15561477203

Committed 10 Jun 2025 01:54PM UTC coverage: 58.351% (-10.1%) from 68.487%

Build # 15561477203

Build Type

Pull #9356

github

Committed by

web-flow

Commit Message

Merge 6440b25db into c6d6d4c0b

Pull Request Pull Request #9356: lnrpc: add incoming/outgoing channel ids filter to forwarding history request

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6.08

/routing/probability_bimodal.go

package routing

import (
        "fmt"
        "math"
        "time"

        "github.com/btcsuite/btcd/btcutil"
        "github.com/go-errors/errors"
        "github.com/lightningnetwork/lnd/lnwire"
        "github.com/lightningnetwork/lnd/routing/route"
)

const (
        // DefaultBimodalScaleMsat is the default value for BimodalScaleMsat in
        // BimodalConfig. It describes the distribution of funds in the LN based
        // on empirical findings. We assume an unbalanced network by default.
        DefaultBimodalScaleMsat = lnwire.MilliSatoshi(300_000_000)

        // DefaultBimodalNodeWeight is the default value for the
        // BimodalNodeWeight in BimodalConfig. It is chosen such that past
        // forwardings on other channels of a router are only slightly taken
        // into account.
        DefaultBimodalNodeWeight = 0.2

        // DefaultBimodalDecayTime is the default value for BimodalDecayTime.
        // We will forget about previous learnings about channel liquidity on
        // the timescale of about a week.
        DefaultBimodalDecayTime = 7 * 24 * time.Hour

        // BimodalScaleMsatMax is the maximum value for BimodalScaleMsat. We
        // limit it here to the fakeHopHintCapacity to avoid issues with hop
        // hint probability calculations.
        BimodalScaleMsatMax = lnwire.MilliSatoshi(
                1000 * fakeHopHintCapacity / 4,
        )

        // BimodalEstimatorName is used to identify the bimodal estimator.
        BimodalEstimatorName = "bimodal"
)

var (
        // ErrInvalidScale is returned when we get a scale below or equal zero.
        ErrInvalidScale = errors.New("scale must be >= 0 and sane")

        // ErrInvalidNodeWeight is returned when we get a node weight that is
        // out of range.
        ErrInvalidNodeWeight = errors.New("node weight must be in [0, 1]")

        // ErrInvalidDecayTime is returned when we get a decay time below zero.
        ErrInvalidDecayTime = errors.New("decay time must be larger than zero")

        // ErrZeroCapacity is returned when we encounter a channel with zero
        // capacity in probability estimation.
        ErrZeroCapacity = errors.New("capacity must be larger than zero")
)

// BimodalConfig contains configuration for our probability estimator.
type BimodalConfig struct {
        // BimodalNodeWeight defines how strongly other previous forwardings on
        // channels of a router should be taken into account when computing a
        // channel's probability to route. The allowed values are in the range
        // [0, 1], where a value of 0 means that only direct information about a
        // channel is taken into account.
        BimodalNodeWeight float64

        // BimodalScaleMsat describes the scale over which channels
        // statistically have some liquidity left. The value determines how
        // quickly the bimodal distribution drops off from the edges of a
        // channel. A larger value (compared to typical channel capacities)
        // means that the drop off is slow and that channel balances are
        // distributed more uniformly. A small value leads to the assumption of
        // very unbalanced channels.
        BimodalScaleMsat lnwire.MilliSatoshi

        // BimodalDecayTime is the scale for the exponential information decay
        // over time for previous successes or failures.
        BimodalDecayTime time.Duration
}

// validate checks the configuration of the estimator for allowed values.
func (p BimodalConfig) validate() error {
        if p.BimodalDecayTime <= 0 {
                return fmt.Errorf("%v: %w", BimodalEstimatorName,
                        ErrInvalidDecayTime)
        }

        if p.BimodalNodeWeight < 0 || p.BimodalNodeWeight > 1 {
                return fmt.Errorf("%v: %w", BimodalEstimatorName,
                        ErrInvalidNodeWeight)
        }

        if p.BimodalScaleMsat == 0 || p.BimodalScaleMsat > BimodalScaleMsatMax {
                return fmt.Errorf("%v: %w", BimodalEstimatorName,
                        ErrInvalidScale)
        }

        return nil
}

// DefaultBimodalConfig returns the default configuration for the estimator.
func DefaultBimodalConfig() BimodalConfig {
        return BimodalConfig{
                BimodalNodeWeight: DefaultBimodalNodeWeight,
                BimodalScaleMsat:  DefaultBimodalScaleMsat,
                BimodalDecayTime:  DefaultBimodalDecayTime,
        }
}

// BimodalEstimator returns node and pair probabilities based on historical
// payment results based on a liquidity distribution model of the LN. The main
// function is to estimate the direct channel probability based on a depleted
// liquidity distribution model, with additional information decay over time. A
// per-node probability can be mixed with the direct probability, taking into
// account successes/failures on other channels of the forwarder.
type BimodalEstimator struct {
        // BimodalConfig contains configuration options for our estimator.
        BimodalConfig
}

// NewBimodalEstimator creates a new BimodalEstimator.
func NewBimodalEstimator(cfg BimodalConfig) (*BimodalEstimator, error) {
        if err := cfg.validate(); err != nil {
                return nil, err
        }

        return &BimodalEstimator{
                BimodalConfig: cfg,
        }, nil
}

// Compile-time checks that interfaces are implemented.
var _ Estimator = (*BimodalEstimator)(nil)
var _ estimatorConfig = (*BimodalConfig)(nil)

// config returns the current configuration of the estimator.
func (p *BimodalEstimator) Config() estimatorConfig {
        return p.BimodalConfig
}

// String returns the estimator's configuration as a string representation.
func (p *BimodalEstimator) String() string {
        return fmt.Sprintf("estimator type: %v, decay time: %v, liquidity "+
                "scale: %v, node weight: %v", BimodalEstimatorName,
                p.BimodalDecayTime, p.BimodalScaleMsat, p.BimodalNodeWeight)
}

// PairProbability estimates the probability of successfully traversing to
// toNode based on historical payment outcomes for the from node. Those outcomes
// are passed in via the results parameter.
func (p *BimodalEstimator) PairProbability(now time.Time,
        results NodeResults, toNode route.Vertex, amt lnwire.MilliSatoshi,
        capacity btcutil.Amount) float64 {

        // We first compute the probability for the desired hop taking into
        // account previous knowledge.
        directProbability := p.directProbability(
                now, results, toNode, amt, lnwire.NewMSatFromSatoshis(capacity),
        )

        // The final probability is computed by taking into account other
        // channels of the from node.
        return p.calculateProbability(directProbability, now, results, toNode)
}

// LocalPairProbability computes the probability to reach toNode given a set of
// previous learnings.
func (p *BimodalEstimator) LocalPairProbability(now time.Time,
        results NodeResults, toNode route.Vertex) float64 {

        // For direct local probabilities we assume to know exactly how much we
        // can send over a channel, which assumes that channels are active and
        // have enough liquidity.
        directProbability := 1.0

        // If we had an unexpected failure for this node, we reduce the
        // probability for some time to avoid infinite retries.
        result, ok := results[toNode]
        if ok && !result.FailTime.IsZero() {
                timeAgo := now.Sub(result.FailTime)

                // We only expect results in the past to get a probability
                // between 0 and 1.
                if timeAgo < 0 {
                        timeAgo = 0
                }
                exponent := -float64(timeAgo) / float64(p.BimodalDecayTime)
                directProbability -= math.Exp(exponent)
        }

        return directProbability
}

// directProbability computes the probability to reach a node based on the
// liquidity distribution in the LN.
func (p *BimodalEstimator) directProbability(now time.Time,
        results NodeResults, toNode route.Vertex, amt lnwire.MilliSatoshi,
        capacity lnwire.MilliSatoshi) float64 {

        // We first determine the time-adjusted success and failure amounts to
        // then compute a probability. We know that we can send a zero amount.
        successAmount := lnwire.MilliSatoshi(0)

        // We know that we cannot send the full capacity.
        failAmount := capacity

        // If we have information about past successes or failures, we modify
        // them with a time decay.
        result, ok := results[toNode]
        if ok {
                // Apply a time decay for the amount we cannot send.
                if !result.FailTime.IsZero() {
                        failAmount = cannotSend(
                                result.FailAmt, capacity, now, result.FailTime,
                                p.BimodalDecayTime,
                        )
                }

                // Apply a time decay for the amount we can send.
                if !result.SuccessTime.IsZero() {
                        successAmount = canSend(
                                result.SuccessAmt, now, result.SuccessTime,
                                p.BimodalDecayTime,
                        )
                }
        }

        // Compute the direct channel probability.
        probability, err := p.probabilityFormula(
                capacity, successAmount, failAmount, amt,
        )
        if err != nil {
                log.Errorf("error computing probability to node: %v "+
                        "(node: %v, results: %v, amt: %v, capacity: %v)",
                        err, toNode, results, amt, capacity)

                return 0.0
        }

        return probability
}

// calculateProbability computes the total hop probability combining the channel
// probability and historic forwarding data of other channels of the node we try
// to send from.
//
// Goals:
// * We want to incentivize good routing nodes: the more routable channels a
// node has, the more we want to incentivize (vice versa for failures).
// -> We reduce/increase the direct probability depending on past
// failures/successes for other channels of the node.
//
// * We want to be forgiving/give other nodes a chance as well: we want to
// forget about (non-)routable channels over time.
// -> We weight the successes/failures with a time decay such that they will not
// influence the total probability if a long time went by.
//
// * If we don't have other info, we want to solely rely on the direct
// probability.
//
// * We want to be able to specify how important the other channels are compared
// to the direct channel.
// -> Introduce a node weight factor that weights the direct probability against
// the node-wide average. The larger the node weight, the more important other
// channels of the node are.
//
// How do failures on low fee nodes redirect routing to higher fee nodes?
// Assumptions:
// * attemptCostPPM of 1000 PPM
// * constant direct channel probability of P0 (usually 0.5 for large amounts)
// * node weight w of 0.2
//
// The question we want to answer is:
// How often would a zero-fee node be tried (even if there were failures for its
// other channels) over trying a high-fee node with 2000 PPM and no direct
// knowledge about the channel to send over?
//
// The probability of a route of length l is P(l) = l * P0.
//
// The total probability after n failures (with the implemented method here) is:
// P(l, n) = P(l-1) * P(n)
// = P(l-1) * (P0 + n*0) / (1 + n*w)
// = P(l) / (1 + n*w)
//
// Condition for a high-fee channel to overcome a low fee channel in the
// Dijkstra weight function (only looking at fee and probability PPM terms):
// highFeePPM + attemptCostPPM * 1/P(l) = 0PPM + attemptCostPPM * 1/P(l, n)
// highFeePPM/attemptCostPPM = 1/P(l, n) - 1/P(l) =
// = (1 + n*w)/P(l) - 1/P(l) =
// = n*w/P(l)
//
// Therefore:
// n = (highFeePPM/attemptCostPPM) * (P(l)/w) =
// = (2000/1000) * 0.5 * l / w = l/w
//
// For a one-hop route we get:
// n = 1/0.2 = 5 tolerated failures
//
// For a three-hop route we get:
// n = 3/0.2 = 15 tolerated failures
//
// For more details on the behavior see tests.
func (p *BimodalEstimator) calculateProbability(directProbability float64,
        now time.Time, results NodeResults, toNode route.Vertex) float64 {

        // If we don't take other channels into account, we can return early.
        if p.BimodalNodeWeight == 0.0 {
                return directProbability
        }

        // If we have up-to-date information about the channel we want to use,
        // i.e. the info stems from results not longer ago than the decay time,
        // we will only use the direct probability. This is needed in order to
        // avoid that other previous results (on all other channels of the same
        // routing node) will distort and pin the calculated probability even if
        // we have accurate direct information. This helps to dip the
        // probability below the min probability in case of failures, to start
        // the splitting process.
        directResult, ok := results[toNode]
        if ok {
                latest := directResult.SuccessTime
                if directResult.FailTime.After(latest) {
                        latest = directResult.FailTime
                }

                // We use BimonodalDecayTime to judge the currentness of the
                // data. It is the time scale on which we assume to have lost
                // information.
                if now.Sub(latest) < p.BimodalDecayTime {
                        log.Tracef("Using direct probability for node %v: %v",
                                toNode, directResult)

                        return directProbability
                }
        }

        // w is a parameter which determines how strongly the other channels of
        // a node should be incorporated, the higher the stronger.
        w := p.BimodalNodeWeight

        // dt determines the timeliness of the previous successes/failures
        // to be taken into account.
        dt := float64(p.BimodalDecayTime)

        // The direct channel probability is weighted fully, all other results
        // are weighted according to how recent the information is.
        totalProbabilities := directProbability
        totalWeights := 1.0

        for peer, result := range results {
                // We don't include the direct hop probability here because it
                // is already included in totalProbabilities.
                if peer == toNode {
                        continue
                }

                // We add probabilities weighted by how recent the info is.
                var weight float64
                if result.SuccessAmt > 0 {
                        exponent := -float64(now.Sub(result.SuccessTime)) / dt
                        weight = math.Exp(exponent)
                        totalProbabilities += w * weight
                        totalWeights += w * weight
                }
                if result.FailAmt > 0 {
                        exponent := -float64(now.Sub(result.FailTime)) / dt
                        weight = math.Exp(exponent)

                        // Failures don't add to total success probability.
                        totalWeights += w * weight
                }
        }

        return totalProbabilities / totalWeights
}

// canSend returns the sendable amount over the channel, respecting time decay.
// canSend approaches zero, if we wait for a much longer time than the decay
// time.
func canSend(successAmount lnwire.MilliSatoshi, now, successTime time.Time,
        decayConstant time.Duration) lnwire.MilliSatoshi {

        // The factor approaches 0 for successTime a long time in the past,
        // is 1 when the successTime is now.
        factor := math.Exp(
                -float64(now.Sub(successTime)) / float64(decayConstant),
        )

        canSend := factor * float64(successAmount)

        return lnwire.MilliSatoshi(canSend)
}

// cannotSend returns the not sendable amount over the channel, respecting time
// decay. cannotSend approaches the capacity, if we wait for a much longer time
// than the decay time.
func cannotSend(failAmount, capacity lnwire.MilliSatoshi, now,
        failTime time.Time, decayConstant time.Duration) lnwire.MilliSatoshi {

        if failAmount > capacity {
                failAmount = capacity
        }

        // The factor approaches 0 for failTime a long time in the past and it
        // is 1 when the failTime is now.
        factor := math.Exp(
                -float64(now.Sub(failTime)) / float64(decayConstant),
        )

        cannotSend := capacity - lnwire.MilliSatoshi(
                factor*float64(capacity-failAmount),
        )

        return cannotSend
}

// primitive computes the indefinite integral of our assumed (normalized)
// liquidity probability distribution. The distribution of liquidity x here is
// the function P(x) ~ exp(-x/s) + exp((x-c)/s) + 1/c, i.e., two exponentials
// residing at the ends of channels. This means that we expect liquidity to be
// at either side of the channel with capacity c. The s parameter (scale)
// defines how far the liquidity leaks into the channel. A very low scale
// assumes completely unbalanced channels, a very high scale assumes a random
// distribution. More details can be found in
// https://github.com/lightningnetwork/lnd/issues/5988#issuecomment-1131234858.
// Additionally, we add a constant term 1/c to the distribution to avoid
// normalization issues and to fall back to a uniform distribution should the
// previous success and fail amounts contradict a bimodal distribution.
func (p *BimodalEstimator) primitive(c, x float64) float64 {
        s := float64(p.BimodalScaleMsat)

        // The indefinite integral of P(x) is given by
        // Int P(x) dx = H(x) = s * (-e(-x/s) + e((x-c)/s) + x/(c*s)),
        // and its norm from 0 to c can be computed from it,
        // norm = [H(x)]_0^c = s * (-e(-c/s) + 1 + 1/s -(-1 + e(-c/s))) =
        // = s * (-2*e(-c/s) + 2 + 1/s).
        // The prefactors s are left out, as they cancel out in the end.
        // norm can only become zero, if c is zero, which we sorted out before
        // calling this method.
        ecs := math.Exp(-c / s)
        norm := -2*ecs + 2 + 1/s

        // It would be possible to split the next term and reuse the factors
        // from before, but this can lead to numerical issues with large
        // numbers.
        excs := math.Exp((x - c) / s)
        exs := math.Exp(-x / s)

        // We end up with the primitive function of the normalized P(x).
        return (-exs + excs + x/(c*s)) / norm
}

// integral computes the integral of our liquidity distribution from the lower
// to the upper value.
func (p *BimodalEstimator) integral(capacity, lower, upper float64) float64 {
        if lower < 0 || lower > upper {
                log.Errorf("probability integral limits nonsensical: capacity:"+
                        "%v lower: %v upper: %v", capacity, lower, upper)

                return 0.0
        }

        return p.primitive(capacity, upper) - p.primitive(capacity, lower)
}

// probabilityFormula computes the expected probability for a payment of
// amountMsat given prior learnings for a channel of certain capacity.
// successAmountMsat and failAmountMsat stand for the unsettled success and
// failure amounts, respectively. The formula is derived using the formalism
// presented in Pickhardt et al., https://arxiv.org/abs/2103.08576.
func (p *BimodalEstimator) probabilityFormula(capacityMsat, successAmountMsat,
        failAmountMsat, amountMsat lnwire.MilliSatoshi) (float64, error) {

        // Convert to positive-valued floats.
        capacity := float64(capacityMsat)
        successAmount := float64(successAmountMsat)
        failAmount := float64(failAmountMsat)
        amount := float64(amountMsat)

        // In order for this formula to give reasonable results, we need to have
        // an estimate of the capacity of a channel (or edge between nodes).
        if capacity == 0.0 {
                return 0, ErrZeroCapacity
        }

        // We cannot send more than the capacity.
        if amount > capacity {
                return 0.0, nil
        }

        // The next statement is a safety check against an illogical condition.
        // We discard the knowledge for the channel in that case since we have
        // inconsistent data.
        if failAmount <= successAmount {
                log.Warnf("Fail amount (%s) is smaller than or equal to the "+
                        "success amount (%s) for capacity (%s)",
                        failAmountMsat, successAmountMsat, capacityMsat)

                successAmount = 0
                failAmount = capacity
        }

        // Mission control may have some outdated values with regard to the
        // current channel capacity between a node pair. This can happen in case
        // a large parallel channel was closed or if a channel was downscaled
        // and can lead to success and/or failure amounts to be out of the range
        // [0, capacity]. We assume that the liquidity situation of the channel
        // is similar as before due to flow bias.

        // In case we have a large success we need to correct it to be in the
        // valid range. We set the success amount close to the capacity, because
        // we assume to still be able to send. Any possible failure (that must
        // in this case be larger than the capacity) is corrected as well.
        if successAmount >= capacity {
                log.Debugf("Correcting success amount %s and failure amount "+
                        "%s to capacity %s", successAmountMsat,
                        failAmount, capacityMsat)

                // We choose the success amount to be one less than the
                // capacity, to both fit success and failure amounts into the
                // capacity range in a consistent manner.
                successAmount = capacity - 1
                failAmount = capacity
        }

        // Having no or only a small success, but a large failure only needs
        // adjustment of the failure amount.
        if failAmount > capacity {
                log.Debugf("Correcting failure amount %s to capacity %s",
                        failAmountMsat, capacityMsat)

                failAmount = capacity
        }

        // We cannot send more than the fail amount.
        if amount >= failAmount {
                return 0.0, nil
        }

        // We can send the amount if it is smaller than the success amount.
        if amount <= successAmount {
                return 1.0, nil
        }

        // The success probability for payment amount a is the integral over the
        // prior distribution P(x), the probability to find liquidity between
        // the amount a and channel capacity c (or failAmount a_f):
        // P(X >= a | X < a_f) = Integral_{a}^{a_f} P(x) dx
        prob := p.integral(capacity, amount, failAmount)
        if math.IsNaN(prob) {
                return 0.0, fmt.Errorf("non-normalized probability is NaN, "+
                        "capacity: %v, amount: %v, fail amount: %v",
                        capacity, amount, failAmount)
        }

        // If we have payment information, we need to adjust the prior
        // distribution P(x) and get the posterior distribution by renormalizing
        // the prior distribution in such a way that the probability mass lies
        // between a_s and a_f.
        reNorm := p.integral(capacity, successAmount, failAmount)
        if math.IsNaN(reNorm) {
                return 0.0, fmt.Errorf("normalization factor is NaN, "+
                        "capacity: %v, success amount: %v, fail amount: %v",
                        capacity, successAmount, failAmount)
        }

        // The normalization factor can only be zero if the success amount is
        // equal or larger than the fail amount. This should not happen as we
        // have checked this scenario above.
        if reNorm == 0.0 {
                return 0.0, fmt.Errorf("normalization factor is zero, "+
                        "capacity: %v, success amount: %v, fail amount: %v",
                        capacity, successAmount, failAmount)
        }

        prob /= reNorm

        // Note that for payment amounts smaller than successAmount, we can get
        // a value larger than unity, which we cap here to get a proper
        // probability.
        if prob > 1.0 {
                if amount > successAmount {
                        return 0.0, fmt.Errorf("unexpected large probability "+
                                "(%v) capacity: %v, amount: %v, success "+
                                "amount: %v, fail amount: %v", prob, capacity,
                                amount, successAmount, failAmount)
                }

                return 1.0, nil
        } else if prob < 0.0 {
                return 0.0, fmt.Errorf("negative probability "+
                        "(%v) capacity: %v, amount: %v, success "+
                        "amount: %v, fail amount: %v", prob, capacity,
                        amount, successAmount, failAmount)
        }

        return prob, nil
}

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