Struct RandomLoss 

pub struct RandomLoss { /* private fields */ }

Random loss model configuration.

Each packet has the given probability of being dropped. By default, each decision is independent (Bernoulli process), producing unrealistic “spread out” loss patterns.

Use correlation to make losses more bursty, or prefer GilbertElliot for more realistic bursty loss with finer control.

Example

use str0m_netem::{RandomLoss, Probability};

// 5% loss, independent
let simple = RandomLoss::new(Probability::new(0.05));

// 5% loss, bursty (losses cluster together)
let bursty = RandomLoss::new(Probability::new(0.05))
    .correlation(Probability::new(0.5));

Implementations

impl RandomLoss

pub fn new(probability: Probability) -> Self

Create a new random loss model with the given drop probability.

Loss decisions are independent by default (no correlation).

pub fn correlation(self, correlation: Probability) -> Self

Set correlation between consecutive loss decisions.

Controls how “bursty” random loss is:

• 0.0: Each loss decision is independent (Bernoulli process)
• 0.5: If a packet was lost, next packet is more likely to be lost too
• 0.9: Very bursty - losses come in clusters

For realistic bursty loss, consider using GilbertElliot instead, which provides more control over burst characteristics.

Trait Implementations

impl Clone for RandomLoss

fn clone(&self) -> RandomLoss

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for RandomLoss

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl From<RandomLoss> for LossModel

fn from(value: RandomLoss) -> Self

Converts to this type from the input type.

impl Copy for RandomLoss