraction[CHANNEL_PARTS],
cost_fraction[CHANNEL_PARTS];
struct amount_msat max_fee;
double min_probability;
double delay_feefactor;
double base_fee_penalty;
u32 prob_cost_factor;
};
/* Representation of the linear MCF network.
* This contains the topology of the extended network (after linearization and
* addition of arc duality).
* This contains also the arc probability and linear fee cost, as well as
* capacity; these quantities remain constant during MCF execution. */
struct linear_network
{
u32 *arc_tail_node;
// notice that a tail node is not needed,
// because the tail of arc is the head of dual(arc)
struct arc *node_adjacency_next_arc;
struct arc *node_adjacency_first_arc;
// probability and fee cost associated to an arc
s64 *arc_prob_cost, *arc_fee_cost;
s64 *capacity;
size_t max_num_arcs,max_num_nodes;
};
/* This is the structure that keeps track of the network properties while we
* seek for a solution. */
struct residual_network {
/* residual capacity on arcs */
s64 *cap;
/* some combination of prob. cost and fee cost on arcs */
s64 *cost;
/* potential function on nodes */
s64 *potential;
};
/* Helper function.
* Given an arc idx, return the dual's idx in the residual network. */
static struct arc arc_dual(struct arc arc)
{
arc.idx ^= (1U << ARC_DUAL_BITOFF);
return arc;
}
/* Helper function. */
static bool arc_is_dual(struct arc arc)
{
bool dual;
arc_to_parts(arc, NULL, NULL, NULL, &dual);
return dual;
}
/* Helper function.
* Given an arc of the network (not residual) give me the flow. */
static s64 get_arc_flow(
const struct residual_network *network,
const struct arc arc)
{
assert(!arc_is_dual(arc));
assert(arc_dual(arc).idx < tal_count(network->cap));
return network->cap[ arc_dual(arc).idx ];
}
/* Helper function.
* Given an arc idx, return the node from which this arc emanates in the residual network. */
static u32 arc_tail(const struct linear_network *linear_network,
const struct arc arc)
{
assert(arc.idx < tal_count(linear_network->arc_tail_node));
return linear_network->arc_tail_node[ arc.idx ];
}
/* Helper function.
* Given an arc idx, return the node that this arc is pointing to in the residual network. */
static u32 arc_head(const struct linear_network *linear_network,
const struct arc arc)
{
const struct arc dual = arc_dual(arc);
assert(dual.idx < tal_count(linear_network->arc_tail_node));
return linear_network->arc_tail_node[dual.idx];
}
/* Helper function.
* Given node idx `node`, return the idx of the first arc whose tail is `node`.
* */
static struct arc node_adjacency_begin(
const struct linear_network * linear_network,
const u32 node)
{
assert(node < tal_count(linear_network->node_adjacency_first_arc));
return linear_network->node_adjacency_first_arc[node];
}
/* Helper function.
* Is this the end of the adjacency list. */
static bool node_adjacency_end(const struct arc arc)
{
return arc.idx == INVALID_INDEX;
}
/* Helper function.
* Given node idx `node` and `arc`, returns the idx of the next arc whose tail is `node`. */
static struct arc node_adjacency_next(
const struct linear_network *linear_network,
const struct arc arc)
{
assert(arc.idx < tal_count(linear_network->node_adjacency_next_arc));
return linear_network->node_adjacency_next_arc[arc.idx];
}
// TODO(eduardo): unit test this
/* Split a directed channel into parts with linear cost function. */
static bool linearize_channel(const struct pay_parameters *params,
const struct gossmap_chan *c, const int dir,
s64 *capacity, s64 *cost)
{
struct chan_extra_half *extra_half = get_chan_extra_half_by_chan(
params->gossmap,
params->chan_extra_map,
c,
dir);
if (!extra_half) {
return false;
}
assert(
amount_msat_less_eq(extra_half->htlc_total, extra_half->known_max));
assert(
amount_msat_less_eq(extra_half->known_min, extra_half->known_max));
s64 h = extra_half->htlc_total.millisatoshis/1000; /* Raw: linearize_channel */
s64 a = extra_half->known_min.millisatoshis/1000, /* Raw: linearize_channel */
b = 1 + extra_half->known_max.millisatoshis/1000; /* Raw: linearize_channel */
/* If HTLCs add up to more than the known_max it means we have a
* completely wrong knowledge. */
// assert(h<b);
/* HTLCs allocated could instead be greater than known_min, we enter in
* the uncertainty region. If h>a it doesn't mean automatically that our
* known_min should have been updated, because we reserve this HTLC
* after sendpay behind the scenes it might happen that sendpay failed
* because of insufficient funds we haven't noticed yet. */
// assert(h<=a);
/* We reduce this channel capacity because HTLC are reserving liquidity. */
a -= h;
b -= h;
a = MAX(a,0);
b = MAX(a+1,b);
capacity[0]=a;
cost[0]=0;
for(size_t i=1;i<CHANNEL_PARTS;++i)
{
capacity[i] = params->cap_fraction[i]*(b-a);
cost[i] = params->cost_fraction[i]
*params->amount.millisatoshis /* Raw: linearize_channel */
*params->prob_cost_factor*1.0/(b-a);
}
return true;
}
static struct residual_network *
alloc_residual_network(const tal_t *ctx, const size_t max_num_nodes,
const size_t max_num_arcs)
{
struct residual_network *residual_network =
tal(ctx, struct residual_network);
if (!residual_network)
goto function_fail;
residual_network->cap = tal_arrz(residual_network, s64, max_num_arcs);
residual_network->cost = tal_arrz(residual_network, s64, max_num_arcs);
residual_network->potential =
tal_arrz(residual_network, s64, max_num_nodes);
if (!residual_network->cap || !residual_network->cost ||
!residual_network->potential) {
goto function_fail;
}
return residual_network;
function_fail:
return tal_free(residual_network);
}
static void init_residual_network(
const struct linear_network * linear_network,
struct residual_network* residual_network)
{
const size_t max_num_arcs = linear_network->max_num_arcs;
const size_t max_num_nodes = linear_network->max_num_nodes;
for(struct arc arc = {0};arc.idx < max_num_arcs; ++arc.idx)
{
if(arc_is_dual(arc))
continue;
struct arc dual = arc_dual(arc);
residual_network->cap[arc.idx]=linear_network->capacity[arc.idx];
residual_network->cap[dual.idx]=0;
residual_network->cost[arc.idx]=residual_network->cost[dual.idx]=0;
}
for(u32 i=0;i<max_num_nodes;++i)
{
residual_network->potential[i]=0;
}
}
static void combine_cost_function(
const struct linear_network* linear_network,
struct residual_network *residual_network,
s64 mu)
{
for(struct arc arc = {0};arc.idx < linear_network->max_num_arcs; ++arc.idx)
{
if(arc_tail(linear_network,arc)==INVALID_INDEX)
continue;
const s64 pcost = linear_network->arc_prob_cost[arc.idx],
fcost = linear_network->arc_fee_cost[arc.idx];
const s64 combined = pcost==INFINITE || fcost==INFINITE ? INFINITE :
mu*fcost + (MU_MAX-1-mu)*pcost;
residual_network->cost[arc.idx]
= mu==0 ? pcost :
(mu==(MU_MAX-1) ? fcost : combined);
}
}
static void linear_network_add_adjacenct_arc(
struct linear_network *linear_network,
const u32 node_idx,
const struct arc arc)
{
assert(arc.idx < tal_count(linear_network->arc_tail_node));
linear_network->arc_tail_node[arc.idx] = node_idx;
assert(node_idx < tal_count(linear_network->node_adjacency_first_arc));
const struct arc first_arc = linear_network->node_adjacency_first_arc[node_idx];
assert(arc.idx < tal_count(linear_network->node_adjacency_next_arc));
linear_network->node_adjacency_next_arc[arc.idx]=first_arc;
assert(node_idx < tal_count(linear_network->node_adjacency_first_arc));
linear_network->node_adjacency_first_arc[node_idx]=arc;
}
/* Get the fee cost associated to this directed channel.
* Cost is expressed as PPM of the payment.
*
* Choose and integer `c_fee` to linearize the following fee function
*
* fee_msat = base_msat + floor(millionths*x_msat / 10^6)
*
* into
*
* fee_microsat = c_fee * x_sat
*
* use `base_fee_penalty` to weight the base fee and `delay_feefactor` to
* weight the CLTV delay.
* */
static s64 linear_fee_cost(
const struct gossmap_chan *c,
const int dir,
double base_fee_penalty,
double delay_feefactor)
{
assert(c);
assert(dir==0 || dir==1);
s64 pfee = c->half[dir].proportional_fee,
bfee = c->half[dir].base_fee,
delay = c->half[dir].delay;
return pfee + bfee* base_fee_penalty+ delay*delay_feefactor;
}
static struct linear_network *
init_linear_network(const tal_t *ctx, const struct pay_parameters *params,
char **fail)
{
tal_t *this_ctx = tal(ctx,tal_t);
struct linear_network * linear_network = tal(ctx, struct linear_network);
if (!linear_network) {
if (fail)
*fail = tal_fmt(ctx, "bad allocation of linear_network");
goto function_fail;
}
const size_t max_num_chans = gossmap_max_chan_idx(params->gossmap);
const size_t max_num_arcs = max_num_chans * ARCS_PER_CHANNEL;
const size_t max_num_nodes = gossmap_max_node_idx(params->gossmap);
linear_network->max_num_arcs = max_num_arcs;
linear_network->max_num_nodes = max_num_nodes;
linear_network->arc_tail_node = tal_arr(linear_network,u32,max_num_arcs);
if(!linear_network->arc_tail_node)
{
if (fail)
*fail = tal_fmt(ctx, "bad allocation of arc_tail_node");
goto function_fail;
}
for(size_t i=0;i<tal_count(linear_network->arc_tail_node);++i)
linear_network->arc_tail_node[i]=INVALID_INDEX;
linear_network->node_adjacency_next_arc = tal_arr(linear_network,struct arc,max_num_arcs);
if(!linear_network->node_adjacency_next_arc)
{
if (fail)
*fail = tal_fmt(ctx, "bad allocation of node_adjacency_next_arc");
goto function_fail;
}
for(size_t i=0;i<tal_count(linear_network->node_adjacency_next_arc);++i)
linear_network->node_adjacency_next_arc[i].idx=INVALID_INDEX;
linear_network->node_adjacency_first_arc = tal_arr(linear_network,struct arc,max_num_nodes);
if(!linear_network->node_adjacency_first_arc)
{
if (fail)
*fail = tal_fmt(ctx, "bad allocation of node_adjacency_first_arc");
goto function_fail;
}
for(size_t i=0;i<tal_count(linear_network->node_adjacency_first_arc);++i)
linear_network->node_adjacency_first_arc[i].idx=INVALID_INDEX;
linear_network->arc_prob_cost = tal_arr(linear_network,s64,max_num_arcs);
if(!linear_network->arc_prob_cost)
{
if (fail)
*fail = tal_fmt(ctx, "bad allocation of arc_prob_cost");
goto function_fail;
}
for(size_t i=0;i<tal_count(linear_network->arc_prob_cost);++i)
linear_network->arc_prob_cost[i]=INFINITE;
linear_network->arc_fee_cost = tal_arr(linear_network,s64,max_num_arcs);
if(!linear_network->arc_fee_cost)
{
if (fail)
*fail = tal_fmt(ctx, "bad allocation of arc_fee_cost");
goto function_fail;
}
for(size_t i=0;i<tal_count(linear_network->arc_fee_cost);++i)
linear_network->arc_fee_cost[i]=INFINITE;
linear_network->capacity = tal_arrz(linear_network,s64,max_num_arcs);
if(!linear_network->capacity)
{
if (fail)
*fail = tal_fmt(ctx, "bad allocation of capacity");
goto function_fail;
}
for(struct gossmap_node *node = gossmap_first_node(params->gossmap);
node;
node=gossmap_next_node(params->gossmap,node))
{
const u32 node_id = gossmap_node_idx(params->gossmap,node);
for(size_t j=0;j<node->num_chans;++j)
{
int half;
const struct gossmap_chan *c = gossmap_nth_chan(params->gossmap,
node, j, &half);
if (!gossmap_chan_set(c,half))
continue;
const u32 chan_id = gossmap_chan_idx(params->gossmap, c);
if (params->disabled && bitmap_test_bit(params->disabled,chan_id))
continue;
const struct gossmap_node *next = gossmap_nth_node(params->gossmap,
c,!half);
const u32 next_id = gossmap_node_idx(params->gossmap,next);
if(node_id==next_id)
continue;
// `cost` is the word normally used to denote cost per
// unit of flow in the context of MCF.
s64 prob_cost[CHANNEL_PARTS], capacity[CHANNEL_PARTS];
// split this channel direction to obtain the arcs
// that are outgoing to `node`
if (!linearize_channel(params, c, half,
capacity, prob_cost)) {
if(fail)
*fail =
tal_fmt(ctx, "linearize_channel failed");
goto function_fail;
}
const s64 fee_cost = linear_fee_cost(c,half,
params->base_fee_penalty,
params->delay_feefactor);
// let's subscribe the 4 parts of the channel direction
// (c,half), the dual of these guys will be subscribed
// when the `i` hits the `next` node.
for(size_t k=0;k<CHANNEL_PARTS;++k)
{
// if(capacity[k]==0)continue;
struct arc arc = arc_from_parts(chan_id, half, k, false);
linear_network_add_adjacenct_arc(linear_network,node_id,arc);
linear_network->capacity[arc.idx] = capacity[k];
linear_network->arc_prob_cost[arc.idx] = prob_cost[k];
linear_network->arc_fee_cost[arc.idx] = fee_cost;
// + the respective dual
struct arc dual = arc_dual(arc);
linear_network_add_adjacenct_arc(linear_network,next_id,dual);
linear_network->capacity[dual.idx] = 0;
linear_network->arc_prob_cost[dual.idx] = -prob_cost[k];
linear_network->arc_fee_cost[dual.idx] = -fee_cost;
}
}
}
tal_free(this_ctx);
return linear_network;
function_fail:
tal_free(this_ctx);
return tal_free(linear_network);
}
/* Simple queue to traverse the network. */
struct queue_data
{
u32 idx;
struct lqueue_link ql;
};
// TODO(eduardo): unit test this
/* Finds an admissible path from source to target, traversing arcs in the
* residual network with capacity greater than 0.
* The path is encoded into prev, which contains the idx of the arcs that are
* traversed. */
static bool
find_admissible_path(const tal_t *ctx,
const struct linear_network *linear_network,
const struct residual_network *residual_network,
const u32 source, const u32 target, struct arc *prev)
{
tal_t *this_ctx = tal(ctx,tal_t);
bool target_found = false;
for(size_t i=0;i<tal_count(prev);++i)
prev[i].idx=INVALID_INDEX;
// The graph is dense, and the farthest node is just a few hops away,
// hence let's BFS search.
LQUEUE(struct queue_data,ql) myqueue = LQUEUE_INIT;
struct queue_data *qdata;
qdata = tal(this_ctx,struct queue_data);
qdata->idx = source;
lqueue_enqueue(&myqueue,qdata);
while(!lqueue_empty(&myqueue))
{
qdata = lqueue_dequeue(&myqueue);
u32 cur = qdata->idx;
tal_free(qdata);
if(cur==target)
{
target_found = true;
break;
}
for(struct arc arc = node_adjacency_begin(linear_network,cur);
!node_adjacency_end(arc);
arc = node_adjacency_next(linear_network,arc))
{
// check if this arc is traversable
if(residual_network->cap[arc.idx] <= 0)
continue;
u32 next = arc_head(linear_network,arc);
assert(next < tal_count(prev));
// if that node has been seen previously
if(prev[next].idx!=INVALID_INDEX)
continue;
prev[next] = arc;
qdata = tal(this_ctx,struct queue_data);
qdata->idx = next;
lqueue_enqueue(&myqueue,qdata);
}
}
tal_free(this_ctx);
return target_found;
}
/* Get the max amount of flow one can send from source to target along the path
* encoded in `prev`. */
static s64 get_augmenting_flow(
const struct linear_network* linear_network,
const struct residual_network *residual_network,
const u32 source,
const u32 target,
const struct arc *prev)
{
s64 flow = INFINITE;
u32 cur = target;
while(cur!=source)
{
assert(cur<tal_count(prev));
const struct arc arc = prev[cur];
flow = MIN(flow , residual_network->cap[arc.idx]);
// we are traversing in the opposite direction to the flow,
// hence the next node is at the tail of the arc.
cur = arc_tail(linear_network,arc);
}
assert(flow<INFINITE && flow>0);
return flow;
}
/* Augment a `flow` amount along the path defined by `prev`.*/
static void augment_flow(
const struct linear_network *linear_network,
struct residual_network *residual_network,
const u32 source,
const u32 target,
const struct arc *prev,
s64 flow)
{
u32 cur = target;
while(cur!=source)
{
assert(cur < tal_count(prev));
const struct arc arc = prev[cur];
const struct arc dual = arc_dual(arc);
assert(arc.idx < tal_count(residual_network->cap));
assert(dual.idx < tal_count(residual_network->cap));
residual_network->cap[arc.idx] -= flow;
residual_network->cap[dual.idx] += flow;
assert(residual_network->cap[arc.idx] >=0 );
// we are traversing in the opposite direction to the flow,
// hence the next node is at the tail of the arc.
cur = arc_tail(linear_network,arc);
}
}
// TODO(eduardo): unit test this
/* Finds any flow that satisfy the capacity and balance constraints of the
* uncertainty network. For the balance function condition we have:
* balance(source) = - balance(target) = amount
* balance(node) = 0 , for every other node
* Returns an error code if no feasible flow is found.
*
* 13/04/2023 This implementation uses a simple augmenting path approach.
* */
static bool find_feasible_flow(const tal_t *ctx,
const struct linear_network *linear_network,
struct residual_network *residual_network,
const u32 source, const u32 target, s64 amount,
char **fail)
{
assert(amount>=0);
tal_t *this_ctx = tal(ctx,tal_t);
/* path information
* prev: is the id of the arc that lead to the node. */
struct arc *prev = tal_arr(this_ctx,struct arc,linear_network->max_num_nodes);
if(!prev)
{
if(fail)
*fail = tal_fmt(ctx, "bad allocation of prev");
goto function_fail;
}
while(amount>0)
{
// find a path from source to target
if (!find_admissible_path(this_ctx, linear_network,
residual_network, source, target,
prev))
{
if(fail)
*fail = tal_fmt(ctx, "find_admissible_path failed");
goto function_fail;
}
// traverse the path and see how much flow we can send
s64 delta = get_augmenting_flow(linear_network,
residual_network,
source,target,prev);
// commit that flow to the path
delta = MIN(amount,delta);
assert(delta>0 && delta<=amount);
augment_flow(linear_network,residual_network,source,target,prev,delta);
amount -= delta;
}
tal_free(this_ctx);
return true;
function_fail:
tal_free(this_ctx);
return false;
}
// TODO(eduardo): unit test this
/* Similar to `find_admissible_path` but use Dijkstra to optimize the distance
* label. Stops when the target is hit. */
static bool find_optimal_path(const tal_t *ctx, struct dijkstra *dijkstra,
const struct linear_network *linear_network,
const struct residual_network *residual_network,
const u32 source, const u32 target,
struct arc *prev, char **fail)
{
tal_t *this_ctx = tal(ctx,tal_t);
bool target_found = false;
bitmap *visited = tal_arrz(this_ctx, bitmap,
BITMAP_NWORDS(linear_network->max_num_nodes));
if(!visited)
{
if(fail)
*fail = tal_fmt(ctx, "bad allocation of visited");
goto finish;
}
for(size_t i=0;i<tal_count(prev);++i)
prev[i].idx=INVALID_INDEX;
const s64 *const distance=dijkstra_distance_data(dijkstra);
dijkstra_init(dijkstra);
dijkstra_update(dijkstra,source,0);
while(!dijkstra_empty(dijkstra))
{
u32 cur = dijkstra_top(dijkstra);
dijkstra_pop(dijkstra);
if(bitmap_test_bit(visited,cur))
continue;
bitmap_set_bit(visited,cur);
if(cur==target)
{
target_found = true;
break;
}
for(struct arc arc = node_adjacency_begin(linear_network,cur);
!node_adjacency_end(arc);
arc = node_adjacency_next(linear_network,arc))
{
// check if this arc is traversable
if(residual_network->cap[arc.idx] <= 0)
continue;
u32 next = arc_head(linear_network,arc);
s64 cij = residual_network->cost[arc.idx]
- residual_network->potential[cur]
+ residual_network->potential[next];
// Dijkstra only works with non-negative weights
assert(cij>=0);
if(distance[next]<=distance[cur]+cij)
continue;
dijkstra_update(dijkstra,next,distance[cur]+cij);
prev[next]=arc;
}
}
if (!target_found && fail)
*fail = tal_fmt(ctx, "no route to destination");
finish:
tal_free(this_ctx);
return target_found;
}
/* Set zero flow in the residual network. */
static void zero_flow(
const struct linear_network *linear_network,
struct residual_network *residual_network)
{
for(u32 node=0;node<linear_network->max_num_nodes;++node)
{
residual_network->potential[node]=0;
for(struct arc arc=node_adjacency_begin(linear_network,node);
!node_adjacency_end(arc);
arc = node_adjacency_next(linear_network,arc))
{
if(arc_is_dual(arc))continue;
struct arc dual = arc_dual(arc);
residual_network->cap[arc.idx] = linear_network->capacity[arc.idx];
residual_network->cap[dual.idx] = 0;
}
}
}
// TODO(eduardo): unit test this
/* Starting from a feasible flow (satisfies the balance and capacity
* constraints), find a solution that minimizes the network->cost function.
*
* TODO(eduardo) The MCF must be called several times until we get a good
* compromise between fees and probabilities. Instead of re-computing the MCF at
* each step, we might use the previous flow result, which is not optimal in the
* current iteration but I might be not too far from the truth.
* It comes to mind to use cycle cancelling. */
static bool optimize_mcf(const tal_t *ctx, struct dijkstra *dijkstra,
const struct linear_network *linear_network,
struct residual_network *residual_network,
const u32 source, const u32 target, const s64 amount,
char **fail)
{
assert(amount>=0);
tal_t *this_ctx = tal(ctx,tal_t);
char *errmsg;
zero_flow(linear_network,residual_network);
struct arc *prev = tal_arr(this_ctx,struct arc,linear_network->max_num_nodes);
const s64 *const distance = dijkstra_distance_data(dijkstra);
s64 remaining_amount = amount;
while(remaining_amount>0)
{
if (!find_optimal_path(this_ctx, dijkstra, linear_network,
residual_network, source, target, prev,
&errmsg)) {
if (fail)
*fail =
tal_fmt(ctx, "find_optimal_path failed: %s",
errmsg);
goto function_fail;
}
// traverse the path and see how much flow we can send
s64 delta = get_augmenting_flow(linear_network,residual_network,source,target,prev);
// commit that flow to the path
delta = MIN(remaining_amount,delta);
assert(delta>0 && delta<=remaining_amount);
augment_flow(linear_network,residual_network,source,target,prev,delta);
remaining_amount -= delta;
// update potentials
for(u32 n=0;n<linear_network->max_num_nodes;++n)
{
// see page 323 of Ahuja-Magnanti-Orlin
residual_network->potential[n] -= MIN(distance[target],distance[n]);
/* Notice:
* if node i is permanently labeled we have
* d_i<=d_t
* which implies
* MIN(d_i,d_t) = d_i
* if node i is temporarily labeled we have
* d_i>=d_t
* which implies
* MIN(d_i,d_t) = d_t
* */
}
}
tal_free(this_ctx);
return true;
function_fail:
tal_free(this_ctx);
return false;
}
// flow on directed channels
struct chan_flow
{
s64 half[2];
};
/* Search in the network a path of positive flow until we reach a node with
* positive balance. */
static u32 find_positive_balance(
const struct gossmap *gossmap,
const struct chan_flow *chan_flow,
const u32 start_idx,
const s64 *balance,
const struct gossmap_chan **prev_chan,
int *prev_dir,
u32 *prev_idx)
{
u32 final_idx = start_idx;
/* TODO(eduardo)
* This is guaranteed to halt if there are no directed flow cycles.
* There souldn't be any. In fact if cost is strickly
* positive, then flow cycles do not exist at all in the
* MCF solution. But if cost is allowed to be zero for
* some arcs, then we might have flow cyles in the final
* solution. We must somehow ensure that the MCF
* algorithm does not come up with spurious flow cycles. */
while(balance[final_idx]<=0)
{
// printf("%s: node = %d\n",__PRETTY_FUNCTION__,final_idx);
u32 updated_idx=INVALID_INDEX;
struct gossmap_node *cur
= gossmap_node_byidx(gossmap,final_idx);
for(size_t i=0;i<cur->num_chans;++i)
{
int dir;
const struct gossmap_chan *c
= gossmap_nth_chan(gossmap,
cur,i,&dir);
if (!gossmap_chan_set(c,dir))
continue;
const u32 c_idx = gossmap_chan_idx(gossmap,c);
// follow the flow
if(chan_flow[c_idx].half[dir]>0)
{
const struct gossmap_node *next
= gossmap_nth_node(gossmap,c,!dir);
u32 next_idx = gossmap_node_idx(gossmap,next);
prev_dir[next_idx] = dir;
prev_chan[next_idx] = c;
prev_idx[next_idx] = final_idx;
updated_idx = next_idx;
break;
}
}
assert(updated_idx!=INVALID_INDEX);
assert(updated_idx!=final_idx);
final_idx = updated_idx;
}
return final_idx;
}
struct list_data
{
struct list_node list;
struct flow *flow_path;
};
static inline uint64_t pseudorand_interval(uint64_t a, uint64_t b)
{
assert(b > a);
return a + pseudorand(b - a);
}
/* Given a flow in the residual network, build a set of payment flows in the
* gossmap that corresponds to this flow. */
static struct flow **
get_flow_paths(const tal_t *ctx, const struct gossmap *gossmap,
// chan_extra_map cannot be const because we use it to keep
// track of htlcs and in_flight sats.
struct chan_extra_map *chan_extra_map,
const struct linear_network *linear_network,
const struct residual_network *residual_network,
// how many msats in excess we paid for not having msat accuracy
// in the MCF solver
struct amount_msat excess,
// error message
char **fail)
{
tal_t *this_ctx = tal(ctx,tal_t);
char *errmsg;
struct flow **flows = tal_arr(ctx,struct flow*,0);
assert(amount_msat_less(excess, AMOUNT_MSAT(1000)));
const size_t max_num_chans = gossmap_max_chan_idx(gossmap);
struct chan_flow *chan_flow = tal_arrz(this_ctx,struct chan_flow,max_num_chans);
const size_t max_num_nodes = gossmap_max_node_idx(gossmap);
s64 *balance = tal_arrz(this_ctx,s64,max_num_nodes);
const struct gossmap_chan **prev_chan
= tal_arr(this_ctx,const struct gossmap_chan *,max_num_nodes);
int *prev_dir = tal_arr(this_ctx,int,max_num_nodes);
u32 *prev_idx = tal_arr(this_ctx,u32,max_num_nodes);
if (!chan_flow || !balance || !prev_chan || !prev_idx || !prev_dir) {
if (fail)
*fail = tal_fmt(ctx, "bad allocation");
goto function_fail;
}
// Convert the arc based residual network flow into a flow in the
// directed channel network.
// Compute balance on the nodes.
for(u32 n = 0;n<max_num_nodes;++n)
{
for(struct arc arc = node_adjacency_begin(linear_network,n);
!node_adjacency_end(arc);
arc = node_adjacency_next(linear_network,arc))
{
if(arc_is_dual(arc))
continue;
u32 m = arc_head(linear_network,arc);
s64 flow = get_arc_flow(residual_network,arc);
u32 chanidx;
int chandir;
balance[n] -= flow;
balance[m] += flow;
arc_to_parts(arc, &chanidx, &chandir, NULL, NULL);
chan_flow[chanidx].half[chandir] +=flow;
}
}
// Select all nodes with negative balance and find a flow that reaches a
// positive balance node.
for(u32 node_idx=0;node_idx<max_num_nodes;++node_idx)
{
// for(size_t i=0;i<tal_count(prev_idx);++i)
// {
// prev_idx[i]=INVALID_INDEX;
// }
// this node has negative balance, flows leaves from here
while(balance[node_idx]<0)
{
prev_chan[node_idx]=NULL;
u32 final_idx = find_positive_balance(gossmap,chan_flow,node_idx,balance,
prev_chan,prev_dir,prev_idx);
/* For each route we will compute the highest htlc_min
* and the smallest htlc_max and use those to constraint
* the flow we will allocate. */
struct amount_msat sup_htlc_min = AMOUNT_MSAT_INIT(0),
inf_htlc_max =
AMOUNT_MSAT_INIT(INFINITE_MSAT);
s64 delta=-balance[node_idx];
int length = 0;
delta = MIN(delta,balance[final_idx]);
// walk backwards, get me the length and the max flow we
// can send.
for(u32 cur_idx = final_idx;
cur_idx!=node_idx;
cur_idx=prev_idx[cur_idx])
{
assert(cur_idx!=INVALID_INDEX);
const int dir = prev_dir[cur_idx];
const struct gossmap_chan *const c = prev_chan[cur_idx];
const u32 c_idx = gossmap_chan_idx(gossmap,c);
delta=MIN(delta,chan_flow[c_idx].half[dir]);
length++;
/* obtain the supremum htlc_min along the route
*/
sup_htlc_min = amount_msat_max(
sup_htlc_min, channel_htlc_min(c, dir));
/* obtain the infimum htlc_max along the route
*/
inf_htlc_max = amount_msat_min(
inf_htlc_max, channel_htlc_max(c, dir));
}
s64 htlc_max=inf_htlc_max.millisatoshis/1000;/* Raw: need htlc_max in sats to do arithmetic operations.*/
s64 htlc_min=(sup_htlc_min.millisatoshis+999)/1000;/* Raw: need htlc_min in sats to do arithmetic operations.*/
if (htlc_min > htlc_max) {
/* htlc_min is too big or htlc_max is too small,
* we cannot send `delta` along this route.
*
* FIXME: We try anyways because failing
* channels will be blacklisted downstream. */
htlc_min = 0;
}
/* If we divide this route into different flows make it
* random to avoid routing nodes making correlations. */
if (delta > htlc_max) {
// FIXME: choosing a number in the range
// [htlc_min, htlc_max] or
// [0.5 htlc_max, htlc_max]
// The choice of the fraction was completely
// arbitrary.
delta = pseudorand_interval(
MAX(htlc_min, (htlc_max * 50) / 100),
htlc_max);
}
struct flow *fp = tal(this_ctx,struct flow);
fp->path = tal_arr(fp,const struct gossmap_chan *,length);
fp->dirs = tal_arr(fp,int,length);
balance[node_idx] += delta;
balance[final_idx]-= delta;
// walk backwards, substract flow
for(u32 cur_idx = final_idx;
cur_idx!=node_idx;
cur_idx=prev_idx[cur_idx])
{
assert(cur_idx!=INVALID_INDEX);
const int dir = prev_dir[cur_idx];
const struct gossmap_chan *const c = prev_chan[cur_idx];
const u32 c_idx = gossmap_chan_idx(gossmap,c);
length--;
fp->path[length]=c;
fp->dirs[length]=dir;
// notice: fp->path and fp->dirs have the path
// in the correct order.
chan_flow[c_idx].half[prev_dir[cur_idx]]-=delta;
}
assert(delta>0);
// substract the excess of msats for not having msat
// accuracy
struct amount_msat delivered = amount_msat(delta*1000);
if (!amount_msat_sub(&delivered, delivered, excess)) {
if (fail)
*fail = tal_fmt(
ctx, "unable to substract excess");
goto function_fail;
}
excess = amount_msat(0);
// complete the flow path by adding real fees and
// probabilities.
if (!flow_complete(this_ctx, fp, gossmap,
chan_extra_map, delivered,
&errmsg)) {
if (fail)
*fail = tal_fmt(
ctx, "flow_complete failed: %s",
errmsg);
goto function_fail;
}
// add fp to flows
tal_arr_expand(&flows, fp);
}
}
/* Establish ownership. */
for(size_t i=0;i<tal_count(flows);++i)
{
flows[i] = tal_steal(flows,flows[i]);
assert(flows[i]);
}
tal_free(this_ctx);
return flows;
function_fail:
tal_free(this_ctx);
return tal_free(flows);
}
/* Given the constraints on max fee and min prob.,
* is the flow A better than B? */
static bool is_better(
struct amount_msat max_fee,
double min_probability,
struct amount_msat A_fee,
double A_prob,
struct amount_msat B_fee,
double B_prob)
{
bool A_fee_pass = amount_msat_less_eq(A_fee,max_fee);
bool B_fee_pass = amount_msat_less_eq(B_fee,max_fee);
bool A_prob_pass = A_prob >= min_probability;
bool B_prob_pass = B_prob >= min_probability;
// all bounds are met
if(A_fee_pass && B_fee_pass && A_prob_pass && B_prob_pass)
{
// prefer lower fees
goto fees_or_prob;
}
// prefer the solution that satisfies both bounds
if(!(A_fee_pass && A_prob_pass) && (B_fee_pass && B_prob_pass))
{
return false;
}
// prefer the solution that satisfies both bounds
if((A_fee_pass && A_prob_pass) && !(B_fee_pass && B_prob_pass))
{
return true;
}
// no solution satisfies both bounds
// bound on fee is met
if(A_fee_pass && B_fee_pass)
{
// pick the highest prob.
return A_prob > B_prob;
}
// bound on prob. is met
if(A_prob_pass && B_prob_pass)
{
goto fees_or_prob;
}
// prefer the solution that satisfies the bound on fees
if(A_fee_pass && !B_fee_pass)
{
return true;
}
if(B_fee_pass && !A_fee_pass)
{
return false;
}
// none of them satisfy the fee bound
// prefer the solution that satisfies the bound on prob.
if(A_prob_pass && !B_prob_pass)
{
return true;
}
if(B_prob_pass && !A_prob_pass)
{
return true;
}
// no bound whatsoever is satisfied
fees_or_prob:
// fees are the same, wins the highest prob.
if(amount_msat_eq(A_fee,B_fee))
{
return A_prob > B_prob;
}
// go for fees
return amount_msat_less_eq(A_fee,B_fee);
}
// TODO(eduardo): choose some default values for the minflow parameters
/* eduardo: I think it should be clear that this module deals with linear
* flows, ie. base fees are not considered. Hence a flow along a path is
* described with a sequence of directed channels and one amount.
* In the `pay_flow` module there are dedicated routes to compute the actual
* amount to be forward on each hop.
*
* TODO(eduardo): notice that we don't pay fees to forward payments with local
* channels and we can tell with absolute certainty the liquidity on them.
* Check that local channels have fee costs = 0 and bounds with certainty (min=max). */
// TODO(eduardo): we should LOG_DBG the process of finding the MCF while
// adjusting the frugality factor.
struct flow **minflow(const tal_t *ctx, struct gossmap *gossmap,
const struct gossmap_node *source,
const struct gossmap_node *target,
struct chan_extra_map *chan_extra_map,
const bitmap *disabled, struct amount_msat amount,
struct amount_msat max_fee, double min_probability,
double delay_feefactor, double base_fee_penalty,
u32 prob_cost_factor, char **fail)
{
tal_t *this_ctx = tal(ctx,tal_t);
char *errmsg;
struct flow **best_flow_paths = NULL;
struct pay_parameters *params = tal(this_ctx,struct pay_parameters);
struct dijkstra *dijkstra;
params->gossmap = gossmap;
params->source = source;
params->target = target;
params->chan_extra_map = chan_extra_map;
params->disabled = disabled;
assert(!disabled
|| tal_bytelen(disabled) == bitmap_sizeof(gossmap_max_chan_idx(gossmap)));
params->amount = amount;
// template the channel partition into linear arcs
params->cap_fraction[0]=0;
params->cost_fraction[0]=0;
for(size_t i =1;i<CHANNEL_PARTS;++i)
{
params->cap_fraction[i]=CHANNEL_PIVOTS[i]-CHANNEL_PIVOTS[i-1];
params->cost_fraction[i]=
log((1-CHANNEL_PIVOTS[i-1])/(1-CHANNEL_PIVOTS[i]))
/params->cap_fraction[i];
// printf("channel part: %ld, fraction: %lf, cost_fraction: %lf\n",
// i,params->cap_fraction[i],params->cost_fraction[i]);
}
params->max_fee = max_fee;
params->min_probability = min_probability;
params->delay_feefactor = delay_feefactor;
params->base_fee_penalty = base_fee_penalty;
params->prob_cost_factor = prob_cost_factor;
// build the uncertainty network with linearization and residual arcs
struct linear_network *linear_network= init_linear_network(this_ctx, params, &errmsg);
if (!linear_network) {
if(fail)
*fail = tal_fmt(ctx, "init_linear_network failed: %s",
errmsg);
goto function_fail;
}
struct residual_network *residual_network =
alloc_residual_network(this_ctx, linear_network->max_num_nodes,
linear_network->max_num_arcs);
if (!residual_network) {
if (fail)
*fail = tal_fmt(
ctx, "failed to allocate the residual network");
goto function_fail;
}
dijkstra = dijkstra_new(this_ctx, gossmap_max_node_idx(params->gossmap));
const u32 target_idx = gossmap_node_idx(params->gossmap,target);
const u32 source_idx = gossmap_node_idx(params->gossmap,source);
init_residual_network(linear_network,residual_network);
struct amount_msat best_fee;
double best_prob_success;
/* TODO(eduardo):
* Some MCF algorithms' performance depend on the size of maxflow. If we
* were to work in units of msats we 1. risking overflow when computing
* costs and 2. we risk a performance overhead for no good reason.
*
* Working in units of sats was my first choice, but maybe working in
* units of 10, or 100 sats could be even better.
*
* IDEA: define the size of our precision as some parameter got at
* runtime that depends on the size of the payment and adjust the MCF
* accordingly.
* For example if we are trying to pay 1M sats our precision could be
* set to 1000sat, then channels that had capacity for 3M sats become 3k
* flow units. */
const u64 pay_amount_msats = params->amount.millisatoshis % 1000; /* Raw: minflow */
const u64 pay_amount_sats = params->amount.millisatoshis/1000 /* Raw: minflow */
+ (pay_amount_msats ? 1 : 0);
const struct amount_msat excess
= amount_msat(pay_amount_msats ? 1000 - pay_amount_msats : 0);
if (!find_feasible_flow(this_ctx, linear_network, residual_network,
source_idx, target_idx, pay_amount_sats,
&errmsg)) {
// there is no flow that satisfy the constraints, we stop here
if(fail)
*fail = tal_fmt(ctx, "failed to find a feasible flow: %s", errmsg);
goto function_fail;
}
// first flow found
best_flow_paths =
get_flow_paths(this_ctx, params->gossmap, params->chan_extra_map,
linear_network, residual_network, excess, &errmsg);
if (!best_flow_paths) {
if (fail)
*fail =
tal_fmt(ctx, "get_flow_paths failed: %s", errmsg);
goto function_fail;
}
best_flow_paths = tal_steal(ctx, best_flow_paths);
best_prob_success =
flowset_probability(this_ctx, best_flow_paths, params->gossmap,
params->chan_extra_map, &errmsg);
if (best_prob_success < 0) {
if (fail)
*fail =
tal_fmt(ctx, "flowset_probability failed: %s", errmsg);
goto function_fail;
}
if (!flowset_fee(&best_fee, best_flow_paths)) {
if (fail)
*fail =
tal_fmt(ctx, "flowset_fee failed on MaxFlow phase");
goto function_fail;
}
// binary search for a value of `mu` that fits our fee and prob.
// constraints.
// mu=0 corresponds to only probabilities
// mu=MU_MAX-1 corresponds to only fee
s64 mu_left = 0, mu_right = MU_MAX;
while(mu_left<mu_right)
{
s64 mu = (mu_left + mu_right)/2;
combine_cost_function(linear_network,residual_network,mu);
/* We solve a linear MCF problem. */
if(!optimize_mcf(this_ctx, dijkstra,linear_network,residual_network,
source_idx,target_idx,pay_amount_sats, &errmsg))
{
// optimize_mcf doesn't fail unless there is a bug.
if (fail)
*fail =
tal_fmt(ctx, "optimize_mcf failed: %s", errmsg);
goto function_fail;
}
struct flow **flow_paths;
/* We dissect the solution of the MCF into payment routes.
* Actual amounts considering fees are computed for every
* channel in the routes. */
flow_paths = get_flow_paths(
this_ctx, params->gossmap, params->chan_extra_map,
linear_network, residual_network, excess, &errmsg);
if(!flow_paths)
{
// get_flow_paths doesn't fail unless there is a bug.
if (fail)
*fail =
tal_fmt(ctx, "get_flow_paths failed: %s", errmsg);
goto function_fail;
}
double prob_success =
flowset_probability(this_ctx, flow_paths, params->gossmap,
params->chan_extra_map, &errmsg);
if (prob_success < 0) {
// flowset_probability doesn't fail unless there is a bug.
if (fail)
*fail =
tal_fmt(ctx, "flowset_probability: %s", errmsg);
goto function_fail;
}
struct amount_msat fee;
if (!flowset_fee(&fee, flow_paths)) {
// flowset_fee doesn't fail unless there is a bug.
if (fail)
*fail =
tal_fmt(ctx, "flowset_fee failed evaluating MinCostFlow candidate");
goto function_fail;
}
/* Is this better than the previous one? */
if(!best_flow_paths ||
is_better(params->max_fee,params->min_probability,
fee,prob_success,
best_fee, best_prob_success))
{
best_flow_paths = tal_free(best_flow_paths);
best_flow_paths = tal_steal(ctx,flow_paths);
best_fee = fee;
best_prob_success=prob_success;
flow_paths = NULL;
}
/* I don't like this candidate. */
else
tal_free(flow_paths);
if(amount_msat_greater(fee,params->max_fee))
{
// too expensive
mu_left = mu+1;
}else if(prob_success < params->min_probability)
{
// too unlikely
mu_right = mu;
}else
{
// with mu constraints are satisfied, now let's optimize
// the fees
mu_left = mu+1;
}
}
tal_free(this_ctx);
return best_flow_paths;
function_fail:
tal_free(this_ctx);
return tal_free(best_flow_paths);
}
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