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Distributions

Enum Distributions 

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pub enum Distributions {
    Uniform(Jiffies, Jiffies),
    Bernoulli(f64, Jiffies),
    Normal(Jiffies, Jiffies),
}
Expand description

Probability distributions for modeling stochastic network behavior.

Distributions provides various probability distributions that can be used to model realistic network characteristics in DScale simulations. Each distribution generates values in Jiffies (simulation time units) and is commonly used for latency modeling, though they can be applied to any stochastic simulation parameter.

§Deterministic Randomness

All distributions use the simulation’s deterministic random number generator, ensuring that identical configurations with the same seed produce identical results. This is crucial for reproducible simulation experiments.

§Common Applications

  • Network Latency: Model variable message delivery times
  • Packet Loss: Simulate unreliable network conditions
  • Jitter: Add realistic variation to timing
  • Failure Models: Probabilistic component failures
  • Load Variation: Variable processing times

§Usage in Network Configuration

Distributions are primarily used with LatencyDescription to configure network topology characteristics:

use dscale::{SimulationBuilder, LatencyDescription, Distributions, Jiffies};

let simulation = SimulationBuilder::default()
    .add_pool::<MyProcess>("servers", 3)
    .latency_topology(&[
        // Low-latency local network with small variation
        LatencyDescription::WithinPool("servers",
            Distributions::Uniform(Jiffies(1), Jiffies(5))),

        // Internet latency with realistic variation
        LatencyDescription::BetweenPools("clients", "servers",
            Distributions::Normal(Jiffies(100), Jiffies(20))),

        // Unreliable network with 10% packet loss
        LatencyDescription::WithinPool("mobile",
            Distributions::Bernoulli(0.9, Jiffies(50))),
    ])
    .build();

Variants§

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Uniform(Jiffies, Jiffies)

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Bernoulli(f64, Jiffies)

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Normal(Jiffies, Jiffies)

Trait Implementations§

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impl Clone for Distributions

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fn clone(&self) -> Distributions

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Copy for Distributions

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impl<T> Any for T
where T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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impl<T> Borrow<T> for T
where T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
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impl<T> BorrowMut<T> for T
where T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
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impl<T> CloneToUninit for T
where T: Clone,

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unsafe fn clone_to_uninit(&self, dest: *mut u8)

🔬This is a nightly-only experimental API. (clone_to_uninit)
Performs copy-assignment from self to dest. Read more
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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into<U> for T
where U: From<T>,

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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T> ToOwned for T
where T: Clone,

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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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impl<V, T> VZip<V> for T
where V: MultiLane<T>,

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fn vzip(self) -> V