Deuxfleurs/garage
48
56
Fork 73
Code Issues 99 Pull requests 13 Releases 30 Wiki Activity

cargo fmt
Some checks failed
continuous-integration/drone/push Build is failing
Details
continuous-integration/drone/pr Build is failing
Details

Browse source
This commit is contained in:
Mendes 2022-10-10 17:21:13 +02:00
parent fcf9ac674a
commit 4abab246f1
8 changed files with 1109 additions and 985 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
src/api/admin/cluster.rs
View file

@ -163,10 +163,10 @@ pub async fn handle_apply_cluster_layout(
let layout = garage.system.get_cluster_layout();
let (layout, msg) = layout.apply_staged_changes(Some(param.version))?;
//TODO : how to display msg ? Should it be in the Body Response ?
for s in msg.iter() {
println!("{}", s);
}
//TODO : how to display msg ? Should it be in the Body Response ?
for s in msg.iter() {
println!("{}", s);
}
garage.system.update_cluster_layout(&layout).await?;

1
src/db/lib.rs
View file

@ -4,7 +4,6 @@ extern crate tracing;
#[cfg(not(any(feature = "lmdb", feature = "sled", feature = "sqlite")))]
//compile_error!("Must activate the Cargo feature for at least one DB engine: lmdb, sled or sqlite.");
#[cfg(feature = "lmdb")]
pub mod lmdb_adapter;
#[cfg(feature = "sled")]

94
src/garage/cli/layout.rs
View file

@ -27,9 +27,9 @@ pub async fn cli_layout_command_dispatch(
LayoutOperation::Revert(revert_opt) => {
cmd_revert_layout(system_rpc_endpoint, rpc_host, revert_opt).await
}
LayoutOperation::Config(config_opt) => {
cmd_config_layout(system_rpc_endpoint, rpc_host, config_opt).await
}
LayoutOperation::Config(config_opt) => {
cmd_config_layout(system_rpc_endpoint, rpc_host, config_opt).await
}
}
}
@ -188,30 +188,37 @@ pub async fn cmd_show_layout(
println!("No nodes have a role in the new layout.");
}
println!();
println!("==== PARAMETERS OF THE LAYOUT COMPUTATION ====");
println!("Zone redundancy: {}", layout.staged_parameters.get().zone_redundancy);
println!(
"Zone redundancy: {}",
layout.staged_parameters.get().zone_redundancy
);
println!();
// this will print the stats of what partitions
// will move around when we apply
match layout.calculate_partition_assignation() {
Ok(msg) => {
for line in msg.iter() {
println!("{}", line);
}
println!("To enact the staged role changes, type:");
println!();
println!(" garage layout apply --version {}", layout.version + 1);
println!();
println!(
match layout.calculate_partition_assignation() {
Ok(msg) => {
for line in msg.iter() {
println!("{}", line);
}
println!("To enact the staged role changes, type:");
println!();
println!(" garage layout apply --version {}", layout.version + 1);
println!();
println!(
"You can also revert all proposed changes with: garage layout revert --version {}",
layout.version + 1)},
Err(Error::Message(s)) => {
println!("Error while trying to compute the assignation: {}", s);
println!("This new layout cannot yet be applied.");},
_ => { println!("Unknown Error"); },
}
layout.version + 1)
}
Err(Error::Message(s)) => {
println!("Error while trying to compute the assignation: {}", s);
println!("This new layout cannot yet be applied.");
}
_ => {
println!("Unknown Error");
}
}
}
Ok(())
@ -225,9 +232,9 @@ pub async fn cmd_apply_layout(
let layout = fetch_layout(rpc_cli, rpc_host).await?;
let (layout, msg) = layout.apply_staged_changes(apply_opt.version)?;
for line in msg.iter() {
println!("{}", line);
}
for line in msg.iter() {
println!("{}", line);
}
send_layout(rpc_cli, rpc_host, layout).await?;
@ -258,26 +265,29 @@ pub async fn cmd_config_layout(
config_opt: ConfigLayoutOpt,
) -> Result<(), Error> {
let mut layout = fetch_layout(rpc_cli, rpc_host).await?;
match config_opt.redundancy {
None => (),
Some(r) => {
if r > layout.replication_factor {
println!("The zone redundancy must be smaller or equal to the \
replication factor ({}).", layout.replication_factor);
}
else if r < 1 {
println!("The zone redundancy must be at least 1.");
}
else {
layout.staged_parameters.update(LayoutParameters{ zone_redundancy: r });
println!("The new zone redundancy has been saved ({}).", r);
}
}
}
match config_opt.redundancy {
None => (),
Some(r) => {
if r > layout.replication_factor {
println!(
"The zone redundancy must be smaller or equal to the \
replication factor ({}).",
layout.replication_factor
);
} else if r < 1 {
println!("The zone redundancy must be at least 1.");
} else {
layout
.staged_parameters
.update(LayoutParameters { zone_redundancy: r });
println!("The new zone redundancy has been saved ({}).", r);
}
}
}
send_layout(rpc_cli, rpc_host, layout).await?;
Ok(())
Ok(())
}
// --- utility ---

7
src/garage/cli/structs.rs
View file

@ -86,10 +86,10 @@ pub enum LayoutOperation {
/// Remove role from Garage cluster node
#[structopt(name = "remove", version = garage_version())]
Remove(RemoveRoleOpt),
/// Configure parameters value for the layout computation
/// Configure parameters value for the layout computation
#[structopt(name = "config", version = garage_version())]
Config(ConfigLayoutOpt),
Config(ConfigLayoutOpt),
/// Show roles currently assigned to nodes and changes staged for commit
#[structopt(name = "show", version = garage_version())]
@ -104,7 +104,6 @@ pub enum LayoutOperation {
Revert(RevertLayoutOpt),
}
#[derive(StructOpt, Debug)]
pub struct AssignRoleOpt {
/// Node(s) to which to assign role (prefix of hexadecimal node id)

710
src/rpc/graph_algo.rs
View file

@ -1,42 +1,40 @@
//! This module deals with graph algorithms.
//! It is used in layout.rs to build the partition to node assignation.
use rand::prelude::SliceRandom;
use std::cmp::{max, min};
use std::collections::VecDeque;
use std::collections::HashMap;
use std::collections::VecDeque;
//Vertex data structures used in all the graphs used in layout.rs.
//usize parameters correspond to node/zone/partitions ids.
//To understand the vertex roles below, please refer to the formal description
//of the layout computation algorithm.
#[derive(Clone,Copy,Debug, PartialEq, Eq, Hash)]
pub enum Vertex{
Source,
Pup(usize), //The vertex p+ of partition p
Pdown(usize), //The vertex p- of partition p
PZ(usize,usize), //The vertex corresponding to x_(partition p, zone z)
N(usize), //The vertex corresponding to node n
Sink
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum Vertex {
Source,
Pup(usize), //The vertex p+ of partition p
Pdown(usize), //The vertex p- of partition p
PZ(usize, usize), //The vertex corresponding to x_(partition p, zone z)
N(usize), //The vertex corresponding to node n
Sink,
}
//Edge data structure for the flow algorithm.
//The graph is stored as an adjacency list
#[derive(Clone, Copy, Debug)]
pub struct FlowEdge {
cap: u32, //flow maximal capacity of the edge
flow: i32, //flow value on the edge
dest: usize, //destination vertex id
rev: usize, //index of the reversed edge (v, self) in the edge list of vertex v
cap: u32, //flow maximal capacity of the edge
flow: i32, //flow value on the edge
dest: usize, //destination vertex id
rev: usize, //index of the reversed edge (v, self) in the edge list of vertex v
}
//Edge data structure for the detection of negative cycles.
//The graph is stored as a list of edges (u,v).
#[derive(Clone, Copy, Debug)]
pub struct WeightedEdge {
w: i32, //weight of the edge
w: i32, //weight of the edge
dest: usize,
}
@ -47,375 +45,377 @@ impl Edge for WeightedEdge {}
//Struct for the graph structure. We do encapsulation here to be able to both
//provide user friendly Vertex enum to address vertices, and to use usize indices
//and Vec instead of HashMap in the graph algorithm to optimize execution speed.
pub struct Graph<E : Edge>{
vertextoid : HashMap<Vertex , usize>,
idtovertex : Vec<Vertex>,
graph : Vec< Vec<E> >
pub struct Graph<E: Edge> {
vertextoid: HashMap<Vertex, usize>,
idtovertex: Vec<Vertex>,
graph: Vec<Vec<E>>,
}
pub type CostFunction = HashMap<(Vertex,Vertex), i32>;
pub type CostFunction = HashMap<(Vertex, Vertex), i32>;
impl<E : Edge> Graph<E>{
pub fn new(vertices : &[Vertex]) -> Self {
let mut map = HashMap::<Vertex, usize>::new();
for (i, vert) in vertices.iter().enumerate(){
map.insert(*vert , i);
}
Graph::<E> {
vertextoid : map,
idtovertex: vertices.to_vec(),
graph : vec![Vec::< E >::new(); vertices.len() ]
}
}
impl<E: Edge> Graph<E> {
pub fn new(vertices: &[Vertex]) -> Self {
let mut map = HashMap::<Vertex, usize>::new();
for (i, vert) in vertices.iter().enumerate() {
map.insert(*vert, i);
}
Graph::<E> {
vertextoid: map,
idtovertex: vertices.to_vec(),
graph: vec![Vec::<E>::new(); vertices.len()],
}
}
}
impl Graph<FlowEdge>{
//This function adds a directed edge to the graph with capacity c, and the
//corresponding reversed edge with capacity 0.
pub fn add_edge(&mut self, u: Vertex, v:Vertex, c: u32) -> Result<(), String>{
if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idu = self.vertextoid[&u];
let idv = self.vertextoid[&v];
let rev_u = self.graph[idu].len();
let rev_v = self.graph[idv].len();
self.graph[idu].push( FlowEdge{cap: c , dest: idv , flow: 0, rev : rev_v} );
self.graph[idv].push( FlowEdge{cap: 0 , dest: idu , flow: 0, rev : rev_u} );
Ok(())
}
//This function returns the list of vertices that receive a positive flow from
//vertex v.
pub fn get_positive_flow_from(&self , v:Vertex) -> Result< Vec<Vertex> , String>{
if !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idv = self.vertextoid[&v];
let mut result = Vec::<Vertex>::new();
for edge in self.graph[idv].iter() {
if edge.flow > 0 {
result.push(self.idtovertex[edge.dest]);
}
}
Ok(result)
}
//This function returns the value of the flow incoming to v.
pub fn get_inflow(&self , v:Vertex) -> Result< i32 , String>{
if !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idv = self.vertextoid[&v];
let mut result = 0;
for edge in self.graph[idv].iter() {
result += max(0,self.graph[edge.dest][edge.rev].flow);
}
Ok(result)
}
impl Graph<FlowEdge> {
//This function adds a directed edge to the graph with capacity c, and the
//corresponding reversed edge with capacity 0.
pub fn add_edge(&mut self, u: Vertex, v: Vertex, c: u32) -> Result<(), String> {
if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idu = self.vertextoid[&u];
let idv = self.vertextoid[&v];
let rev_u = self.graph[idu].len();
let rev_v = self.graph[idv].len();
self.graph[idu].push(FlowEdge {
cap: c,
dest: idv,
flow: 0,
rev: rev_v,
});
self.graph[idv].push(FlowEdge {
cap: 0,
dest: idu,
flow: 0,
rev: rev_u,
});
Ok(())
}
//This function returns the value of the flow outgoing from v.
pub fn get_outflow(&self , v:Vertex) -> Result< i32 , String>{
if !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idv = self.vertextoid[&v];
let mut result = 0;
for edge in self.graph[idv].iter() {
result += max(0,edge.flow);
}
Ok(result)
}
//This function returns the list of vertices that receive a positive flow from
//vertex v.
pub fn get_positive_flow_from(&self, v: Vertex) -> Result<Vec<Vertex>, String> {
if !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idv = self.vertextoid[&v];
let mut result = Vec::<Vertex>::new();
for edge in self.graph[idv].iter() {
if edge.flow > 0 {
result.push(self.idtovertex[edge.dest]);
}
}
Ok(result)
}
//This function computes the flow total value by computing the outgoing flow
//from the source.
pub fn get_flow_value(&mut self) -> Result<i32, String> {
self.get_outflow(Vertex::Source)
}
//This function returns the value of the flow incoming to v.
pub fn get_inflow(&self, v: Vertex) -> Result<i32, String> {
if !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idv = self.vertextoid[&v];
let mut result = 0;
for edge in self.graph[idv].iter() {
result += max(0, self.graph[edge.dest][edge.rev].flow);
}
Ok(result)
}
//This function shuffles the order of the edge lists. It keeps the ids of the
//reversed edges consistent.
fn shuffle_edges(&mut self) {
let mut rng = rand::thread_rng();
for i in 0..self.graph.len() {
self.graph[i].shuffle(&mut rng);
//We need to update the ids of the reverse edges.
for j in 0..self.graph[i].len() {
let target_v = self.graph[i][j].dest;
let target_rev = self.graph[i][j].rev;
self.graph[target_v][target_rev].rev = j;
}
}
}
//This function returns the value of the flow outgoing from v.
pub fn get_outflow(&self, v: Vertex) -> Result<i32, String> {
if !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idv = self.vertextoid[&v];
let mut result = 0;
for edge in self.graph[idv].iter() {
result += max(0, edge.flow);
}
Ok(result)
}
//Computes an upper bound of the flow n the graph
pub fn flow_upper_bound(&self) -> u32{
let idsource = self.vertextoid[&Vertex::Source];
let mut flow_upper_bound = 0;
for edge in self.graph[idsource].iter(){
flow_upper_bound += edge.cap;
}
flow_upper_bound
}
//This function computes the maximal flow using Dinic's algorithm. It starts with
//the flow values already present in the graph. So it is possible to add some edge to
//the graph, compute a flow, add other edges, update the flow.
pub fn compute_maximal_flow(&mut self) -> Result<(), String> {
if !self.vertextoid.contains_key(&Vertex::Source) {
return Err("The graph does not contain a source.".to_string());
}
if !self.vertextoid.contains_key(&Vertex::Sink) {
return Err("The graph does not contain a sink.".to_string());
}
//This function computes the flow total value by computing the outgoing flow
//from the source.
pub fn get_flow_value(&mut self) -> Result<i32, String> {
self.get_outflow(Vertex::Source)
}
let idsource = self.vertextoid[&Vertex::Source];
let idsink = self.vertextoid[&Vertex::Sink];
let nb_vertices = self.graph.len();
//This function shuffles the order of the edge lists. It keeps the ids of the
//reversed edges consistent.
fn shuffle_edges(&mut self) {
let mut rng = rand::thread_rng();
for i in 0..self.graph.len() {
self.graph[i].shuffle(&mut rng);
//We need to update the ids of the reverse edges.
for j in 0..self.graph[i].len() {
let target_v = self.graph[i][j].dest;
let target_rev = self.graph[i][j].rev;
self.graph[target_v][target_rev].rev = j;
}
}
}
let flow_upper_bound = self.flow_upper_bound();
//To ensure the dispersion of the associations generated by the
//assignation, we shuffle the neighbours of the nodes. Hence,
//the vertices do not consider their neighbours in the same order.
self.shuffle_edges();
//We run Dinic's max flow algorithm
loop {
//We build the level array from Dinic's algorithm.
let mut level = vec![None; nb_vertices];
//Computes an upper bound of the flow n the graph
pub fn flow_upper_bound(&self) -> u32 {
let idsource = self.vertextoid[&Vertex::Source];
let mut flow_upper_bound = 0;
for edge in self.graph[idsource].iter() {
flow_upper_bound += edge.cap;
}
flow_upper_bound
}
let mut fifo = VecDeque::new();
fifo.push_back((idsource, 0));
while !fifo.is_empty() {
if let Some((id, lvl)) = fifo.pop_front() {
if level[id] == None { //it means id has not yet been reached
level[id] = Some(lvl);
for edge in self.graph[id].iter() {
if edge.cap as i32 - edge.flow > 0 {
fifo.push_back((edge.dest, lvl + 1));
}
}
}
}
}
if level[idsink] == None {
//There is no residual flow
break;
}
//Now we run DFS respecting the level array
let mut next_nbd = vec![0; nb_vertices];
let mut lifo = VecDeque::new();
//This function computes the maximal flow using Dinic's algorithm. It starts with
//the flow values already present in the graph. So it is possible to add some edge to
//the graph, compute a flow, add other edges, update the flow.
pub fn compute_maximal_flow(&mut self) -> Result<(), String> {
if !self.vertextoid.contains_key(&Vertex::Source) {
return Err("The graph does not contain a source.".to_string());
}
if !self.vertextoid.contains_key(&Vertex::Sink) {
return Err("The graph does not contain a sink.".to_string());
}
lifo.push_back((idsource, flow_upper_bound));
let idsource = self.vertextoid[&Vertex::Source];
let idsink = self.vertextoid[&Vertex::Sink];
while let Some((id_tmp, f_tmp)) = lifo.back() {
let id = *id_tmp;
let f = *f_tmp;
if id == idsink {
//The DFS reached the sink, we can add a
//residual flow.
lifo.pop_back();
while let Some((id, _)) = lifo.pop_back() {
let nbd = next_nbd[id];
self.graph[id][nbd].flow += f as i32;
let id_rev = self.graph[id][nbd].dest;
let nbd_rev = self.graph[id][nbd].rev;
self.graph[id_rev][nbd_rev].flow -= f as i32;
}
lifo.push_back((idsource, flow_upper_bound));
continue;
}
//else we did not reach the sink
let nbd = next_nbd[id];
if nbd >= self.graph[id].len() {
//There is nothing to explore from id anymore
lifo.pop_back();
if let Some((parent, _)) = lifo.back() {
next_nbd[*parent] += 1;
}
continue;
}
//else we can try to send flow from id to its nbd
let new_flow = min(f as i32, self.graph[id][nbd].cap as i32 - self.graph[id][nbd].flow) as u32;
if new_flow == 0 {
next_nbd[id] += 1;
continue;
}
if let (Some(lvldest), Some(lvlid)) =
(level[self.graph[id][nbd].dest], level[id]){
if lvldest <= lvlid {
//We cannot send flow to nbd.
next_nbd[id] += 1;
continue;
}
}
//otherwise, we send flow to nbd.
lifo.push_back((self.graph[id][nbd].dest, new_flow));
}
}
Ok(())
}
let nb_vertices = self.graph.len();
//This function takes a flow, and a cost function on the edges, and tries to find an
// equivalent flow with a better cost, by finding improving overflow cycles. It uses
// as subroutine the Bellman Ford algorithm run up to path_length.
// We assume that the cost of edge (u,v) is the opposite of the cost of (v,u), and only
// one needs to be present in the cost function.
pub fn optimize_flow_with_cost(&mut self , cost: &CostFunction, path_length: usize )
-> Result<(),String>{
//We build the weighted graph g where we will look for negative cycle
let mut gf = self.build_cost_graph(cost)?;
let mut cycles = gf.list_negative_cycles(path_length);
while !cycles.is_empty() {
//we enumerate negative cycles
for c in cycles.iter(){
for i in 0..c.len(){
//We add one flow unit to the edge (u,v) of cycle c
let idu = self.vertextoid[&c[i]];
let idv = self.vertextoid[&c[(i+1)%c.len()]];
for j in 0..self.graph[idu].len(){
//since idu appears at most once in the cycles, we enumerate every
//edge at most once.
let edge = self.graph[idu][j];
if edge.dest == idv {
self.graph[idu][j].flow += 1;
self.graph[idv][edge.rev].flow -=1;
break;
}
}
}
}
let flow_upper_bound = self.flow_upper_bound();
gf = self.build_cost_graph(cost)?;
cycles = gf.list_negative_cycles(path_length);
}
Ok(())
}
//To ensure the dispersion of the associations generated by the
//assignation, we shuffle the neighbours of the nodes. Hence,
//the vertices do not consider their neighbours in the same order.
self.shuffle_edges();
//Construct the weighted graph G_f from the flow and the cost function
fn build_cost_graph(&self , cost: &CostFunction) -> Result<Graph<WeightedEdge>,String>{
let mut g = Graph::<WeightedEdge>::new(&self.idtovertex);
let nb_vertices = self.idtovertex.len();
for i in 0..nb_vertices {
for edge in self.graph[i].iter() {
if edge.cap as i32 -edge.flow > 0 {
//It is possible to send overflow through this edge
let u = self.idtovertex[i];
let v = self.idtovertex[edge.dest];
if cost.contains_key(&(u,v)) {
g.add_edge(u,v, cost[&(u,v)])?;
}
else if cost.contains_key(&(v,u)) {
g.add_edge(u,v, -cost[&(v,u)])?;
}
else{
g.add_edge(u,v, 0)?;
}
}
}
}
Ok(g)
//We run Dinic's max flow algorithm
loop {
//We build the level array from Dinic's algorithm.
let mut level = vec![None; nb_vertices];
}
let mut fifo = VecDeque::new();
fifo.push_back((idsource, 0));
while !fifo.is_empty() {
if let Some((id, lvl)) = fifo.pop_front() {
if level[id] == None {
//it means id has not yet been reached
level[id] = Some(lvl);
for edge in self.graph[id].iter() {
if edge.cap as i32 - edge.flow > 0 {
fifo.push_back((edge.dest, lvl + 1));
}
}
}
}
}
if level[idsink] == None {
//There is no residual flow
break;
}
//Now we run DFS respecting the level array
let mut next_nbd = vec![0; nb_vertices];
let mut lifo = VecDeque::new();
lifo.push_back((idsource, flow_upper_bound));
while let Some((id_tmp, f_tmp)) = lifo.back() {
let id = *id_tmp;
let f = *f_tmp;
if id == idsink {
//The DFS reached the sink, we can add a
//residual flow.
lifo.pop_back();
while let Some((id, _)) = lifo.pop_back() {
let nbd = next_nbd[id];
self.graph[id][nbd].flow += f as i32;
let id_rev = self.graph[id][nbd].dest;
let nbd_rev = self.graph[id][nbd].rev;
self.graph[id_rev][nbd_rev].flow -= f as i32;
}
lifo.push_back((idsource, flow_upper_bound));
continue;
}
//else we did not reach the sink
let nbd = next_nbd[id];
if nbd >= self.graph[id].len() {
//There is nothing to explore from id anymore
lifo.pop_back();
if let Some((parent, _)) = lifo.back() {
next_nbd[*parent] += 1;
}
continue;
}
//else we can try to send flow from id to its nbd
let new_flow = min(
f as i32,
self.graph[id][nbd].cap as i32 - self.graph[id][nbd].flow,
) as u32;
if new_flow == 0 {
next_nbd[id] += 1;
continue;
}
if let (Some(lvldest), Some(lvlid)) = (level[self.graph[id][nbd].dest], level[id]) {
if lvldest <= lvlid {
//We cannot send flow to nbd.
next_nbd[id] += 1;
continue;
}
}
//otherwise, we send flow to nbd.
lifo.push_back((self.graph[id][nbd].dest, new_flow));
}
}
Ok(())
}
//This function takes a flow, and a cost function on the edges, and tries to find an
// equivalent flow with a better cost, by finding improving overflow cycles. It uses
// as subroutine the Bellman Ford algorithm run up to path_length.
// We assume that the cost of edge (u,v) is the opposite of the cost of (v,u), and only
// one needs to be present in the cost function.
pub fn optimize_flow_with_cost(
&mut self,
cost: &CostFunction,
path_length: usize,
) -> Result<(), String> {
//We build the weighted graph g where we will look for negative cycle
let mut gf = self.build_cost_graph(cost)?;
let mut cycles = gf.list_negative_cycles(path_length);
while !cycles.is_empty() {
//we enumerate negative cycles
for c in cycles.iter() {
for i in 0..c.len() {
//We add one flow unit to the edge (u,v) of cycle c
let idu = self.vertextoid[&c[i]];
let idv = self.vertextoid[&c[(i + 1) % c.len()]];
for j in 0..self.graph[idu].len() {
//since idu appears at most once in the cycles, we enumerate every
//edge at most once.
let edge = self.graph[idu][j];
if edge.dest == idv {
self.graph[idu][j].flow += 1;
self.graph[idv][edge.rev].flow -= 1;
break;
}
}
}
}
gf = self.build_cost_graph(cost)?;
cycles = gf.list_negative_cycles(path_length);
}
Ok(())
}
//Construct the weighted graph G_f from the flow and the cost function
fn build_cost_graph(&self, cost: &CostFunction) -> Result<Graph<WeightedEdge>, String> {
let mut g = Graph::<WeightedEdge>::new(&self.idtovertex);
let nb_vertices = self.idtovertex.len();
for i in 0..nb_vertices {
for edge in self.graph[i].iter() {
if edge.cap as i32 - edge.flow > 0 {
//It is possible to send overflow through this edge
let u = self.idtovertex[i];
let v = self.idtovertex[edge.dest];
if cost.contains_key(&(u, v)) {
g.add_edge(u, v, cost[&(u, v)])?;
} else if cost.contains_key(&(v, u)) {
g.add_edge(u, v, -cost[&(v, u)])?;
} else {
g.add_edge(u, v, 0)?;
}
}
}
}
Ok(g)
}
}
impl Graph<WeightedEdge>{
//This function adds a single directed weighted edge to the graph.
pub fn add_edge(&mut self, u: Vertex, v:Vertex, w: i32) -> Result<(), String>{
if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idu = self.vertextoid[&u];
let idv = self.vertextoid[&v];
self.graph[idu].push( WeightedEdge{ w , dest: idv} );
Ok(())
}
impl Graph<WeightedEdge> {
//This function adds a single directed weighted edge to the graph.
pub fn add_edge(&mut self, u: Vertex, v: Vertex, w: i32) -> Result<(), String> {
if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
return Err("The graph does not contain the provided vertex.".to_string());
}
let idu = self.vertextoid[&u];
let idv = self.vertextoid[&v];
self.graph[idu].push(WeightedEdge { w, dest: idv });
Ok(())
}
//This function lists the negative cycles it manages to find after path_length
//iterations of the main loop of the Bellman-Ford algorithm. For the classical
//algorithm, path_length needs to be equal to the number of vertices. However,
//for particular graph structures like our case, the algorithm is still correct
//when path_length is the length of the longest possible simple path.
//See the formal description of the algorithm for more details.
fn list_negative_cycles(&self, path_length: usize) -> Vec< Vec<Vertex> > {
let nb_vertices = self.graph.len();
//We start with every vertex at distance 0 of some imaginary extra -1 vertex.
let mut distance = vec![0 ; nb_vertices];
//The prev vector collects for every vertex from where does the shortest path come
let mut prev = vec![None; nb_vertices];
//This function lists the negative cycles it manages to find after path_length
//iterations of the main loop of the Bellman-Ford algorithm. For the classical
//algorithm, path_length needs to be equal to the number of vertices. However,
//for particular graph structures like our case, the algorithm is still correct
//when path_length is the length of the longest possible simple path.
//See the formal description of the algorithm for more details.
fn list_negative_cycles(&self, path_length: usize) -> Vec<Vec<Vertex>> {
let nb_vertices = self.graph.len();
for _ in 0..path_length +1 {
for id in 0..nb_vertices{
for e in self.graph[id].iter(){
if distance[id] + e.w < distance[e.dest] {
distance[e.dest] = distance[id] + e.w;
prev[e.dest] = Some(id);
}
}
}
}
//We start with every vertex at distance 0 of some imaginary extra -1 vertex.
let mut distance = vec![0; nb_vertices];
//The prev vector collects for every vertex from where does the shortest path come
let mut prev = vec![None; nb_vertices];
for _ in 0..path_length + 1 {
for id in 0..nb_vertices {
for e in self.graph[id].iter() {
if distance[id] + e.w < distance[e.dest] {
distance[e.dest] = distance[id] + e.w;
prev[e.dest] = Some(id);
}
}
}
}
//If self.graph contains a negative cycle, then at this point the graph described
//by prev (which is a directed 1-forest/functional graph)
//must contain a cycle. We list the cycles of prev.
let cycles_prev = cycles_of_1_forest(&prev);
//Remark that the cycle in prev is in the reverse order compared to the cycle
//in the graph. Thus the .rev().
return cycles_prev.iter().map(|cycle| cycle.iter().rev().map(
|id| self.idtovertex[*id]
).collect() ).collect();
}
//If self.graph contains a negative cycle, then at this point the graph described
//by prev (which is a directed 1-forest/functional graph)
//must contain a cycle. We list the cycles of prev.
let cycles_prev = cycles_of_1_forest(&prev);
//Remark that the cycle in prev is in the reverse order compared to the cycle
//in the graph. Thus the .rev().
return cycles_prev
.iter()
.map(|cycle| cycle.iter().rev().map(|id| self.idtovertex[*id]).collect())
.collect();
}
}
//This function returns the list of cycles of a directed 1 forest. It does not
//check for the consistency of the input.
fn cycles_of_1_forest(forest: &[Option<usize>]) -> Vec<Vec<usize>> {
let mut cycles = Vec::<Vec::<usize>>::new();
let mut time_of_discovery = vec![None; forest.len()];
fn cycles_of_1_forest(forest: &[Option<usize>]) -> Vec<Vec<usize>> {
let mut cycles = Vec::<Vec<usize>>::new();
let mut time_of_discovery = vec![None; forest.len()];
for t in 0..forest.len(){
let mut id = t;
//while we are on a valid undiscovered node
while time_of_discovery[id] == None {
time_of_discovery[id] = Some(t);
if let Some(i) = forest[id] {
id = i;
}
else{
break;
}
}
if forest[id] != None && time_of_discovery[id] == Some(t) {
//We discovered an id that we explored at this iteration t.
//It means we are on a cycle
let mut cy = vec![id; 1];
let mut id2 = id;
while let Some(id_next) = forest[id2] {
id2 = id_next;
if id2 != id {
cy.push(id2);
}
else {
break;
}
}
cycles.push(cy);
}
}
cycles
for t in 0..forest.len() {
let mut id = t;
//while we are on a valid undiscovered node
while time_of_discovery[id] == None {
time_of_discovery[id] = Some(t);
if let Some(i) = forest[id] {
id = i;
} else {
break;
}
}
if forest[id] != None && time_of_discovery[id] == Some(t) {
//We discovered an id that we explored at this iteration t.
//It means we are on a cycle
let mut cy = vec![id; 1];
let mut id2 = id;
while let Some(id_next) = forest[id2] {
id2 = id_next;
if id2 != id {
cy.push(id2);
} else {
break;
}
}
cycles.push(cy);
}
}
cycles
}

1270
src/rpc/layout.rs
View file

File diff suppressed because it is too large Load diff

3
src/rpc/lib.rs
View file

@ -7,12 +7,11 @@ mod consul;
#[cfg(feature = "kubernetes-discovery")]
mod kubernetes;
pub mod layout;
pub mod graph_algo;
pub mod layout;
pub mod ring;
pub mod system;
mod metrics;
pub mod rpc_helper;

1
src/rpc/system.rs
View file

@ -565,7 +565,6 @@ impl System {
return Err(Error::Message(msg));
}
let update_ring = self.update_ring.lock().await;
let mut layout: ClusterLayout = self.ring.borrow().layout.clone();