14 Commits
Author SHA1 Message Date
swaits 794d6dffb1 bump crate to 0.6.1 2023-02-10 23:02:46 -05:00
swaits c3d210cf16 refactor: switch to array-init
- Use array-init for the lowest level vector operations. Makes them nicer and less bug-prone, with zero performance impact.
- Remove unnecessary/unused ::new() functions on Vector and HeightVector.
- Update README with new dependency.
- Fix typo in README.

Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-10 22:51:38 -05:00
swaits e79242118f bump crate to 0.6.0 2023-02-10 12:38:11 -07:00
swaits 0a62dc06b3 test sr.ht build manifest using package instead of cargo install
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-10 12:31:57 -07:00
swaits 79c90cbd4f test: get to 100% coverage, clean up cargo features
- eliminated `f64` feature because Cargo doesn’t understand mutually exclusive features. So, we default to `f64`, and leave the `f32` feature there if people want to change to that.
- integrated tarpaulin for coverage
- added tests to get to 100% coverage
- fixed NetworkCoordinate Div impl safety checks (now acts like Add and Sub)
- update README to explain cargo feature change
- cleanup build script with the new simpler features model

Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-10 12:14:24 -07:00
swaits f829708e5a test: add examples to CI
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 20:25:51 -07:00
swaits 1ce84b63ec test: add more stuff to CI
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 20:17:24 -07:00
swaits 7159c4b07a bump crate to 0.5.4
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 16:39:02 -07:00
swaits c07463122a perf: add benchmark, optimize
Realized about a 10x speedup.

- add benches/million-updates.rs
- optimization: use rand instead of nanorand (because of thread_rng() initialization being muchf aster)
- update README

Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 16:36:24 -07:00
swaits 2e97c879e1 bump crate to 0.5.3
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 11:18:04 -07:00
swaits 64543a8729 doc: updates to NetworkCoordinate
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 11:17:27 -07:00
swaits 8d1eadb017 enable cargo fmt check in autobuild
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 11:02:03 -07:00
swaits cdab359b4e enable and fix stricter clippy linting
- add clippy to .build.yml to run on sr.ht
- add missing docs
- use core:: instead of std:: where possible
- use hypot() and mul_add() for better numeric stability
- make all float tests use asser_approx_eq() and explicitly check for NaN/Inf
- add missing semicolons here and there
- add #[must_use] to ensure caller uses returned value
- add const fn where appropriate
- change visibility from pub(crate) to pub (in private modules)

Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-09 10:54:42 -07:00
swaits c3b3ff1e4d feat: f32/f64 cargo features added
Signed-off-by: Stephen Waits <steve@waits.net>
2023-02-08 17:01:19 -07:00
11 changed files with 429 additions and 178 deletions

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+28 -3
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@@ -1,23 +1,48 @@
image: archlinux
packages:
- rustup
- cargo-tarpaulin
sources:
- https://git.sr.ht/~swaits/vivaldi-nc
artifacts:
- vivaldi-nc/tarpaulin-report.html
tasks:
- setup: |
rustup toolchain install nightly stable
cd vivaldi-nc/
rustup run stable cargo fetch
- format: |
rustup default stable
cd vivaldi-nc/
cargo fmt --verbose --check
- lint: |
rustup default stable
cd vivaldi-nc/
cargo clippy --verbose
- coverage: |
rustup default stable
cd vivaldi-nc/
cargo tarpaulin --verbose --all-features --out Html
- stable: |
rustup default stable
cd vivaldi-nc/
cargo build --verbose
cargo test --verbose
cargo test --verbose --all-features
- nightly: |
rustup default nightly
cd vivaldi-nc/
cargo build --verbose ||:
cargo test --verbose ||:
cargo clippy --verbose ||:
cargo build --verbose ||:
cargo test --verbose --all-features ||:
- bench: |
rustup default stable
cd vivaldi-nc/
cargo bench --verbose --all-features
- examples: |
rustup default stable
cd vivaldi-nc/
cargo run --verbose --all-features --example planetlab
cargo run --verbose --all-features --example planetlab --release
- docs: |
cd vivaldi-nc/
rustup run stable cargo doc --no-deps
+2
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@@ -1,2 +1,4 @@
/flamegraph.svg
/target
Cargo.lock
/tarpaulin-report.html
+18 -2
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@@ -1,6 +1,6 @@
[package]
name = "vivaldi-nc"
version = "0.5.1"
version = "0.6.1"
edition = "2021"
authors = ["Stephen Waits <steve@waits.net>"]
description = "Vivaldi network coordinates for fast, distributed latency estimates in multinode networks, with a clean and simple interface."
@@ -15,13 +15,29 @@ exclude = [
"examples/*",
]
[features]
default = []
f32 = []
[profile.bench]
debug = true
[profile.release]
debug = true
[dev-dependencies]
assert_approx_eq = "1.1.0"
criterion = "0.4.0"
proptest = "1.0.0"
serde_json = "1.0.91"
[[bench]]
name = "million-updates"
harness = false
[dependencies]
nanorand = {version = "0.7.0", features = ["wyrand", "getrandom"]}
array-init = "2.1.0"
num-traits = "0.2.15"
rand = "0.8.5"
serde = {version = "1.0.152", features = ["serde_derive"]}
serde_with = "2.2.0"
+29 -9
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@@ -110,11 +110,22 @@ and compact binary formats like [bincode](https://crates.io/crates/bincode) and
See the [module documentation](https://docs.rs/vivaldi-nc) for more detailed
usage examples.
### Cargo Features
By default, the internal data structures and operations all use `f64`, which is
slightly faster on modern architectures. If you want to use `f32` instead, you
can enable it as a cargo feature:
```toml
[dependencies]
vivaldi-nc = { version = "(version)", features = ["f32"] }
```
### Examples
The repository includes an example which loads a n-to-n latency sample from
PlanetLab and iterates on `NetworkCoordinate`s until some low-enough mean error
is reached. The output is a JSON array of elements which contain the NC's
The repository includes an example which loads a 490 node N-to-N latency sample
from PlanetLab and iterates on `NetworkCoordinate`s until some low-enough mean
error is reached. The output is a JSON array of elements which contain the NC's
position, its height (or stem latency estimate), and its estimation of error
(lower is better) that look like this:
@@ -163,10 +174,11 @@ One design goal of this crate is to minimize dependencies. When dependencies
are required, I try to be very selective about them (re: bloat and licensing).
This crate depends on:
- [`nanorand`](https://crates.io/crates/nanorand): A fast PRNG based on WyRand.
It pulls in `getrandom` as a portable source of entropy. I chose this over
the more commonly used, but heavyweight `rand` because it's significantly
smaller and does just what I need, and not much more.
- [`rand`](https://crates.io/crates/rand): I started with `nanorand`; however,
this is faster because of `rand::thread_rng()`; whereas with `nanorand` we
ended up needing to reimplement that same functionality through something
like `lazy_static!` plus `Mutex`, which is not worth it for a few percentage
points of performance.
- [`num-traits`](https://crates.io/crates/num-traits): A brilliant crate which
makes it easier to operate on numbers in generics; like using `Float` as a
constraint on a generic type. Its convenience outweighs its cost.
@@ -176,12 +188,17 @@ This crate depends on:
- [`serde_with`](https://crates.io/crates/serde_with): Because the inner
`Vector<T,N>` uses a const generic length, we use this to help derive
`Deserialize`.
- [`array-init`](https://crates.io/crates/array-init): Makes the array
operations for the internal `Vector` so much nicer. Once we have
`feature(array_zip)` stabilized, we could use that instead. Until then, this
works well with zero performance impact.
## Design Goals & Alternatives
Several crates implemented Vivaldi NC before this one. So, why another?
I had several design goals which the existing crates didn't satisfy:
I had several design goals which the existing crates didn't satisfy, in order
of priority:
1. Provide the *simplest interface possible*. I just want to have some sort of
Coordinate struct and be able to update it, and then use it to estimate
@@ -193,7 +210,10 @@ I had several design goals which the existing crates didn't satisfy:
3. Serializable/Deserializable by default. These things are useful for sending
across networks. The intent is to be able to do that without a bunch of
rigamarole. Support `serde` traits by default.
4. Well documented. Well tested. This varies by the crates.
4. Well documented. Well tested. This varies in the existing crates.
5. Performance. While it's a low priority, it should be reasonable. The library
currently does about 10,000 `NetworkCoordinate::update()` calls per
millisecond on my Macbook Pro (Intel i7-9750H (12) @ 2.60GHz).
### Other Vivaldi NC Implementations
+52
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@@ -0,0 +1,52 @@
use std::time::Duration;
use criterion::{criterion_group, criterion_main, Criterion};
use vivaldi_nc::{NetworkCoordinate2D, NetworkCoordinate3D};
const NUM_NODES: usize = 1_000;
fn million_vivaldi_2d_updates() {
// create NUM_NODES NCs
let mut nc: Vec<NetworkCoordinate2D> =
(0..NUM_NODES).map(|_| NetworkCoordinate2D::new()).collect();
// loop, update each item for every other item
for i in 0..NUM_NODES {
let nc_i = nc[i].clone();
for (j, nc_j) in nc.iter_mut().enumerate().take(NUM_NODES) {
if i == j {
continue;
}
let rtt = if i > j { i - j } else { j - i };
// let nc_j = nc[j].clone();
nc_j.update(&nc_i, Duration::from_millis(rtt.try_into().unwrap()));
}
}
}
fn million_vivaldi_3d_updates() {
// create NUM_NODES NCs
let mut nc: Vec<NetworkCoordinate3D> =
(0..NUM_NODES).map(|_| NetworkCoordinate3D::new()).collect();
// loop, update each item for every other item
for i in 0..NUM_NODES {
let nc_i = nc[i].clone();
for (j, nc_j) in nc.iter_mut().enumerate().take(NUM_NODES) {
if i == j {
continue;
}
let rtt = if i > j { i - j } else { j - i };
// let nc_j = nc[j].clone();
nc_j.update(&nc_i, Duration::from_millis(rtt.try_into().unwrap()));
}
}
}
fn run_benchmarks(c: &mut Criterion) {
c.bench_function("million 2D updates", |b| b.iter(million_vivaldi_2d_updates));
c.bench_function("million 3D updates", |b| b.iter(million_vivaldi_3d_updates));
}
criterion_group!(benches, run_benchmarks);
criterion_main!(benches);
+17 -5
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@@ -2,13 +2,25 @@
//
// for info on the format, see `NetLatency-Data/README.md`
//
// **** Features ****
//
/// `FloatType` is a type alias for either `f32` or `f64` depending on cargo features
#[cfg(feature = "f32")]
type FloatType = f32;
/// `FloatType` is a type alias for either `f32` or `f64` depending on cargo features
#[cfg(not(feature = "f32"))]
type FloatType = f64;
use std::{
fs::File,
io::{BufReader, Read},
time::Duration,
};
use nanorand::{Rng, WyRand};
use rand::Rng;
use vivaldi_nc::NetworkCoordinate;
// this will return a Vec<Vec<Duration>>
@@ -66,10 +78,10 @@ fn main() {
// 4. adjust local NC
let mut error = 0.0;
for _ in 0..30_000 {
let mut rng = WyRand::new();
let mut rng = rand::thread_rng();
// 1. choose a local and remote NC
let i_local = rng.generate::<usize>() % NUM_NODES;
let i_remote = rng.generate::<usize>() % NUM_NODES;
let i_local = rng.gen_range(0..NUM_NODES);
let i_remote = rng.gen_range(0..NUM_NODES);
if i_local == i_remote {
continue;
}
@@ -85,7 +97,7 @@ fn main() {
nc[i_local].update(&nc_remote, rtt_measured);
// complete, now let's sum up the error
error = nc.iter().map(|n| n.error()).sum::<f32>() / NUM_NODES as f32;
error = nc.iter().map(|n| n.error()).sum::<FloatType>() / NUM_NODES as FloatType;
assert!(error.is_finite());
if error < 5.0 {
break;
+2
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@@ -8,3 +8,5 @@ cc 4a5cb6ca844d5306203ae00a99375c83a20ddf4ca427f0ae0b541697efa3d9b7 # shrinks to
cc 38ed1e2c313317809f5469d856d2c1c61e4851c64a156cd1bdd0757fa7c77128 # shrinks to x = 0.0, y = -0.0, h = 0.0, m = -0.0
cc b7d1b5e9fe3e33066b583c4532865373179972e9b5786ecc62438f23bc939e87 # shrinks to x = -0.0, y = 0.0, h = -3.448118e-33, m = -0.0
cc 3c4fc4f21c82ffe5bcbc740ca9ab598e0894226acb86a3046a3a24cbbcdbf8e6 # shrinks to x = 0.0, y = 0.0, h = -0.0
cc d1a551d7eb92bf69e3d0cd0b677baac5aaa5a95d6a016fccf67cf57af71c8b0c # shrinks to x = -5.098216e26, y = 0.0, h = 0.0
cc 7fa6f2f56f2398bae6804c5f0c0f3f47a7304e505c34f3addb676546cefd97f2 # shrinks to x = -0.0, y = 1.500389e38, h = 2.828499e38
+116 -65
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@@ -42,12 +42,24 @@
//! > Each node has a positive height element in its coordinates, so that
//! > its height can always be scaled up or down.
use std::ops::{Add, Mul, Sub};
use core::ops::{Add, Mul, Sub};
use nanorand::{Rng, WyRand};
use rand::prelude::*;
use serde::{Deserialize, Serialize};
use crate::vector::Vector;
use crate::vector::{self, Vector};
//
// **** Features ****
//
/// `FloatType` is a type alias for either `f32` or `f64` depending on cargo features
#[cfg(feature = "f32")]
type FloatType = f32;
/// `FloatType` is a type alias for either `f32` or `f64` depending on cargo features
#[cfg(not(feature = "f32"))]
type FloatType = f64;
//
// **** Constants ****
@@ -63,10 +75,16 @@ use crate::vector::Vector;
///
/// - `N`: the dimensionality of the vector portion (i.e. non-height) of the Vivaldi height vector
#[derive(Copy, Clone, Debug, Serialize, Deserialize)]
pub(crate) struct HeightVector<const N: usize> {
pub struct HeightVector<const N: usize> {
/// `position` is the Euclidean coordinate part of the `HeightVector`, representing a position
/// in the network's latency space
#[serde(flatten)]
position: Vector<f32, N>,
height: f32,
position: Vector<FloatType, N>,
/// `height` is a representation of a node's stemp time, or its latency to get into the network
/// core (or backbone). It's a way to account for triangle inequality violations.
/// in the network's latency space
height: FloatType,
}
//
@@ -74,21 +92,16 @@ pub(crate) struct HeightVector<const N: usize> {
//
impl<const N: usize> HeightVector<N> {
/// A new height vector is a random unit vector
pub(crate) fn new() -> Self {
Self::random()
}
/// A new height vector is a random unit vector
pub(crate) fn random() -> Self {
let mut rng = WyRand::new();
let mut rng = rand::thread_rng();
let mut vec = [0.0; N];
for i in vec.iter_mut().take(N) {
*i = rng.generate::<f32>();
*i = rng.gen::<FloatType>() - 0.5;
}
let height = rng.generate::<f32>().abs();
let height = rng.gen::<FloatType>().abs();
Self {
position: Vector::<f32, N>::from(vec),
position: Vector::<FloatType, N>::from(vec),
height,
}
.normalized()
@@ -96,7 +109,7 @@ impl<const N: usize> HeightVector<N> {
/// The magnitude of a Vivaldi height vector is defined as the magnitude of the vector plus the
/// height value.
pub(crate) fn len(&self) -> f32 {
pub(crate) fn len(&self) -> FloatType {
self.position.len() + self.height
}
@@ -104,15 +117,16 @@ impl<const N: usize> HeightVector<N> {
/// by the inveerse of its length.
pub(crate) fn normalized(&self) -> Self {
let len = self.len();
if len < f32::EPSILON {
// if we have a bad vector, generate a new random vector
Self::new()
// scale it by the inverse of its magnitude like any normal vector
let ret = Self {
position: self.position / len,
height: self.height / len,
};
if ret.is_valid() {
ret
} else {
// otherwise scale it by the inverse of its magnitude like any normal vector
Self {
position: self.position / len,
height: self.height / len,
}
// if we have a bad vector, generate a new random vector
Self::random()
}
}
@@ -144,17 +158,17 @@ impl<const N: usize> Default for HeightVector<N> {
/// Default value for a Vivaldi height vector is just the defaults of its children types.
fn default() -> Self {
Self {
position: Default::default(),
position: vector::Vector::default(),
height: Default::default(),
}
}
}
impl<const N: usize> From<([f32; N], f32)> for HeightVector<N> {
/// Convert from a `([f32; N], f32)` (vector, height) to a Vivaldi height vector type.
fn from(value: ([f32; N], f32)) -> Self {
impl<const N: usize> From<([FloatType; N], FloatType)> for HeightVector<N> {
/// Convert from a `([FloatType; N], FloatType)` (vector, height) to a Vivaldi height vector type.
fn from(value: ([FloatType; N], FloatType)) -> Self {
let ret = Self {
position: Vector::<f32, N>::from(value.0),
position: Vector::<FloatType, N>::from(value.0),
height: value.1,
};
if ret.is_valid() {
@@ -200,11 +214,11 @@ impl<const N: usize> Sub for HeightVector<N> {
}
}
impl<const N: usize> Mul<f32> for HeightVector<N> {
impl<const N: usize> Mul<FloatType> for HeightVector<N> {
type Output = Self;
/// Multiply a Vivaldi height vector by a scalar. Works the same as normal vector scaling.
fn mul(self, rhs: f32) -> Self::Output {
fn mul(self, rhs: FloatType) -> Self::Output {
let ret = Self {
position: self.position * rhs,
height: self.height * rhs,
@@ -228,59 +242,64 @@ mod tests {
proptest! {
#[test]
fn proptest_len(x: f32, y: f32, h: f32) {
let len = ((x*x) + (y*y)).sqrt() + h.abs();
fn proptest_len(x: FloatType, y: FloatType, h: FloatType) {
let len = x.hypot(y) + h.abs();
let a = HeightVector::<2>::from(([x,y],h));
println!("a = {a:#?}");
if x.is_nan() || x.is_infinite() || y.is_nan() || y.is_infinite() || h.is_nan() || h.is_infinite() || h < 0.0 {
// we should've gottne a random univ vector here
assert_approx_eq!(a.len(), 1.0);
} else {
// `HeightVector` we created from proptest values should be valid and have a length
assert_eq!(a.len(), len);
if len.is_nan() || len.is_infinite() {
assert!(a.len().is_nan() || a.len().is_infinite());
} else {
assert_approx_eq!(a.len(), len);
}
}
}
#[test]
fn proptest_add(x0 in -1_000_000_000..1_000_000_000i32, y0 in -1_000_000_000..1_000_000_000i32, h0 in 0..1_000_000_000i32, x1 in -1_000_000_000..1_000_000_000i32, y1 in -1_000_000_000..1_000_000_000i32, h1 in 0..1_000_000i32) {
// convert our integer range inputs to f32s
let (fx0,fy0,fh0) = (x0 as f32 / 1_000.0, y0 as f32 / 1_000.0, h0 as f32 / 1_000.0);
let (fx1,fy1,fh1) = (x1 as f32 / 1_000.0, y1 as f32 / 1_000.0, h1 as f32 / 1_000.0);
// convert our integer range inputs to FloatType
let (fx0,fy0,fh0) = (x0 as FloatType / 1_000.0, y0 as FloatType / 1_000.0, h0 as FloatType / 1_000.0);
let (fx1,fy1,fh1) = (x1 as FloatType / 1_000.0, y1 as FloatType / 1_000.0, h1 as FloatType / 1_000.0);
let a = HeightVector::<2>::from(([fx0 ,fy0 ],fh0 ));
let b = HeightVector::<2>::from(([fx1 ,fy1 ],fh1 ));
let c = a+b;
assert_eq!(c.position[0], fx0 + fx1);
assert_eq!(c.position[1], fy0 + fy1);
assert_eq!(c.height, fh0 + fh1);
assert_approx_eq!(c.position[0], fx0 + fx1);
assert_approx_eq!(c.position[1], fy0 + fy1);
assert_approx_eq!(c.height, fh0 + fh1);
}
#[test]
fn proptest_sub(x0 in -1_000_000_000..1_000_000_000i32, y0 in -1_000_000_000..1_000_000_000i32, h0 in 0..1_000_000_000i32, x1 in -1_000_000_000..1_000_000_000i32, y1 in -1_000_000_000..1_000_000_000i32, h1 in 0..1_000_000i32) {
// convert our integer range inputs to f32s
let (fx0,fy0,fh0) = (x0 as f32 / 1_000.0, y0 as f32 / 1_000.0, h0 as f32 / 1_000.0);
let (fx1,fy1,fh1) = (x1 as f32 / 1_000.0, y1 as f32 / 1_000.0, h1 as f32 / 1_000.0);
// convert our integer range inputs to FloatType
let (fx0,fy0,fh0) = (x0 as FloatType / 1_000.0, y0 as FloatType / 1_000.0, h0 as FloatType / 1_000.0);
let (fx1,fy1,fh1) = (x1 as FloatType / 1_000.0, y1 as FloatType / 1_000.0, h1 as FloatType / 1_000.0);
let a = HeightVector::<2>::from(([fx0 ,fy0 ],fh0 ));
let b = HeightVector::<2>::from(([fx1 ,fy1 ],fh1 ));
let c = a-b;
assert_eq!(c.position[0], fx0 - fx1);
assert_eq!(c.position[1], fy0 - fy1);
assert_eq!(c.height, fh0 + fh1);
assert_approx_eq!(c.position[0], fx0 - fx1);
assert_approx_eq!(c.position[1], fy0 - fy1);
assert_approx_eq!(c.height, fh0 + fh1);
}
#[test]
fn proptest_mul(x in -1_000_000_000..1_000_000_000i32, y in -1_000_000_000..1_000_000_000i32, h in 0..1_000_000_000i32, m in -1_000_000_000..1_000_000_000i32) {
// convert our integer range inputs to f32s
let (fx,fy,fh,fm) = (x as f32 / 1_000.0, y as f32 / 1_000.0, h as f32 / 1_000.0, m as f32 / 1_000.0);
// convert our integer range inputs to FloatType
let (fx,fy,fh,fm) = (x as FloatType / 1_000.0, y as FloatType / 1_000.0, h as FloatType / 1_000.0, m as FloatType / 1_000.0);
let a = HeightVector::<2>::from(([fx, fy], fh));
let b = a * fm;
if fm < 0.0 {
assert_approx_eq!(b.len(), 1.0)
assert_approx_eq!(b.len(), 1.0);
} else {
assert_eq!(b.position[0], fx * fm);
assert_eq!(b.position[1], fy * fm);
assert_eq!(b.height, fh * fm);
assert_approx_eq!(b.position[0], fx * fm);
assert_approx_eq!(b.position[1], fy * fm);
assert_approx_eq!(b.height, fh * fm);
}
}
}
@@ -294,7 +313,7 @@ mod tests {
#[test]
fn test_new() {
// new gives us a random unit length vec
let a = HeightVector::<3>::new();
let a = HeightVector::<3>::random();
assert_approx_eq!(a.len(), 1.0);
}
@@ -308,9 +327,9 @@ mod tests {
#[test]
fn test_from() {
let a = HeightVector::<2>::from(([1.0, 2.0], 3.0));
assert_eq!(a.position[0], 1.0);
assert_eq!(a.position[1], 2.0);
assert_eq!(a.height, 3.0);
assert_approx_eq!(a.position[0], 1.0);
assert_approx_eq!(a.position[1], 2.0);
assert_approx_eq!(a.height, 3.0);
}
#[test]
@@ -318,9 +337,9 @@ mod tests {
let a = HeightVector::<2>::from(([1.0, 2.0], 3.0));
let b = HeightVector::<2>::from(([3.0, 2.0], 1.0));
let c = a + b;
assert_eq!(c.position[0], 4.0);
assert_eq!(c.position[1], 4.0);
assert_eq!(c.height, 4.0);
assert_approx_eq!(c.position[0], 4.0);
assert_approx_eq!(c.position[1], 4.0);
assert_approx_eq!(c.height, 4.0);
}
#[test]
@@ -328,17 +347,17 @@ mod tests {
let a = HeightVector::<2>::from(([1.0, 2.0], 3.0));
let b = HeightVector::<2>::from(([3.0, 2.0], 1.0));
let c = a - b;
assert_eq!(c.position[0], -2.0);
assert_eq!(c.position[1], 0.0);
assert_eq!(c.height, 4.0);
assert_approx_eq!(c.position[0], -2.0);
assert_approx_eq!(c.position[1], 0.0);
assert_approx_eq!(c.height, 4.0);
}
#[test]
fn test_mul_scalar() {
let a = HeightVector::<2>::from(([1.0, 2.0], 3.0)) * 10.0;
assert_eq!(a.position[0], 10.0);
assert_eq!(a.position[1], 20.0);
assert_eq!(a.height, 30.0);
assert_approx_eq!(a.position[0], 10.0);
assert_approx_eq!(a.position[1], 20.0);
assert_approx_eq!(a.height, 30.0);
}
#[test]
@@ -354,4 +373,36 @@ mod tests {
let t = serde_json::to_string(&a);
assert_eq!(t.as_ref().unwrap(), s);
}
#[test]
fn test_sub_invalid() {
let valid = HeightVector::<2>::from(([1.0, 2.0], 3.0));
let mut invalid = HeightVector::<2>::from(([1.0, 2.0], 3.0));
// force invalidity here - because `::from()` actually also catches this
invalid.height = 1.0 / 0.0;
let result = valid - invalid;
assert_approx_eq!(result.len(), 1.0);
}
#[test]
fn test_add_invalid() {
let valid = HeightVector::<2>::from(([1.0, 2.0], 3.0));
let mut invalid = HeightVector::<2>::from(([1.0 / 0.0, 2.0], 3.0));
// make sure `from()` caught and replaced the invalid vec
assert_approx_eq!(invalid.len(), 1.0);
// force invalidity here - because `::from()` actually also catches this
invalid.height = 1.0 / 0.0;
let result = valid + invalid;
assert_approx_eq!(result.len(), 1.0);
}
#[test]
fn test_zero_norm() {
let a = HeightVector::<2>::from(([0.0, 0.0], 0.0));
let b = a.normalized();
let len = b.len();
assert_approx_eq!(len, 1.0);
}
}
+12
View File
@@ -93,6 +93,18 @@
//! ```
//!
#![deny(clippy::all)]
#![deny(clippy::correctness)]
#![deny(clippy::style)]
#![deny(clippy::suspicious)]
#![deny(clippy::complexity)]
#![deny(clippy::perf)]
#![deny(clippy::cargo)]
#![deny(clippy::pedantic)]
#![deny(clippy::nursery)]
#![allow(clippy::type_repetition_in_bounds)]
#![allow(clippy::module_name_repetitions)]
mod height_vector;
mod vector;
+106 -48
View File
@@ -1,29 +1,6 @@
//! A Vivaldi [`NetworkCoordinate`]
//!
//! This is an implementation of Vivaldi NCs per the original paper. It implements the following
//! alogirthm (quoting from paper):
//!
//! ```text
//! // Incorporate new information: node j has been
//! // measured to be rtt ms away, has coordinates x j,
//! // and an error estimate of e j .
//! //
//! // Our own coordinates and error estimate are xi and ei.
//! //
//! // The constants ce and cc are tuning parameters.
//!
//! vivaldi(rtt, xj, ej)
//! // Sample weight balances local and remote error. (1)
//! w = ei /(ei + ej)
//! // Compute relative error of this sample. (2)
//! es = ∣∣∣‖xi − xj‖ − rtt∣∣∣/rtt
//! // Update weighted moving average of local error. (3)
//! ei = es × ce × w + ei × (1 − ce × w)
//! // Update local coordinates. (4)
//! δ = cc × w
//! xi = xi + δ × (rtt − ‖xi − xj ‖) × u(xi − xj)
//! ```
//! Main interface module for Vivaldi network coordinates.
//!
//! For usage explanation and examples, please see the main [`crate`] documentation.
use std::time::Duration;
@@ -31,29 +8,53 @@ use serde::{Deserialize, Serialize};
use crate::height_vector::HeightVector;
//
// **** Features ****
//
#[cfg(feature = "f32")]
type FloatType = f32;
#[cfg(not(feature = "f32"))]
type FloatType = f64;
//
// **** Constants ****
//
// Vivaldi tuning parameters
const C_ERROR: f32 = 0.25;
const C_DELTA: f32 = 0.25;
const C_ERROR: FloatType = 0.25;
const C_DELTA: FloatType = 0.25;
// initial error value
const DEFAULT_ERROR: f32 = 200.0;
const DEFAULT_ERROR: FloatType = 200.0;
// error should always be greater than zero
const MIN_ERROR: f32 = f32::EPSILON;
const MIN_ERROR: FloatType = FloatType::EPSILON;
//
// **** Structs ****
//
/// A `NetworkCoordinate<N>` is the main interface to a Vivaldi network coordinate.
///
/// # Generic Parameters
///
/// - `N`: Const generic for number of dimensions. For example, `NetworkCoordinate<3>` is a
/// 3-Dimentionsal Euclidean coordinate plus a height. Should be a positive number greater than
/// zero.
///
/// **Note:** Dimensions other than 2D or 3D are usually not useful. If you want to use one of
/// those dimensions, you can use type aliases ([`NetworkCoordinate2D`] or [`NetworkCoordinate3D`])
/// which are a little more ergonomic than using the generic here.
///
/// # Examples
///
/// For an explanation and examples of usage, please see the main [`crate`] documentation.
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct NetworkCoordinate<const N: usize> {
#[serde(flatten)]
heightvec: HeightVector<N>,
error: f32,
error: FloatType,
}
// type aliases for convenience
@@ -61,13 +62,13 @@ pub struct NetworkCoordinate<const N: usize> {
/// A 2D [`NetworkCoordinate`]. Includes a 2D Euclidean position and a height.
///
/// This type alias is just for convenience. It's functionally equivalent to
/// `NetworkCoordinate<2>`.
/// `NetworkCoordinate<2>`. For more information, see [`NetworkCoordinate`].
pub type NetworkCoordinate2D = NetworkCoordinate<2>;
/// A 3D [`NetworkCoordinate`]. Includes a 3D Euclidean position and a height.
///
/// This type alias is just for convenience. It's functionally equivalent to
/// `NetworkCoordinate<3>`.
/// `NetworkCoordinate<3>`. For more information, see [`NetworkCoordinate`].
pub type NetworkCoordinate3D = NetworkCoordinate<3>;
//
@@ -88,6 +89,7 @@ impl<const N: usize> NetworkCoordinate<N> {
/// // print the NC
/// println!("Our new NC is: {:#?}", a);
/// ```
#[must_use]
pub fn new() -> Self {
Self::default()
}
@@ -119,9 +121,14 @@ impl<const N: usize> NetworkCoordinate<N> {
/// println!("Estimated RTT: {}", a.estimated_rtt(&b).as_millis());
/// ```
///
#[must_use]
pub fn estimated_rtt(&self, rhs: &Self) -> Duration {
// estimated rss is euclidean distance between the two plus the sum of the heights
Duration::from_secs_f32((self.heightvec - rhs.heightvec).len() / 1000.0)
#[cfg(feature = "f32")]
return Duration::from_secs_f32((self.heightvec - rhs.heightvec).len() / 1000.0);
#[cfg(not(feature = "f32"))]
return Duration::from_secs_f64((self.heightvec - rhs.heightvec).len() / 1000.0);
}
/// Given another Vivaldi [`NetworkCoordinate`], adjust our coordinateto better represent the actual round
@@ -154,16 +161,53 @@ impl<const N: usize> NetworkCoordinate<N> {
/// // Now we can update our NC to adjust our position relative to the remote node:
/// local.update(&remote, rtt);
/// ```
///
/// # Algorithm
///
/// This is an implementation of Vivaldi NCs per the original paper. It implements the following
/// alogirthm (quoting from paper):
///
/// ```text
/// // Incorporate new information: node j has been
/// // measured to be rtt ms away, has coordinates xj,
/// // and an error estimate of ej .
/// //
/// // Our own coordinates and error estimate are xi and ei.
/// //
/// // The constants ce and cc are tuning parameters.
///
/// vivaldi(rtt, xj, ej)
/// // Sample weight balances local and remote error. (1)
/// w = ei /(ei + ej)
/// // Compute relative error of this sample. (2)
/// es = ∣∣∣‖xi − xj‖ − rtt∣∣∣/rtt
/// // Update weighted moving average of local error. (3)
/// ei = es × ce × w + ei × (1 − ce × w)
/// // Update local coordinates. (4)
/// δ = cc × w
/// xi = xi + δ × (rtt − ‖xi − xj ‖) × u(xi − xj)
/// ```
///
pub fn update(&mut self, rhs: &Self, rtt: Duration) -> &Self {
// rtt needs to be positive
if rtt.as_secs_f32() < 0.0 {
return self;
}
// convert Durations into f32s as fractional milliseconds for convenience
// convert Durations into FloatType as fractional milliseconds for convenience
#[cfg(feature = "f32")]
let rtt_ms = rtt.as_secs_f32() * 1000.0;
#[cfg(feature = "f32")]
let rtt_estimated_ms = self.estimated_rtt(rhs).as_secs_f32() * 1000.0;
#[cfg(not(feature = "f32"))]
let rtt_ms = rtt.as_secs_f64() * 1000.0;
#[cfg(not(feature = "f32"))]
let rtt_estimated_ms = self.estimated_rtt(rhs).as_secs_f64() * 1000.0;
// rtt needs to be positive
if rtt_ms < 0.0 {
// Note: `rtt` is guaranteed to be positive because `Duration` enforces it.
// If this panics, something changed where `Duration` now allows for negative
// values.
unreachable!();
}
// Sample weight balances local and remote error. (1)
// w = ei /(ei + ej )
let w = self.error / (self.error + rhs.error);
@@ -175,7 +219,11 @@ impl<const N: usize> NetworkCoordinate<N> {
// Update weighted moving average of local error. (3)
// ei = es × ce × w + ei × (1 − ce × w)
self.error = (es * C_ERROR * w + self.error * (1.0 - C_ERROR * w)).max(MIN_ERROR);
// self.error = (es * C_ERROR * w + self.error * (1.0 - C_ERROR * w)).max(MIN_ERROR);
// NOTE: using `mul_add()` which is a little safer (avoid overflows)
self.error = (es * C_ERROR)
.mul_add(w, self.error * C_ERROR.mul_add(-w, 1.0))
.max(MIN_ERROR);
// Update local coordinates. (4)
// δ = cc × w
@@ -187,7 +235,11 @@ impl<const N: usize> NetworkCoordinate<N> {
// if we ended up with an invalid coordinate, return a new random coordinate with default
// error
if self.heightvec.is_invalid() {
*self = Self::new()
*self = Self::new();
// We should never get here because the call to `normalized()` above should catch any
// invalid `heightvec`
unreachable!();
}
// return reference to updated self
@@ -196,7 +248,8 @@ impl<const N: usize> NetworkCoordinate<N> {
/// getter for error value - useful for consumers to understand the estimated accuracty of this
/// `NetworkCoordinate`
pub fn error(&self) -> f32 {
#[must_use]
pub const fn error(&self) -> FloatType {
self.error
}
}
@@ -206,7 +259,7 @@ impl<const N: usize> NetworkCoordinate<N> {
//
impl<const N: usize> Default for NetworkCoordinate<N> {
/// A default `NetworkCoordinate` has a random position and DEFAULT_ERROR
/// A default `NetworkCoordinate` has a random position and `DEFAULT_ERROR`
fn default() -> Self {
Self {
heightvec: HeightVector::<N>::random(),
@@ -273,10 +326,8 @@ mod tests {
let mut mad = NetworkCoordinate::<2>::new();
// verify the initial error
let error =
(slc.error.powf(2.0) + nyc.error.powf(2.0) + lax.error.powf(2.0) + mad.error.powf(2.0))
.sqrt();
assert_eq!(error, 400.0);
let error = slc.error.hypot(nyc.error.hypot(lax.error.hypot(mad.error)));
assert_approx_eq!(error, 400.0);
// iterate plenty of times to converge and minimize error
(0..20).for_each(|_| {
@@ -313,7 +364,7 @@ mod tests {
// make sure it's the right length and works like we expect a normal NC
assert_approx_eq!(a.heightvec.len(), 2.649_509, 0.001);
assert_eq!(a.error, 1.0);
assert_approx_eq!(a.error, 1.0);
assert_eq!(a.estimated_rtt(&a).as_millis(), 0);
// serialize it into a new JSON string and make sure it matches the original
@@ -332,4 +383,11 @@ mod tests {
let estimate = a.estimated_rtt(&b);
assert_approx_eq!(estimate.as_secs_f32(), 0.080_099);
}
#[test]
fn test_error_getter() {
let s = "{\"position\":[1.5,0.5,2.0],\"height\":25.0,\"error\":1.0}";
let a: NetworkCoordinate<3> = serde_json::from_str(s).unwrap();
assert_approx_eq!(a.error(), 1.0);
}
}
+47 -46
View File
@@ -3,6 +3,7 @@
//! This is the simplest possible implementation of a vector to support the needs of a
//! Vivaldi `HeightVector`.
use array_init::array_init;
use num_traits::Float;
use serde::{Deserialize, Serialize};
use serde_with::serde_as;
@@ -20,7 +21,7 @@ use std::ops::{Add, Div, Index, Mul, Sub};
/// - `N`: the number of dimensions (i.e. 2 for 2D vectors, 3 for 3D, etc)
#[serde_as]
#[derive(Copy, Clone, Debug, PartialEq, Eq, Serialize, Deserialize)]
pub(crate) struct Vector<T, const N: usize>
pub struct Vector<T, const N: usize>
where
T: Float + Serialize + for<'d> Deserialize<'d>,
{
@@ -37,17 +38,10 @@ impl<T, const N: usize> Vector<T, N>
where
T: Float + Serialize + for<'d> Deserialize<'d>,
{
/// Create a new Zero vector.
pub(crate) fn new() -> Self {
Self::default()
}
/// Compute magnitude of vector.
pub(crate) fn len(&self) -> T {
(0..N)
.map(|i| self.inner[i] * self.inner[i])
.fold(T::zero(), |sum_sq, x| sum_sq + x)
.sqrt()
// use hypot() instead of sqrt() of sum of squares to avoid overflows
self.inner.iter().fold(T::zero(), |acc, x| acc.hypot(*x))
}
/// Checks whether the `Vector` is invalid.
@@ -92,11 +86,9 @@ where
/// Vector+Vector addition.
fn add(self, rhs: Self) -> Self::Output {
let mut ret = [T::zero(); N]; //Self::Output::new();
for i in 0..N {
ret[i] = self[i] + rhs[i];
Self::Output {
inner: array_init(|i| self[i] + rhs[i]),
}
Self::Output::from(ret)
}
}
@@ -108,11 +100,9 @@ where
/// Vector-Vector subtraction.
fn sub(self, rhs: Self) -> Self::Output {
let mut ret = [T::zero(); N]; //Self::Output::new();
for i in 0..N {
ret[i] = self[i] - rhs[i];
Self::Output {
inner: array_init(|i| self[i] - rhs[i]),
}
Self::Output::from(ret)
}
}
@@ -124,11 +114,9 @@ where
/// Vector*scalar multiplication.
fn mul(self, rhs: T) -> Self::Output {
let mut ret = Self::Output::new();
for i in 0..N {
ret.inner[i] = self.inner[i] * rhs;
Self::Output {
inner: array_init(|i| self[i] * rhs),
}
ret
}
}
@@ -140,11 +128,9 @@ where
/// Vector/scalar division.
fn div(self, rhs: T) -> Self::Output {
let mut ret = Self::Output::new();
for i in 0..N {
ret.inner[i] = self.inner[i] / rhs;
Self::Output {
inner: array_init(|i| self[i] / rhs),
}
ret
}
}
@@ -175,9 +161,9 @@ mod tests {
#[test]
fn proptest_new(x: f32, y: f32, z: f32){
let v = Vector::<f32, 3>::from([x,y,z]);
assert_eq!(v[0], x);
assert_eq!(v[1], y);
assert_eq!(v[2], z);
assert_approx_eq!(v[0], x);
assert_approx_eq!(v[1], y);
assert_approx_eq!(v[2], z);
assert!(v[0].is_finite());
assert!(v[1].is_finite());
assert!(v[1].is_finite());
@@ -187,9 +173,25 @@ mod tests {
fn proptest_mul(x: f32, y: f32, z: f32, m: f32) {
let v = Vector::<f32, 3>::from([x,y,z]);
let w = v * m;
assert_eq!(v[0] * m, w[0]);
assert_eq!(v[1] * m, w[1]);
assert_eq!(v[2] * m, w[2]);
let (x,y,z) = (v[0]*m, v[1]*m, v[2]*m);
if x.is_nan() || x.is_infinite() {
assert!(w[0].is_nan() || w[0].is_infinite());
} else {
assert_approx_eq!(w[0], x);
}
if y.is_nan() || y.is_infinite() {
assert!(w[1].is_nan() || w[1].is_infinite());
} else {
assert_approx_eq!(w[1], y);
}
if z.is_nan() || z.is_infinite() {
assert!(w[2].is_nan() || w[2].is_infinite());
} else {
assert_approx_eq!(w[2], z);
}
}
#[test]
@@ -198,23 +200,22 @@ mod tests {
let w = v / d;
let (x,y,z) = (v[0]/d, v[1]/d, v[2]/d);
if x.is_nan() {
assert!(w[0].is_nan());
if x.is_nan() || x.is_infinite() {
assert!(w[0].is_nan() ||w[0].is_infinite());
} else {
assert_eq!(v[0] / d, x);
assert_approx_eq!(w[0], x);
}
if y.is_nan() {
assert!(w[1].is_nan());
if y.is_nan() || y.is_infinite() {
assert!(w[1].is_nan() || w[1].is_infinite());
} else {
assert_eq!(v[1] / d, y);
assert_approx_eq!(w[1], y);
}
if z.is_nan() {
assert!(w[2].is_nan());
if z.is_nan() || z.is_infinite() {
assert!(w[2].is_nan() || w[2].is_infinite());
} else {
assert_eq!(v[2] / d, z);
assert_approx_eq!(w[2], z);
}
}
}
@@ -222,7 +223,7 @@ mod tests {
#[test]
fn test_default_new() {
let a = Vector::<f32, 3>::default();
let b = Vector::<f32, 3>::new();
let b = Vector::<f32, 3>::from([1.0; 3]) * 0.0;
let c = Vector::<f32, 3>::from([0.0; 3]);
assert_eq!(a, b);
assert_eq!(b, c);
@@ -263,9 +264,9 @@ mod tests {
#[test]
fn test_index() {
let a = Vector::<f32, 3>::from([1.0, 2.0, 3.0]);
assert_eq!(a[0], 1.0);
assert_eq!(a[1], 2.0);
assert_eq!(a[2], 3.0);
assert_approx_eq!(a[0], 1.0);
assert_approx_eq!(a[1], 2.0);
assert_approx_eq!(a[2], 3.0);
}
#[test]
fn test_length() {