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authorAlex Auvolat <alex@adnab.me>2022-05-01 09:57:05 +0200
committerAlex Auvolat <alex@adnab.me>2022-05-01 09:57:05 +0200
commit2aeaddd5e2e1911b084f6d49ccb2236b7fec31af (patch)
tree7bf19ac4ba213e46194d8a71872bdbcfbeab19d8
parentc1d1646c4d62300ec48503aa65623ee7e3df8685 (diff)
downloadgarage-2aeaddd5e2e1911b084f6d49ccb2236b7fec31af.tar.gz
garage-2aeaddd5e2e1911b084f6d49ccb2236b7fec31af.zip
Apply cargo fmt
-rw-r--r--src/rpc/layout.rs940
-rw-r--r--src/util/bipartite.rs694
2 files changed, 842 insertions, 792 deletions
diff --git a/src/rpc/layout.rs b/src/rpc/layout.rs
index afd7df17..ac31da72 100644
--- a/src/rpc/layout.rs
+++ b/src/rpc/layout.rs
@@ -1,12 +1,12 @@
+use std::cmp::min;
use std::cmp::Ordering;
-use std::cmp::{min};
-use std::collections::{HashMap};
+use std::collections::HashMap;
use serde::{Deserialize, Serialize};
+use garage_util::bipartite::*;
use garage_util::crdt::{AutoCrdt, Crdt, LwwMap};
use garage_util::data::*;
-use garage_util::bipartite::*;
use rand::prelude::SliceRandom;
@@ -168,454 +168,506 @@ impl ClusterLayout {
true
}
+ /// This function calculates a new partition-to-node assignation.
+ /// The computed assignation maximizes the capacity of a
+ /// partition (assuming all partitions have the same size).
+ /// Among such optimal assignation, it minimizes the distance to
+ /// the former assignation (if any) to minimize the amount of
+ /// data to be moved. A heuristic ensures node triplets
+ /// dispersion (in garage_util::bipartite::optimize_matching()).
+ pub fn calculate_partition_assignation(&mut self) -> bool {
+ //The nodes might have been updated, some might have been deleted.
+ //So we need to first update the list of nodes and retrieve the
+ //assignation.
+ let old_node_assignation = self.update_nodes_and_ring();
+
+ let (node_zone, _) = self.get_node_zone_capacity();
+
+ //We compute the optimal number of partition to assign to
+ //every node and zone.
+ if let Some((part_per_nod, part_per_zone)) = self.optimal_proportions() {
+ //We collect part_per_zone in a vec to not rely on the
+ //arbitrary order in which elements are iterated in
+ //Hashmap::iter()
+ let part_per_zone_vec = part_per_zone
+ .iter()
+ .map(|(x, y)| (x.clone(), *y))
+ .collect::<Vec<(String, usize)>>();
+ //We create an indexing of the zones
+ let mut zone_id = HashMap::<String, usize>::new();
+ for i in 0..part_per_zone_vec.len() {
+ zone_id.insert(part_per_zone_vec[i].0.clone(), i);
+ }
- /// This function calculates a new partition-to-node assignation.
- /// The computed assignation maximizes the capacity of a
- /// partition (assuming all partitions have the same size).
- /// Among such optimal assignation, it minimizes the distance to
- /// the former assignation (if any) to minimize the amount of
- /// data to be moved. A heuristic ensures node triplets
- /// dispersion (in garage_util::bipartite::optimize_matching()).
- pub fn calculate_partition_assignation(&mut self) -> bool {
-
- //The nodes might have been updated, some might have been deleted.
- //So we need to first update the list of nodes and retrieve the
- //assignation.
- let old_node_assignation = self.update_nodes_and_ring();
-
- let (node_zone, _) = self.get_node_zone_capacity();
-
- //We compute the optimal number of partition to assign to
- //every node and zone.
- if let Some((part_per_nod, part_per_zone)) = self.optimal_proportions(){
- //We collect part_per_zone in a vec to not rely on the
- //arbitrary order in which elements are iterated in
- //Hashmap::iter()
- let part_per_zone_vec = part_per_zone.iter()
- .map(|(x,y)| (x.clone(),*y))
- .collect::<Vec<(String,usize)>>();
- //We create an indexing of the zones
- let mut zone_id = HashMap::<String,usize>::new();
- for i in 0..part_per_zone_vec.len(){
- zone_id.insert(part_per_zone_vec[i].0.clone(), i);
- }
-
- //We compute a candidate for the new partition to zone
- //assignation.
- let nb_zones = part_per_zone.len();
- let nb_nodes = part_per_nod.len();
- let nb_partitions = 1<<PARTITION_BITS;
- let left_cap_vec = vec![self.replication_factor as u32 ; nb_partitions];
- let right_cap_vec = part_per_zone_vec.iter().map(|(_,y)| *y as u32)
- .collect();
- let mut zone_assignation =
- dinic_compute_matching(left_cap_vec, right_cap_vec);
-
-
- //We create the structure for the partition-to-node assignation.
- let mut node_assignation =
- vec![vec![None; self.replication_factor ];nb_partitions];
- //We will decrement part_per_nod to keep track of the number
- //of partitions that we still have to associate.
- let mut part_per_nod = part_per_nod.clone();
-
- //We minimize the distance to the former assignation(if any)
-
- //We get the id of the zones of the former assignation
- //(and the id no_zone if there is no node assignated)
- let no_zone = part_per_zone_vec.len();
- let old_zone_assignation : Vec<Vec<usize>> =
- old_node_assignation.iter().map(|x| x.iter().map(
- |id| match *id { Some(i) => zone_id[&node_zone[i]] ,
- None => no_zone }
- ).collect()).collect();
-
- //We minimize the distance to the former zone assignation
- zone_assignation = optimize_matching(
- &old_zone_assignation, &zone_assignation, nb_zones+1); //+1 for no_zone
-
- //We need to assign partitions to nodes in their zone
- //We first put the nodes assignation that can stay the same
- for i in 0..nb_partitions{
- for j in 0..self.replication_factor {
- if let Some(Some(former_node)) = old_node_assignation[i].iter().find(
- |x| if let Some(id) = x {
- zone_id[&node_zone[*id]] == zone_assignation[i][j]
- }
- else {false}
- )
- {
- if part_per_nod[*former_node] > 0 {
- node_assignation[i][j] = Some(*former_node);
- part_per_nod[*former_node] -= 1;
- }
- }
- }
- }
-
-
- //We complete the assignation of partitions to nodes
- let mut rng = rand::thread_rng();
- for i in 0..nb_partitions {
- for j in 0..self.replication_factor {
- if node_assignation[i][j] == None {
- let possible_nodes : Vec<usize> = (0..nb_nodes)
- .filter(
- |id| zone_id[&node_zone[*id]] == zone_assignation[i][j]
- && part_per_nod[*id] > 0).collect();
- assert!(possible_nodes.len()>0);
- //We randomly pick a node
- if let Some(nod) = possible_nodes.choose(&mut rng){
- node_assignation[i][j] = Some(*nod);
- part_per_nod[*nod] -= 1;
- }
- }
- }
- }
-
- //We write the assignation in the 1D table
- self.ring_assignation_data = Vec::<CompactNodeType>::new();
- for i in 0..nb_partitions{
- for j in 0..self.replication_factor {
- if let Some(id) = node_assignation[i][j] {
- self.ring_assignation_data.push(id as CompactNodeType);
- }
- else {assert!(false)}
- }
- }
-
- true
- }
- else { false }
- }
-
- /// The LwwMap of node roles might have changed. This function updates the node_id_vec
- /// and returns the assignation given by ring, with the new indices of the nodes, and
- /// None of the node is not present anymore.
- /// We work with the assumption that only this function and calculate_new_assignation
- /// do modify assignation_ring and node_id_vec.
- fn update_nodes_and_ring(&mut self) -> Vec<Vec<Option<usize>>> {
- let nb_partitions = 1usize<<PARTITION_BITS;
- let mut node_assignation =
- vec![vec![None; self.replication_factor ];nb_partitions];
- let rf = self.replication_factor;
- let ring = &self.ring_assignation_data;
-
- let new_node_id_vec : Vec::<Uuid> = self.roles.items().iter()
- .map(|(k, _, _)| *k)
- .collect();
-
- if ring.len() == rf*nb_partitions {
- for i in 0..nb_partitions {
- for j in 0..self.replication_factor {
- node_assignation[i][j] = new_node_id_vec.iter()
- .position(|id| *id == self.node_id_vec[ring[i*rf + j] as usize]);
- }
- }
- }
-
- self.node_id_vec = new_node_id_vec;
- self.ring_assignation_data = vec![];
- return node_assignation;
- }
-
- ///This function compute the number of partition to assign to
- ///every node and zone, so that every partition is replicated
- ///self.replication_factor times and the capacity of a partition
- ///is maximized.
- fn optimal_proportions(&mut self) -> Option<(Vec<usize>, HashMap<String, usize>)> {
-
- let mut zone_capacity :HashMap<String, u32>= HashMap::new();
-
- let (node_zone, node_capacity) = self.get_node_zone_capacity();
- let nb_nodes = self.node_id_vec.len();
-
- for i in 0..nb_nodes
- {
- if zone_capacity.contains_key(&node_zone[i]) {
- zone_capacity.insert(node_zone[i].clone(), zone_capacity[&node_zone[i]] + node_capacity[i]);
- }
- else{
- zone_capacity.insert(node_zone[i].clone(), node_capacity[i]);
- }
- }
-
- //Compute the optimal number of partitions per zone
- let sum_capacities: u32 =zone_capacity.values().sum();
-
- if sum_capacities <= 0 {
- println!("No storage capacity in the network.");
- return None;
- }
-
- let nb_partitions = 1<<PARTITION_BITS;
-
- //Initially we would like to use zones porportionally to
- //their capacity.
- //However, a large zone can be associated to at most
- //nb_partitions to ensure replication of the date.
- //So we take the min with nb_partitions:
- let mut part_per_zone : HashMap<String, usize> =
- zone_capacity.iter()
- .map(|(k, v)| (k.clone(), min(nb_partitions,
- (self.replication_factor*nb_partitions
- **v as usize)/sum_capacities as usize) ) ).collect();
-
- //The replication_factor-1 upper bounds the number of
- //part_per_zones that are greater than nb_partitions
- for _ in 1..self.replication_factor {
- //The number of partitions that are not assignated to
- //a zone that takes nb_partitions.
- let sum_capleft : u32 = zone_capacity.keys()
- .filter(| k | {part_per_zone[*k] < nb_partitions} )
- .map(|k| zone_capacity[k]).sum();
-
- //The number of replication of the data that we need
- //to ensure.
- let repl_left = self.replication_factor
- - part_per_zone.values()
- .filter(|x| {**x == nb_partitions})
- .count();
- if repl_left == 0 {
- break;
- }
-
- for k in zone_capacity.keys() {
- if part_per_zone[k] != nb_partitions
- {
- part_per_zone.insert(k.to_string() , min(nb_partitions,
- (nb_partitions*zone_capacity[k] as usize
- *repl_left)/sum_capleft as usize));
- }
- }
- }
-
- //Now we divide the zone's partition share proportionally
- //between their nodes.
-
- let mut part_per_nod : Vec<usize> = (0..nb_nodes).map(
- |i| (part_per_zone[&node_zone[i]]*node_capacity[i] as usize)/zone_capacity[&node_zone[i]] as usize
- )
- .collect();
-
- //We must update the part_per_zone to make it correspond to
- //part_per_nod (because of integer rounding)
- part_per_zone = part_per_zone.iter().map(|(k,_)|
- (k.clone(), 0))
- .collect();
- for i in 0..nb_nodes {
- part_per_zone.insert(
- node_zone[i].clone() ,
- part_per_zone[&node_zone[i]] + part_per_nod[i]);
- }
-
- //Because of integer rounding, the total sum of part_per_nod
- //might not be replication_factor*nb_partitions.
- // We need at most to add 1 to every non maximal value of
- // part_per_nod. The capacity of a partition will be bounded
- // by the minimal value of
- // node_capacity_vec[i]/part_per_nod[i]
- // so we try to maximize this minimal value, keeping the
- // part_per_zone capped
-
- let discrepancy : usize =
- nb_partitions*self.replication_factor
- - part_per_nod.iter().sum::<usize>();
-
- //We use a stupid O(N^2) algorithm. If the number of nodes
- //is actually expected to be high, one should optimize this.
-
- for _ in 0..discrepancy {
- if let Some(idmax) = (0..nb_nodes)
- .filter(|i| part_per_zone[&node_zone[*i]] < nb_partitions)
- .max_by( |i,j|
- (node_capacity[*i]*(part_per_nod[*j]+1) as u32)
- .cmp(&(node_capacity[*j]*(part_per_nod[*i]+1) as u32))
- )
- {
- part_per_nod[idmax] += 1;
- part_per_zone.insert(node_zone[idmax].clone(),part_per_zone[&node_zone[idmax]]+1);
- }
- }
-
- //We check the algorithm consistency
-
- let discrepancy : usize =
- nb_partitions*self.replication_factor
- - part_per_nod.iter().sum::<usize>();
- assert!(discrepancy == 0);
- assert!(if let Some(v) = part_per_zone.values().max()
- {*v <= nb_partitions} else {false} );
-
- Some((part_per_nod, part_per_zone))
- }
-
-
- //Returns vectors of zone and capacity; indexed by the same (temporary)
- //indices as node_id_vec.
- fn get_node_zone_capacity(& self) -> (Vec<String> , Vec<u32>) {
-
- let node_zone = self.node_id_vec.iter().map(
- |id_nod| match self.node_role(id_nod) {
- Some(NodeRole{zone,capacity:_,tags:_}) => zone.clone() ,
- _ => "".to_string()
- }
- ).collect();
-
- let node_capacity = self.node_id_vec.iter().map(
- |id_nod| match self.node_role(id_nod) {
- Some(NodeRole{zone:_,capacity,tags:_}) =>
- if let Some(c)=capacity
- {*c}
- else {0},
- _ => 0
- }
- ).collect();
-
- (node_zone,node_capacity)
- }
+ //We compute a candidate for the new partition to zone
+ //assignation.
+ let nb_zones = part_per_zone.len();
+ let nb_nodes = part_per_nod.len();
+ let nb_partitions = 1 << PARTITION_BITS;
+ let left_cap_vec = vec![self.replication_factor as u32; nb_partitions];
+ let right_cap_vec = part_per_zone_vec.iter().map(|(_, y)| *y as u32).collect();
+ let mut zone_assignation = dinic_compute_matching(left_cap_vec, right_cap_vec);
+
+ //We create the structure for the partition-to-node assignation.
+ let mut node_assignation = vec![vec![None; self.replication_factor]; nb_partitions];
+ //We will decrement part_per_nod to keep track of the number
+ //of partitions that we still have to associate.
+ let mut part_per_nod = part_per_nod.clone();
+
+ //We minimize the distance to the former assignation(if any)
+
+ //We get the id of the zones of the former assignation
+ //(and the id no_zone if there is no node assignated)
+ let no_zone = part_per_zone_vec.len();
+ let old_zone_assignation: Vec<Vec<usize>> = old_node_assignation
+ .iter()
+ .map(|x| {
+ x.iter()
+ .map(|id| match *id {
+ Some(i) => zone_id[&node_zone[i]],
+ None => no_zone,
+ })
+ .collect()
+ })
+ .collect();
+
+ //We minimize the distance to the former zone assignation
+ zone_assignation =
+ optimize_matching(&old_zone_assignation, &zone_assignation, nb_zones + 1); //+1 for no_zone
+
+ //We need to assign partitions to nodes in their zone
+ //We first put the nodes assignation that can stay the same
+ for i in 0..nb_partitions {
+ for j in 0..self.replication_factor {
+ if let Some(Some(former_node)) = old_node_assignation[i].iter().find(|x| {
+ if let Some(id) = x {
+ zone_id[&node_zone[*id]] == zone_assignation[i][j]
+ } else {
+ false
+ }
+ }) {
+ if part_per_nod[*former_node] > 0 {
+ node_assignation[i][j] = Some(*former_node);
+ part_per_nod[*former_node] -= 1;
+ }
+ }
+ }
+ }
-}
+ //We complete the assignation of partitions to nodes
+ let mut rng = rand::thread_rng();
+ for i in 0..nb_partitions {
+ for j in 0..self.replication_factor {
+ if node_assignation[i][j] == None {
+ let possible_nodes: Vec<usize> = (0..nb_nodes)
+ .filter(|id| {
+ zone_id[&node_zone[*id]] == zone_assignation[i][j]
+ && part_per_nod[*id] > 0
+ })
+ .collect();
+ assert!(possible_nodes.len() > 0);
+ //We randomly pick a node
+ if let Some(nod) = possible_nodes.choose(&mut rng) {
+ node_assignation[i][j] = Some(*nod);
+ part_per_nod[*nod] -= 1;
+ }
+ }
+ }
+ }
+
+ //We write the assignation in the 1D table
+ self.ring_assignation_data = Vec::<CompactNodeType>::new();
+ for i in 0..nb_partitions {
+ for j in 0..self.replication_factor {
+ if let Some(id) = node_assignation[i][j] {
+ self.ring_assignation_data.push(id as CompactNodeType);
+ } else {
+ assert!(false)
+ }
+ }
+ }
+ true
+ } else {
+ false
+ }
+ }
+
+ /// The LwwMap of node roles might have changed. This function updates the node_id_vec
+ /// and returns the assignation given by ring, with the new indices of the nodes, and
+ /// None of the node is not present anymore.
+ /// We work with the assumption that only this function and calculate_new_assignation
+ /// do modify assignation_ring and node_id_vec.
+ fn update_nodes_and_ring(&mut self) -> Vec<Vec<Option<usize>>> {
+ let nb_partitions = 1usize << PARTITION_BITS;
+ let mut node_assignation = vec![vec![None; self.replication_factor]; nb_partitions];
+ let rf = self.replication_factor;
+ let ring = &self.ring_assignation_data;
+
+ let new_node_id_vec: Vec<Uuid> = self.roles.items().iter().map(|(k, _, _)| *k).collect();
+
+ if ring.len() == rf * nb_partitions {
+ for i in 0..nb_partitions {
+ for j in 0..self.replication_factor {
+ node_assignation[i][j] = new_node_id_vec
+ .iter()
+ .position(|id| *id == self.node_id_vec[ring[i * rf + j] as usize]);
+ }
+ }
+ }
+
+ self.node_id_vec = new_node_id_vec;
+ self.ring_assignation_data = vec![];
+ return node_assignation;
+ }
+
+ ///This function compute the number of partition to assign to
+ ///every node and zone, so that every partition is replicated
+ ///self.replication_factor times and the capacity of a partition
+ ///is maximized.
+ fn optimal_proportions(&mut self) -> Option<(Vec<usize>, HashMap<String, usize>)> {
+ let mut zone_capacity: HashMap<String, u32> = HashMap::new();
+
+ let (node_zone, node_capacity) = self.get_node_zone_capacity();
+ let nb_nodes = self.node_id_vec.len();
+
+ for i in 0..nb_nodes {
+ if zone_capacity.contains_key(&node_zone[i]) {
+ zone_capacity.insert(
+ node_zone[i].clone(),
+ zone_capacity[&node_zone[i]] + node_capacity[i],
+ );
+ } else {
+ zone_capacity.insert(node_zone[i].clone(), node_capacity[i]);
+ }
+ }
+
+ //Compute the optimal number of partitions per zone
+ let sum_capacities: u32 = zone_capacity.values().sum();
+
+ if sum_capacities <= 0 {
+ println!("No storage capacity in the network.");
+ return None;
+ }
+
+ let nb_partitions = 1 << PARTITION_BITS;
+
+ //Initially we would like to use zones porportionally to
+ //their capacity.
+ //However, a large zone can be associated to at most
+ //nb_partitions to ensure replication of the date.
+ //So we take the min with nb_partitions:
+ let mut part_per_zone: HashMap<String, usize> = zone_capacity
+ .iter()
+ .map(|(k, v)| {
+ (
+ k.clone(),
+ min(
+ nb_partitions,
+ (self.replication_factor * nb_partitions * *v as usize)
+ / sum_capacities as usize,
+ ),
+ )
+ })
+ .collect();
+
+ //The replication_factor-1 upper bounds the number of
+ //part_per_zones that are greater than nb_partitions
+ for _ in 1..self.replication_factor {
+ //The number of partitions that are not assignated to
+ //a zone that takes nb_partitions.
+ let sum_capleft: u32 = zone_capacity
+ .keys()
+ .filter(|k| part_per_zone[*k] < nb_partitions)
+ .map(|k| zone_capacity[k])
+ .sum();
+
+ //The number of replication of the data that we need
+ //to ensure.
+ let repl_left = self.replication_factor
+ - part_per_zone
+ .values()
+ .filter(|x| **x == nb_partitions)
+ .count();
+ if repl_left == 0 {
+ break;
+ }
+
+ for k in zone_capacity.keys() {
+ if part_per_zone[k] != nb_partitions {
+ part_per_zone.insert(
+ k.to_string(),
+ min(
+ nb_partitions,
+ (nb_partitions * zone_capacity[k] as usize * repl_left)
+ / sum_capleft as usize,
+ ),
+ );
+ }
+ }
+ }
+
+ //Now we divide the zone's partition share proportionally
+ //between their nodes.
+
+ let mut part_per_nod: Vec<usize> = (0..nb_nodes)
+ .map(|i| {
+ (part_per_zone[&node_zone[i]] * node_capacity[i] as usize)
+ / zone_capacity[&node_zone[i]] as usize
+ })
+ .collect();
+
+ //We must update the part_per_zone to make it correspond to
+ //part_per_nod (because of integer rounding)
+ part_per_zone = part_per_zone.iter().map(|(k, _)| (k.clone(), 0)).collect();
+ for i in 0..nb_nodes {
+ part_per_zone.insert(
+ node_zone[i].clone(),
+ part_per_zone[&node_zone[i]] + part_per_nod[i],
+ );
+ }
+
+ //Because of integer rounding, the total sum of part_per_nod
+ //might not be replication_factor*nb_partitions.
+ // We need at most to add 1 to every non maximal value of
+ // part_per_nod. The capacity of a partition will be bounded
+ // by the minimal value of
+ // node_capacity_vec[i]/part_per_nod[i]
+ // so we try to maximize this minimal value, keeping the
+ // part_per_zone capped
+
+ let discrepancy: usize =
+ nb_partitions * self.replication_factor - part_per_nod.iter().sum::<usize>();
+
+ //We use a stupid O(N^2) algorithm. If the number of nodes
+ //is actually expected to be high, one should optimize this.
+
+ for _ in 0..discrepancy {
+ if let Some(idmax) = (0..nb_nodes)
+ .filter(|i| part_per_zone[&node_zone[*i]] < nb_partitions)
+ .max_by(|i, j| {
+ (node_capacity[*i] * (part_per_nod[*j] + 1) as u32)
+ .cmp(&(node_capacity[*j] * (part_per_nod[*i] + 1) as u32))
+ }) {
+ part_per_nod[idmax] += 1;
+ part_per_zone.insert(
+ node_zone[idmax].clone(),
+ part_per_zone[&node_zone[idmax]] + 1,
+ );
+ }
+ }
+ //We check the algorithm consistency
+
+ let discrepancy: usize =
+ nb_partitions * self.replication_factor - part_per_nod.iter().sum::<usize>();
+ assert!(discrepancy == 0);
+ assert!(if let Some(v) = part_per_zone.values().max() {
+ *v <= nb_partitions
+ } else {
+ false
+ });
+
+ Some((part_per_nod, part_per_zone))
+ }
+
+ //Returns vectors of zone and capacity; indexed by the same (temporary)
+ //indices as node_id_vec.
+ fn get_node_zone_capacity(&self) -> (Vec<String>, Vec<u32>) {
+ let node_zone = self
+ .node_id_vec
+ .iter()
+ .map(|id_nod| match self.node_role(id_nod) {
+ Some(NodeRole {
+ zone,
+ capacity: _,
+ tags: _,
+ }) => zone.clone(),
+ _ => "".to_string(),
+ })
+ .collect();
+
+ let node_capacity = self
+ .node_id_vec
+ .iter()
+ .map(|id_nod| match self.node_role(id_nod) {
+ Some(NodeRole {
+ zone: _,
+ capacity,
+ tags: _,
+ }) => {
+ if let Some(c) = capacity {
+ *c
+ } else {
+ 0
+ }
+ }
+ _ => 0,
+ })
+ .collect();
+
+ (node_zone, node_capacity)
+ }
+}
#[cfg(test)]
mod tests {
- use super::*;
- use itertools::Itertools;
-
- fn check_assignation(cl : &ClusterLayout) {
-
- //Check that input data has the right format
- let nb_partitions = 1usize<<PARTITION_BITS;
- assert!([1,2,3].contains(&cl.replication_factor));
- assert!(cl.ring_assignation_data.len() == nb_partitions*cl.replication_factor);
-
- let (node_zone, node_capacity) = cl.get_node_zone_capacity();
-
-
- //Check that is is a correct assignation with zone redundancy
- let rf = cl.replication_factor;
- for i in 0..nb_partitions{
- assert!( rf ==
- cl.ring_assignation_data[rf*i..rf*(i+1)].iter()
- .map(|nod| node_zone[*nod as usize].clone())
- .unique()
- .count() );
- }
-
- let nb_nodes = cl.node_id_vec.len();
- //Check optimality
- let node_nb_part =(0..nb_nodes).map(|i| cl.ring_assignation_data
- .iter()
- .filter(|x| **x==i as u8)
- .count())
- .collect::<Vec::<_>>();
-
- let zone_vec = node_zone.iter().unique().collect::<Vec::<_>>();
- let zone_nb_part = zone_vec.iter().map( |z| cl.ring_assignation_data.iter()
- .filter(|x| node_zone[**x as usize] == **z)
- .count()
- ).collect::<Vec::<_>>();
-
- //Check optimality of the zone assignation : would it be better for the
- //node_capacity/node_partitions ratio to change the assignation of a partition
-
- if let Some(idmin) = (0..nb_nodes).min_by(
- |i,j| (node_capacity[*i]*node_nb_part[*j] as u32)
- .cmp(&(node_capacity[*j]*node_nb_part[*i] as u32))
- ){
- if let Some(idnew) = (0..nb_nodes)
- .filter( |i| if let Some(p) = zone_vec.iter().position(|z| **z==node_zone[*i])
- {zone_nb_part[p] < nb_partitions }
- else { false })
- .max_by(
- |i,j| (node_capacity[*i]*(node_nb_part[*j]as u32+1))
- .cmp(&(node_capacity[*j]*(node_nb_part[*i] as u32+1)))
- ){
- assert!(node_capacity[idmin]*(node_nb_part[idnew] as u32+1) >=
- node_capacity[idnew]*node_nb_part[idmin] as u32);
- }
-
- }
-
- //In every zone, check optimality of the nod assignation
- for z in zone_vec {
- let node_of_z_iter = (0..nb_nodes).filter(|id| node_zone[*id] == *z );
- if let Some(idmin) = node_of_z_iter.clone().min_by(
- |i,j| (node_capacity[*i]*node_nb_part[*j] as u32)
- .cmp(&(node_capacity[*j]*node_nb_part[*i] as u32))
- ){
- if let Some(idnew) = node_of_z_iter.min_by(
- |i,j| (node_capacity[*i]*(node_nb_part[*j] as u32+1))
- .cmp(&(node_capacity[*j]*(node_nb_part[*i] as u32+1)))
- ){
- assert!(node_capacity[idmin]*(node_nb_part[idnew] as u32+1) >=
- node_capacity[idnew]*node_nb_part[idmin] as u32);
- }
- }
- }
-
- }
-
- fn update_layout(cl : &mut ClusterLayout, node_id_vec : &Vec<u8>,
- node_capacity_vec : &Vec<u32> , node_zone_vec : &Vec<String>) {
- for i in 0..node_id_vec.len(){
- if let Some(x) = FixedBytes32::try_from(&[i as u8;32]) {
- cl.node_id_vec.push(x);
- }
-
- let update = cl.roles.update_mutator(cl.node_id_vec[i] ,
- NodeRoleV(Some(NodeRole{
- zone : (node_zone_vec[i].to_string()),
- capacity : (Some(node_capacity_vec[i])),
- tags : (vec![])})));
- cl.roles.merge(&update);
- }
- }
-
- #[test]
- fn test_assignation() {
-
- let mut node_id_vec = vec![1,2,3];
- let mut node_capacity_vec = vec![4000,1000,2000];
- let mut node_zone_vec= vec!["A", "B", "C"].into_iter().map(|x| x.to_string()).collect();
-
- let mut cl = ClusterLayout {
- node_id_vec: vec![],
-
- roles : LwwMap::new(),
-
- replication_factor: 3,
- ring_assignation_data : vec![],
- version:0,
- staging: LwwMap::new(),
- staging_hash: sha256sum(&[1;32]),
- };
- update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
- cl.calculate_partition_assignation();
- check_assignation(&cl);
-
- node_id_vec = vec![1,2,3, 4, 5, 6, 7, 8, 9];
- node_capacity_vec = vec![4000,1000,1000, 3000, 1000, 1000, 2000, 10000, 2000];
- node_zone_vec= vec!["A", "B", "C", "C", "C", "B", "G", "H", "I"].into_iter().map(|x| x.to_string()).collect();
- update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
- cl.calculate_partition_assignation();
- check_assignation(&cl);
-
- node_capacity_vec = vec![4000,1000,2000, 7000, 1000, 1000, 2000, 10000, 2000];
- update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
- cl.calculate_partition_assignation();
- check_assignation(&cl);
-
-
- node_capacity_vec = vec![4000,4000,2000, 7000, 1000, 9000, 2000, 10, 2000];
- update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
- cl.calculate_partition_assignation();
- check_assignation(&cl);
-
- }
-}
+ use super::*;
+ use itertools::Itertools;
+
+ fn check_assignation(cl: &ClusterLayout) {
+ //Check that input data has the right format
+ let nb_partitions = 1usize << PARTITION_BITS;
+ assert!([1, 2, 3].contains(&cl.replication_factor));
+ assert!(cl.ring_assignation_data.len() == nb_partitions * cl.replication_factor);
+
+ let (node_zone, node_capacity) = cl.get_node_zone_capacity();
+
+ //Check that is is a correct assignation with zone redundancy
+ let rf = cl.replication_factor;
+ for i in 0..nb_partitions {
+ assert!(
+ rf == cl.ring_assignation_data[rf * i..rf * (i + 1)]
+ .iter()
+ .map(|nod| node_zone[*nod as usize].clone())
+ .unique()
+ .count()
+ );
+ }
+
+ let nb_nodes = cl.node_id_vec.len();
+ //Check optimality
+ let node_nb_part = (0..nb_nodes)
+ .map(|i| {
+ cl.ring_assignation_data
+ .iter()
+ .filter(|x| **x == i as u8)
+ .count()
+ })
+ .collect::<Vec<_>>();
+
+ let zone_vec = node_zone.iter().unique().collect::<Vec<_>>();
+ let zone_nb_part = zone_vec
+ .iter()
+ .map(|z| {
+ cl.ring_assignation_data
+ .iter()
+ .filter(|x| node_zone[**x as usize] == **z)
+ .count()
+ })
+ .collect::<Vec<_>>();
+
+ //Check optimality of the zone assignation : would it be better for the
+ //node_capacity/node_partitions ratio to change the assignation of a partition
+
+ if let Some(idmin) = (0..nb_nodes).min_by(|i, j| {
+ (node_capacity[*i] * node_nb_part[*j] as u32)
+ .cmp(&(node_capacity[*j] * node_nb_part[*i] as u32))
+ }) {
+ if let Some(idnew) = (0..nb_nodes)
+ .filter(|i| {
+ if let Some(p) = zone_vec.iter().position(|z| **z == node_zone[*i]) {
+ zone_nb_part[p] < nb_partitions
+ } else {
+ false
+ }
+ })
+ .max_by(|i, j| {
+ (node_capacity[*i] * (node_nb_part[*j] as u32 + 1))
+ .cmp(&(node_capacity[*j] * (node_nb_part[*i] as u32 + 1)))
+ }) {
+ assert!(
+ node_capacity[idmin] * (node_nb_part[idnew] as u32 + 1)
+ >= node_capacity[idnew] * node_nb_part[idmin] as u32
+ );
+ }
+ }
+
+ //In every zone, check optimality of the nod assignation
+ for z in zone_vec {
+ let node_of_z_iter = (0..nb_nodes).filter(|id| node_zone[*id] == *z);
+ if let Some(idmin) = node_of_z_iter.clone().min_by(|i, j| {
+ (node_capacity[*i] * node_nb_part[*j] as u32)
+ .cmp(&(node_capacity[*j] * node_nb_part[*i] as u32))
+ }) {
+ if let Some(idnew) = node_of_z_iter.min_by(|i, j| {
+ (node_capacity[*i] * (node_nb_part[*j] as u32 + 1))
+ .cmp(&(node_capacity[*j] * (node_nb_part[*i] as u32 + 1)))
+ }) {
+ assert!(
+ node_capacity[idmin] * (node_nb_part[idnew] as u32 + 1)
+ >= node_capacity[idnew] * node_nb_part[idmin] as u32
+ );
+ }
+ }
+ }
+ }
+
+ fn update_layout(
+ cl: &mut ClusterLayout,
+ node_id_vec: &Vec<u8>,
+ node_capacity_vec: &Vec<u32>,
+ node_zone_vec: &Vec<String>,
+ ) {
+ for i in 0..node_id_vec.len() {
+ if let Some(x) = FixedBytes32::try_from(&[i as u8; 32]) {
+ cl.node_id_vec.push(x);
+ }
+ let update = cl.roles.update_mutator(
+ cl.node_id_vec[i],
+ NodeRoleV(Some(NodeRole {
+ zone: (node_zone_vec[i].to_string()),
+ capacity: (Some(node_capacity_vec[i])),
+ tags: (vec![]),
+ })),
+ );
+ cl.roles.merge(&update);
+ }
+ }
+
+ #[test]
+ fn test_assignation() {
+ let mut node_id_vec = vec![1, 2, 3];
+ let mut node_capacity_vec = vec![4000, 1000, 2000];
+ let mut node_zone_vec = vec!["A", "B", "C"]
+ .into_iter()
+ .map(|x| x.to_string())
+ .collect();
+
+ let mut cl = ClusterLayout {
+ node_id_vec: vec![],
+ roles: LwwMap::new(),
+ replication_factor: 3,
+ ring_assignation_data: vec![],
+ version: 0,
+ staging: LwwMap::new(),
+ staging_hash: sha256sum(&[1; 32]),
+ };
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+ node_id_vec = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
+ node_capacity_vec = vec![4000, 1000, 1000, 3000, 1000, 1000, 2000, 10000, 2000];
+ node_zone_vec = vec!["A", "B", "C", "C", "C", "B", "G", "H", "I"]
+ .into_iter()
+ .map(|x| x.to_string())
+ .collect();
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+ node_capacity_vec = vec![4000, 1000, 2000, 7000, 1000, 1000, 2000, 10000, 2000];
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+ node_capacity_vec = vec![4000, 4000, 2000, 7000, 1000, 9000, 2000, 10, 2000];
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+ }
+}
diff --git a/src/util/bipartite.rs b/src/util/bipartite.rs
index aec7b042..ade831a4 100644
--- a/src/util/bipartite.rs
+++ b/src/util/bipartite.rs
@@ -1,378 +1,376 @@
/*
- * This module deals with graph algorithm in complete bipartite
+ * This module deals with graph algorithm in complete bipartite
* graphs. It is used in layout.rs to build the partition to node
* assignation.
* */
-use std::cmp::{min,max};
-use std::collections::VecDeque;
use rand::prelude::SliceRandom;
+use std::cmp::{max, min};
+use std::collections::VecDeque;
//Graph data structure for the flow algorithm.
-#[derive(Clone,Copy,Debug)]
-struct EdgeFlow{
- c : i32,
- flow : i32,
- v : usize,
- rev : usize,
+#[derive(Clone, Copy, Debug)]
+struct EdgeFlow {
+ c: i32,
+ flow: i32,
+ v: usize,
+ rev: usize,
}
//Graph data structure for the detection of positive cycles.
-#[derive(Clone,Copy,Debug)]
-struct WeightedEdge{
- w : i32,
- u : usize,
- v : usize,
+#[derive(Clone, Copy, Debug)]
+struct WeightedEdge {
+ w: i32,
+ u: usize,
+ v: usize,
}
-
-/* This function takes two matchings (old_match and new_match) in a
- * complete bipartite graph. It returns a matching that has the
+/* This function takes two matchings (old_match and new_match) in a
+ * complete bipartite graph. It returns a matching that has the
* same degree as new_match at every vertex, and that is as close
* as possible to old_match.
* */
-pub fn optimize_matching( old_match : &Vec<Vec<usize>> ,
- new_match : &Vec<Vec<usize>> ,
- nb_right : usize )
- -> Vec<Vec<usize>> {
- let nb_left = old_match.len();
- let ed = WeightedEdge{w:-1,u:0,v:0};
- let mut edge_vec = vec![ed ; nb_left*nb_right];
-
- //We build the complete bipartite graph structure, represented
- //by the list of all edges.
- for i in 0..nb_left {
- for j in 0..nb_right{
- edge_vec[i*nb_right + j].u = i;
- edge_vec[i*nb_right + j].v = nb_left+j;
- }
- }
-
- for i in 0..edge_vec.len() {
- //We add the old matchings
- if old_match[edge_vec[i].u].contains(&(edge_vec[i].v-nb_left)) {
- edge_vec[i].w *= -1;
- }
- //We add the new matchings
- if new_match[edge_vec[i].u].contains(&(edge_vec[i].v-nb_left)) {
- (edge_vec[i].u,edge_vec[i].v) =
- (edge_vec[i].v,edge_vec[i].u);
- edge_vec[i].w *= -1;
- }
- }
- //Now edge_vec is a graph where edges are oriented LR if we
- //can add them to new_match, and RL otherwise. If
- //adding/removing them makes the matching closer to old_match
- //they have weight 1; and -1 otherwise.
-
- //We shuffle the edge list so that there is no bias depending in
- //partitions/zone label in the triplet dispersion
- let mut rng = rand::thread_rng();
- edge_vec.shuffle(&mut rng);
-
- //Discovering and flipping a cycle with positive weight in this
- //graph will make the matching closer to old_match.
- //We use Bellman Ford algorithm to discover positive cycles
- loop{
- if let Some(cycle) = positive_cycle(&edge_vec, nb_left, nb_right) {
- for i in cycle {
- //We flip the edges of the cycle.
- (edge_vec[i].u,edge_vec[i].v) =
- (edge_vec[i].v,edge_vec[i].u);
- edge_vec[i].w *= -1;
- }
- }
- else {
- //If there is no cycle, we return the optimal matching.
- break;
- }
- }
-
- //The optimal matching is build from the graph structure.
- let mut matching = vec![Vec::<usize>::new() ; nb_left];
- for e in edge_vec {
- if e.u > e.v {
- matching[e.v].push(e.u-nb_left);
- }
- }
- matching
+pub fn optimize_matching(
+ old_match: &Vec<Vec<usize>>,
+ new_match: &Vec<Vec<usize>>,
+ nb_right: usize,
+) -> Vec<Vec<usize>> {
+ let nb_left = old_match.len();
+ let ed = WeightedEdge { w: -1, u: 0, v: 0 };
+ let mut edge_vec = vec![ed; nb_left * nb_right];
+
+ //We build the complete bipartite graph structure, represented
+ //by the list of all edges.
+ for i in 0..nb_left {
+ for j in 0..nb_right {
+ edge_vec[i * nb_right + j].u = i;
+ edge_vec[i * nb_right + j].v = nb_left + j;
+ }
+ }
+
+ for i in 0..edge_vec.len() {
+ //We add the old matchings
+ if old_match[edge_vec[i].u].contains(&(edge_vec[i].v - nb_left)) {
+ edge_vec[i].w *= -1;
+ }
+ //We add the new matchings
+ if new_match[edge_vec[i].u].contains(&(edge_vec[i].v - nb_left)) {
+ (edge_vec[i].u, edge_vec[i].v) = (edge_vec[i].v, edge_vec[i].u);
+ edge_vec[i].w *= -1;
+ }
+ }
+ //Now edge_vec is a graph where edges are oriented LR if we
+ //can add them to new_match, and RL otherwise. If
+ //adding/removing them makes the matching closer to old_match
+ //they have weight 1; and -1 otherwise.
+
+ //We shuffle the edge list so that there is no bias depending in
+ //partitions/zone label in the triplet dispersion
+ let mut rng = rand::thread_rng();
+ edge_vec.shuffle(&mut rng);
+
+ //Discovering and flipping a cycle with positive weight in this
+ //graph will make the matching closer to old_match.
+ //We use Bellman Ford algorithm to discover positive cycles
+ loop {
+ if let Some(cycle) = positive_cycle(&edge_vec, nb_left, nb_right) {
+ for i in cycle {
+ //We flip the edges of the cycle.
+ (edge_vec[i].u, edge_vec[i].v) = (edge_vec[i].v, edge_vec[i].u);
+ edge_vec[i].w *= -1;
+ }
+ } else {
+ //If there is no cycle, we return the optimal matching.
+ break;
+ }
+ }
+
+ //The optimal matching is build from the graph structure.
+ let mut matching = vec![Vec::<usize>::new(); nb_left];
+ for e in edge_vec {
+ if e.u > e.v {
+ matching[e.v].push(e.u - nb_left);
+ }
+ }
+ matching
}
//This function finds a positive cycle in a bipartite wieghted graph.
-fn positive_cycle( edge_vec : &Vec<WeightedEdge>, nb_left : usize,
- nb_right : usize) -> Option<Vec<usize>> {
- let nb_side_min = min(nb_left, nb_right);
- let nb_vertices = nb_left+nb_right;
- let weight_lowerbound = -((nb_left +nb_right) as i32) -1;
- let mut accessed = vec![false ; nb_left];
-
- //We try to find a positive cycle accessible from the left
- //vertex i.
- for i in 0..nb_left{
- if accessed[i] {
- continue;
- }
- let mut weight =vec![weight_lowerbound ; nb_vertices];
- let mut prev =vec![ edge_vec.len() ; nb_vertices];
- weight[i] = 0;
- //We compute largest weighted paths from i.
- //Since the graph is bipartite, any simple cycle has length
- //at most 2*nb_side_min. In the general Bellman-Ford
- //algorithm, the bound here is the number of vertices. Since
- //the number of partitions can be much larger than the
- //number of nodes, we optimize that.
- for _ in 0..(2*nb_side_min) {
- for j in 0..edge_vec.len() {
- let e = edge_vec[j];
- if weight[e.v] < weight[e.u]+e.w {
- weight[e.v] = weight[e.u]+e.w;
- prev[e.v] = j;
- }
- }
- }
- //We update the accessed table
- for i in 0..nb_left {
- if weight[i] > weight_lowerbound {
- accessed[i] = true;
- }
- }
- //We detect positive cycle
- for e in edge_vec {
- if weight[e.v] < weight[e.u]+e.w {
- //it means e is on a path branching from a positive cycle
- let mut was_seen = vec![false ; nb_vertices];
- let mut curr = e.u;
- //We track back with prev until we reach the cycle.
- while !was_seen[curr]{
- was_seen[curr] = true;
- curr = edge_vec[prev[curr]].u;
- }
- //Now curr is on the cycle. We collect the edges ids.
- let mut cycle = Vec::<usize>::new();
- cycle.push(prev[curr]);
- let mut cycle_vert = edge_vec[prev[curr]].u;
- while cycle_vert != curr {
- cycle.push(prev[cycle_vert]);
- cycle_vert = edge_vec[prev[cycle_vert]].u;
- }
-
- return Some(cycle);
- }
- }
- }
-
- None
-}
+fn positive_cycle(
+ edge_vec: &Vec<WeightedEdge>,
+ nb_left: usize,
+ nb_right: usize,
+) -> Option<Vec<usize>> {
+ let nb_side_min = min(nb_left, nb_right);
+ let nb_vertices = nb_left + nb_right;
+ let weight_lowerbound = -((nb_left + nb_right) as i32) - 1;
+ let mut accessed = vec![false; nb_left];
+ //We try to find a positive cycle accessible from the left
+ //vertex i.
+ for i in 0..nb_left {
+ if accessed[i] {
+ continue;
+ }
+ let mut weight = vec![weight_lowerbound; nb_vertices];
+ let mut prev = vec![edge_vec.len(); nb_vertices];
+ weight[i] = 0;
+ //We compute largest weighted paths from i.
+ //Since the graph is bipartite, any simple cycle has length
+ //at most 2*nb_side_min. In the general Bellman-Ford
+ //algorithm, the bound here is the number of vertices. Since
+ //the number of partitions can be much larger than the
+ //number of nodes, we optimize that.
+ for _ in 0..(2 * nb_side_min) {
+ for j in 0..edge_vec.len() {
+ let e = edge_vec[j];
+ if weight[e.v] < weight[e.u] + e.w {
+ weight[e.v] = weight[e.u] + e.w;
+ prev[e.v] = j;
+ }
+ }
+ }
+ //We update the accessed table
+ for i in 0..nb_left {
+ if weight[i] > weight_lowerbound {
+ accessed[i] = true;
+ }
+ }
+ //We detect positive cycle
+ for e in edge_vec {
+ if weight[e.v] < weight[e.u] + e.w {
+ //it means e is on a path branching from a positive cycle
+ let mut was_seen = vec![false; nb_vertices];
+ let mut curr = e.u;
+ //We track back with prev until we reach the cycle.
+ while !was_seen[curr] {
+ was_seen[curr] = true;
+ curr = edge_vec[prev[curr]].u;
+ }
+ //Now curr is on the cycle. We collect the edges ids.
+ let mut cycle = Vec::<usize>::new();
+ cycle.push(prev[curr]);
+ let mut cycle_vert = edge_vec[prev[curr]].u;
+ while cycle_vert != curr {
+ cycle.push(prev[cycle_vert]);
+ cycle_vert = edge_vec[prev[cycle_vert]].u;
+ }
-// This function takes two arrays of capacity and computes the
-// maximal matching in the complete bipartite graph such that the
+ return Some(cycle);
+ }
+ }
+ }
+
+ None
+}
+
+// This function takes two arrays of capacity and computes the
+// maximal matching in the complete bipartite graph such that the
// left vertex i is matched to left_cap_vec[i] right vertices, and
// the right vertex j is matched to right_cap_vec[j] left vertices.
// To do so, we use Dinic's maximum flow algorithm.
-pub fn dinic_compute_matching( left_cap_vec : Vec<u32>,
- right_cap_vec : Vec<u32>) -> Vec< Vec<usize> >
-{
- let mut graph = Vec::<Vec::<EdgeFlow> >::new();
- let ed = EdgeFlow{c:0,flow:0,v:0, rev:0};
-
- // 0 will be the source
- graph.push(vec![ed ; left_cap_vec.len()]);
- for i in 0..left_cap_vec.len()
- {
- graph[0][i].c = left_cap_vec[i] as i32;
- graph[0][i].v = i+2;
- graph[0][i].rev = 0;
- }
-
- //1 will be the sink
- graph.push(vec![ed ; right_cap_vec.len()]);
- for i in 0..right_cap_vec.len()
- {
- graph[1][i].c = right_cap_vec[i] as i32;
- graph[1][i].v = i+2+left_cap_vec.len();
- graph[1][i].rev = 0;
- }
-
- //we add left vertices
- for i in 0..left_cap_vec.len() {
- graph.push(vec![ed ; 1+right_cap_vec.len()]);
- graph[i+2][0].c = 0; //directed
- graph[i+2][0].v = 0;
- graph[i+2][0].rev = i;
-
- for j in 0..right_cap_vec.len() {
- graph[i+2][j+1].c = 1;
- graph[i+2][j+1].v = 2+left_cap_vec.len()+j;
- graph[i+2][j+1].rev = i+1;
- }
- }
-
- //we add right vertices
- for i in 0..right_cap_vec.len() {
- let lft_ln = left_cap_vec.len();
- graph.push(vec![ed ; 1+lft_ln]);
- graph[i+lft_ln+2][0].c = graph[1][i].c;
- graph[i+lft_ln+2][0].v = 1;
- graph[i+lft_ln+2][0].rev = i;
-
- for j in 0..left_cap_vec.len() {
- graph[i+2+lft_ln][j+1].c = 0; //directed
- graph[i+2+lft_ln][j+1].v = j+2;
- graph[i+2+lft_ln][j+1].rev = i+1;
- }
- }
-
- //To ensure the dispersion of the triplets generated by the
- //assignation, we shuffle the neighbours of the nodes. Hence,
- //left vertices do not consider the right ones in the same order.
- let mut rng = rand::thread_rng();
- for i in 0..graph.len() {
- graph[i].shuffle(&mut rng);
- //We need to update the ids of the reverse edges.
- for j in 0..graph[i].len() {
- let target_v = graph[i][j].v;
- let target_rev = graph[i][j].rev;
- graph[target_v][target_rev].rev = j;
- }
- }
-
- let nb_vertices = graph.len();
-
- //We run Dinic's max flow algorithm
- loop{
- //We build the level array from Dinic's algorithm.
- let mut level = vec![-1; nb_vertices];
-
- let mut fifo = VecDeque::new();
- fifo.push_back((0,0));
- while !fifo.is_empty() {
- if let Some((id,lvl)) = fifo.pop_front(){
- if level[id] == -1 {
- level[id] = lvl;
- for e in graph[id].iter(){
- if e.c-e.flow > 0{
- fifo.push_back((e.v,lvl+1));
- }
- }
- }
- }
- }
- if level[1] == -1 {
- //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();
-
- let flow_upper_bound;
- if let Some(x) = left_cap_vec.iter().max() {
- flow_upper_bound=*x as i32;
- }
- else {
- flow_upper_bound = 0;
- assert!(false);
- }
-
- lifo.push_back((0,flow_upper_bound));
-
- loop
- {
- if let Some((id_tmp, f_tmp)) = lifo.back() {
- let id = *id_tmp;
- let f = *f_tmp;
- if id == 1 {
- //The DFS reached the sink, we can add a
- //residual flow.
- lifo.pop_back();
- while !lifo.is_empty() {
- if let Some((id,_)) = lifo.pop_back(){
- let nbd=next_nbd[id];
- graph[id][nbd].flow += f;
- let id_v = graph[id][nbd].v;
- let nbd_v = graph[id][nbd].rev;
- graph[id_v][nbd_v].flow -= f;
- }
- }
- lifo.push_back((0,flow_upper_bound));
- continue;
- }
- //else we did not reach the sink
- let nbd = next_nbd[id];
- if nbd >= 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,graph[id][nbd].c
- - graph[id][nbd].flow);
- if level[graph[id][nbd].v] <= level[id] ||
- new_flow == 0 {
- //We cannot send flow to nbd.
- next_nbd[id] += 1;
- continue;
- }
- //otherwise, we send flow to nbd.
- lifo.push_back((graph[id][nbd].v, new_flow));
- }
- else {
- break;
- }
- }
- }
-
- //We return the association
- let assoc_table = (0..left_cap_vec.len()).map(
- |id| graph[id+2].iter()
- .filter(|e| e.flow > 0)
- .map( |e| e.v-2-left_cap_vec.len())
- .collect()).collect();
-
- //consistency check
-
- //it is a flow
- for i in 3..graph.len(){
- assert!( graph[i].iter().map(|e| e.flow).sum::<i32>() == 0);
- for e in graph[i].iter(){
- assert!(e.flow + graph[e.v][e.rev].flow == 0);
- }
- }
-
- //it solves the matching problem
- for i in 0..left_cap_vec.len(){
- assert!(left_cap_vec[i] as i32 ==
- graph[i+2].iter().map(|e| max(0,e.flow)).sum::<i32>());
- }
- for i in 0..right_cap_vec.len(){
- assert!(right_cap_vec[i] as i32 ==
- graph[i+2+left_cap_vec.len()].iter()
- .map(|e| max(0,e.flow)).sum::<i32>());
- }
-
-
- assoc_table
-}
+pub fn dinic_compute_matching(left_cap_vec: Vec<u32>, right_cap_vec: Vec<u32>) -> Vec<Vec<usize>> {
+ let mut graph = Vec::<Vec<EdgeFlow>>::new();
+ let ed = EdgeFlow {
+ c: 0,
+ flow: 0,
+ v: 0,
+ rev: 0,
+ };
+
+ // 0 will be the source
+ graph.push(vec![ed; left_cap_vec.len()]);
+ for i in 0..left_cap_vec.len() {
+ graph[0][i].c = left_cap_vec[i] as i32;
+ graph[0][i].v = i + 2;
+ graph[0][i].rev = 0;
+ }
+
+ //1 will be the sink
+ graph.push(vec![ed; right_cap_vec.len()]);
+ for i in 0..right_cap_vec.len() {
+ graph[1][i].c = right_cap_vec[i] as i32;
+ graph[1][i].v = i + 2 + left_cap_vec.len();
+ graph[1][i].rev = 0;
+ }
+
+ //we add left vertices
+ for i in 0..left_cap_vec.len() {
+ graph.push(vec![ed; 1 + right_cap_vec.len()]);
+ graph[i + 2][0].c = 0; //directed
+ graph[i + 2][0].v = 0;
+ graph[i + 2][0].rev = i;
+
+ for j in 0..right_cap_vec.len() {
+ graph[i + 2][j + 1].c = 1;
+ graph[i + 2][j + 1].v = 2 + left_cap_vec.len() + j;
+ graph[i + 2][j + 1].rev = i + 1;
+ }
+ }
+ //we add right vertices
+ for i in 0..right_cap_vec.len() {
+ let lft_ln = left_cap_vec.len();
+ graph.push(vec![ed; 1 + lft_ln]);
+ graph[i + lft_ln + 2][0].c = graph[1][i].c;
+ graph[i + lft_ln + 2][0].v = 1;
+ graph[i + lft_ln + 2][0].rev = i;
+
+ for j in 0..left_cap_vec.len() {
+ graph[i + 2 + lft_ln][j + 1].c = 0; //directed
+ graph[i + 2 + lft_ln][j + 1].v = j + 2;
+ graph[i + 2 + lft_ln][j + 1].rev = i + 1;
+ }
+ }
+
+ //To ensure the dispersion of the triplets generated by the
+ //assignation, we shuffle the neighbours of the nodes. Hence,
+ //left vertices do not consider the right ones in the same order.
+ let mut rng = rand::thread_rng();
+ for i in 0..graph.len() {
+ graph[i].shuffle(&mut rng);
+ //We need to update the ids of the reverse edges.
+ for j in 0..graph[i].len() {
+ let target_v = graph[i][j].v;
+ let target_rev = graph[i][j].rev;
+ graph[target_v][target_rev].rev = j;
+ }
+ }
+
+ let nb_vertices = graph.len();
+
+ //We run Dinic's max flow algorithm
+ loop {
+ //We build the level array from Dinic's algorithm.
+ let mut level = vec![-1; nb_vertices];
+
+ let mut fifo = VecDeque::new();
+ fifo.push_back((0, 0));
+ while !fifo.is_empty() {
+ if let Some((id, lvl)) = fifo.pop_front() {
+ if level[id] == -1 {
+ level[id] = lvl;
+ for e in graph[id].iter() {
+ if e.c - e.flow > 0 {
+ fifo.push_back((e.v, lvl + 1));
+ }
+ }
+ }
+ }
+ }
+ if level[1] == -1 {
+ //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();
+
+ let flow_upper_bound;
+ if let Some(x) = left_cap_vec.iter().max() {
+ flow_upper_bound = *x as i32;
+ } else {
+ flow_upper_bound = 0;
+ assert!(false);
+ }
+
+ lifo.push_back((0, flow_upper_bound));
+
+ loop {
+ if let Some((id_tmp, f_tmp)) = lifo.back() {
+ let id = *id_tmp;
+ let f = *f_tmp;
+ if id == 1 {
+ //The DFS reached the sink, we can add a
+ //residual flow.
+ lifo.pop_back();
+ while !lifo.is_empty() {
+ if let Some((id, _)) = lifo.pop_back() {
+ let nbd = next_nbd[id];
+ graph[id][nbd].flow += f;
+ let id_v = graph[id][nbd].v;
+ let nbd_v = graph[id][nbd].rev;
+ graph[id_v][nbd_v].flow -= f;
+ }
+ }
+ lifo.push_back((0, flow_upper_bound));
+ continue;
+ }
+ //else we did not reach the sink
+ let nbd = next_nbd[id];
+ if nbd >= 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, graph[id][nbd].c - graph[id][nbd].flow);
+ if level[graph[id][nbd].v] <= level[id] || new_flow == 0 {
+ //We cannot send flow to nbd.
+ next_nbd[id] += 1;
+ continue;
+ }
+ //otherwise, we send flow to nbd.
+ lifo.push_back((graph[id][nbd].v, new_flow));
+ } else {
+ break;
+ }
+ }
+ }
+
+ //We return the association
+ let assoc_table = (0..left_cap_vec.len())
+ .map(|id| {
+ graph[id + 2]
+ .iter()
+ .filter(|e| e.flow > 0)
+ .map(|e| e.v - 2 - left_cap_vec.len())
+ .collect()
+ })
+ .collect();
+
+ //consistency check
+
+ //it is a flow
+ for i in 3..graph.len() {
+ assert!(graph[i].iter().map(|e| e.flow).sum::<i32>() == 0);
+ for e in graph[i].iter() {
+ assert!(e.flow + graph[e.v][e.rev].flow == 0);
+ }
+ }
+
+ //it solves the matching problem
+ for i in 0..left_cap_vec.len() {
+ assert!(left_cap_vec[i] as i32 == graph[i + 2].iter().map(|e| max(0, e.flow)).sum::<i32>());
+ }
+ for i in 0..right_cap_vec.len() {
+ assert!(
+ right_cap_vec[i] as i32
+ == graph[i + 2 + left_cap_vec.len()]
+ .iter()
+ .map(|e| max(0, e.flow))
+ .sum::<i32>()
+ );
+ }
+
+ assoc_table
+}
#[cfg(test)]
mod tests {
- use super::*;
-
- #[test]
- fn test_flow() {
- let left_vec = vec![3;8];
- let right_vec = vec![0,4,8,4,8];
- //There are asserts in the function that computes the flow
- let _ = dinic_compute_matching(left_vec, right_vec);
- }
-
- //maybe add tests relative to the matching optilization ?
-}
+ use super::*;
+ #[test]
+ fn test_flow() {
+ let left_vec = vec![3; 8];
+ let right_vec = vec![0, 4, 8, 4, 8];
+ //There are asserts in the function that computes the flow
+ let _ = dinic_compute_matching(left_vec, right_vec);
+ }
+ //maybe add tests relative to the matching optilization ?
+}