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-rw-r--r--src/api/admin/cluster.rs8
-rw-r--r--src/db/lib.rs1
-rw-r--r--src/garage/cli/layout.rs94
-rw-r--r--src/garage/cli/structs.rs7
-rw-r--r--src/rpc/graph_algo.rs754
-rw-r--r--src/rpc/layout.rs1332
-rw-r--r--src/rpc/lib.rs3
-rw-r--r--src/rpc/system.rs1
8 files changed, 1162 insertions, 1038 deletions
diff --git a/src/api/admin/cluster.rs b/src/api/admin/cluster.rs
index 630179b5..da3d8c44 100644
--- a/src/api/admin/cluster.rs
+++ b/src/api/admin/cluster.rs
@@ -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?;
diff --git a/src/db/lib.rs b/src/db/lib.rs
index af539494..0a776a91 100644
--- a/src/db/lib.rs
+++ b/src/db/lib.rs
@@ -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")]
diff --git a/src/garage/cli/layout.rs b/src/garage/cli/layout.rs
index 32f637eb..f747fbe4 100644
--- a/src/garage/cli/layout.rs
+++ b/src/garage/cli/layout.rs
@@ -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 ---
diff --git a/src/garage/cli/structs.rs b/src/garage/cli/structs.rs
index 896379bb..02ed8992 100644
--- a/src/garage/cli/structs.rs
+++ b/src/garage/cli/structs.rs
@@ -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)
diff --git a/src/rpc/graph_algo.rs b/src/rpc/graph_algo.rs
index 70ccf35a..13c60692 100644
--- a/src/rpc/graph_algo.rs
+++ b/src/rpc/graph_algo.rs
@@ -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>,
-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() ]
- }
- }
+ graph: Vec<Vec<E>>,
}
-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)
- }
-
- //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 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 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;
- }
- }
- }
-
- //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());
- }
-
- let idsource = self.vertextoid[&Vertex::Source];
- let idsink = self.vertextoid[&Vertex::Sink];
-
- let nb_vertices = self.graph.len();
-
- 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];
-
- 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)
-
- }
-
-
+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 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];
-
- 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();
- }
-
+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)
+ }
+
+ //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 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 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;
+ }
+ }
+ }
+
+ //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());
+ }
+
+ let idsource = self.vertextoid[&Vertex::Source];
+ let idsink = self.vertextoid[&Vertex::Sink];
+
+ let nb_vertices = self.graph.len();
+
+ 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];
+
+ 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(())
+ }
+
+ //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];
+
+ 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();
+ }
+}
//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()];
-
- 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
+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
}
-
-
diff --git a/src/rpc/layout.rs b/src/rpc/layout.rs
index 976f94af..3a6f42ee 100644
--- a/src/rpc/layout.rs
+++ b/src/rpc/layout.rs
@@ -7,7 +7,7 @@ use itertools::Itertools;
use serde::{Deserialize, Serialize};
-use garage_util::crdt::{AutoCrdt, Crdt, LwwMap, Lww};
+use garage_util::crdt::{AutoCrdt, Crdt, Lww, LwwMap};
use garage_util::data::*;
use garage_util::error::*;
@@ -27,11 +27,11 @@ pub struct ClusterLayout {
pub version: u64,
pub replication_factor: usize,
-
- //This attribute is only used to retain the previously computed partition size,
- //to know to what extent does it change with the layout update.
- pub partition_size: u32,
- pub parameters: LayoutParameters,
+
+ //This attribute is only used to retain the previously computed partition size,
+ //to know to what extent does it change with the layout update.
+ pub partition_size: u32,
+ pub parameters: LayoutParameters,
pub roles: LwwMap<Uuid, NodeRoleV>,
@@ -39,7 +39,7 @@ pub struct ClusterLayout {
/// in the system (this includes gateway nodes).
/// The order here is different than the vec stored by `roles`, because:
/// 1. non-gateway nodes are first so that they have lower numbers holding
- /// in u8 (the number of non-gateway nodes is at most 256).
+ /// in u8 (the number of non-gateway nodes is at most 256).
/// 2. nodes that don't have a role are excluded (but they need to
/// stay in the CRDT as tombstones)
pub node_id_vec: Vec<Uuid>,
@@ -49,7 +49,7 @@ pub struct ClusterLayout {
pub ring_assignation_data: Vec<CompactNodeType>,
/// Role changes which are staged for the next version of the layout
- pub staged_parameters: Lww<LayoutParameters>,
+ pub staged_parameters: Lww<LayoutParameters>,
pub staging: LwwMap<Uuid, NodeRoleV>,
pub staging_hash: Hash,
}
@@ -58,14 +58,14 @@ pub struct ClusterLayout {
///algorithm. It is stored as a Crdt.
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
pub struct LayoutParameters {
- pub zone_redundancy:usize,
+ pub zone_redundancy: usize,
}
impl AutoCrdt for LayoutParameters {
const WARN_IF_DIFFERENT: bool = true;
}
-const NB_PARTITIONS : usize = 1usize << PARTITION_BITS;
+const NB_PARTITIONS: usize = 1usize << PARTITION_BITS;
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
pub struct NodeRoleV(pub Option<NodeRole>);
@@ -96,27 +96,28 @@ impl NodeRole {
}
}
- pub fn tags_string(&self) -> String {
- let mut tags = String::new();
- if self.tags.is_empty() {
- return tags
- }
- tags.push_str(&self.tags[0].clone());
- for t in 1..self.tags.len(){
- tags.push(',');
- tags.push_str(&self.tags[t].clone());
- }
- tags
- }
+ pub fn tags_string(&self) -> String {
+ let mut tags = String::new();
+ if self.tags.is_empty() {
+ return tags;
+ }
+ tags.push_str(&self.tags[0].clone());
+ for t in 1..self.tags.len() {
+ tags.push(',');
+ tags.push_str(&self.tags[t].clone());
+ }
+ tags
+ }
}
impl ClusterLayout {
pub fn new(replication_factor: usize) -> Self {
-
- //We set the default zone redundancy to be equal to the replication factor,
- //i.e. as strict as possible.
- let parameters = LayoutParameters{ zone_redundancy: replication_factor};
- let staged_parameters = Lww::<LayoutParameters>::new(parameters.clone());
+ //We set the default zone redundancy to be equal to the replication factor,
+ //i.e. as strict as possible.
+ let parameters = LayoutParameters {
+ zone_redundancy: replication_factor,
+ };
+ let staged_parameters = Lww::<LayoutParameters>::new(parameters.clone());
let empty_lwwmap = LwwMap::new();
let empty_lwwmap_hash = blake2sum(&rmp_to_vec_all_named(&empty_lwwmap).unwrap()[..]);
@@ -124,12 +125,12 @@ impl ClusterLayout {
ClusterLayout {
version: 0,
replication_factor,
- partition_size: 0,
+ partition_size: 0,
roles: LwwMap::new(),
node_id_vec: Vec::new(),
ring_assignation_data: Vec::new(),
- parameters,
- staged_parameters,
+ parameters,
+ staged_parameters,
staging: empty_lwwmap,
staging_hash: empty_lwwmap_hash,
}
@@ -142,11 +143,10 @@ impl ClusterLayout {
true
}
Ordering::Equal => {
- let param_changed = self.staged_parameters.get() != other.staged_parameters.get();
- self.staged_parameters.merge(&other.staged_parameters);
+ let param_changed = self.staged_parameters.get() != other.staged_parameters.get();
+ self.staged_parameters.merge(&other.staged_parameters);
self.staging.merge(&other.staging);
-
let new_staging_hash = blake2sum(&rmp_to_vec_all_named(&self.staging).unwrap()[..]);
let stage_changed = new_staging_hash != self.staging_hash;
@@ -158,7 +158,7 @@ impl ClusterLayout {
}
}
- pub fn apply_staged_changes(mut self, version: Option<u64>) -> Result<(Self,Message), Error> {
+ pub fn apply_staged_changes(mut self, version: Option<u64>) -> Result<(Self, Message), Error> {
match version {
None => {
let error = r#"
@@ -177,14 +177,14 @@ To know the correct value of the new layout version, invoke `garage layout show`
self.roles.merge(&self.staging);
self.roles.retain(|(_, _, v)| v.0.is_some());
- let msg = self.calculate_partition_assignation()?;
+ let msg = self.calculate_partition_assignation()?;
self.staging.clear();
self.staging_hash = blake2sum(&rmp_to_vec_all_named(&self.staging).unwrap()[..]);
self.version += 1;
- Ok((self,msg))
+ Ok((self, msg))
}
pub fn revert_staged_changes(mut self, version: Option<u64>) -> Result<Self, Error> {
@@ -229,44 +229,52 @@ To know the correct value of the new layout version, invoke `garage layout show`
}
}
- ///Returns the uuids of the non_gateway nodes in self.node_id_vec.
- pub fn useful_nodes(&self) -> Vec<Uuid> {
- let mut result = Vec::<Uuid>::new();
- for uuid in self.node_id_vec.iter() {
- match self.node_role(uuid) {
- Some(role) if role.capacity != None => result.push(*uuid),
- _ => ()
- }
- }
- result
- }
-
- ///Given a node uuids, this function returns the label of its zone
- pub fn get_node_zone(&self, uuid : &Uuid) -> Result<String,Error> {
- match self.node_role(uuid) {
- Some(role) => Ok(role.zone.clone()),
- _ => Err(Error::Message("The Uuid does not correspond to a node present in the cluster.".into()))
- }
- }
-
- ///Given a node uuids, this function returns its capacity or fails if it does not have any
- pub fn get_node_capacity(&self, uuid : &Uuid) -> Result<u32,Error> {
- match self.node_role(uuid) {
- Some(NodeRole{capacity : Some(cap), zone: _, tags: _}) => Ok(*cap),
- _ => Err(Error::Message("The Uuid does not correspond to a node present in the \
- cluster or this node does not have a positive capacity.".into()))
- }
- }
-
- ///Returns the sum of capacities of non gateway nodes in the cluster
- pub fn get_total_capacity(&self) -> Result<u32,Error> {
- let mut total_capacity = 0;
- for uuid in self.useful_nodes().iter() {
- total_capacity += self.get_node_capacity(uuid)?;
- }
- Ok(total_capacity)
- }
+ ///Returns the uuids of the non_gateway nodes in self.node_id_vec.
+ pub fn useful_nodes(&self) -> Vec<Uuid> {
+ let mut result = Vec::<Uuid>::new();
+ for uuid in self.node_id_vec.iter() {
+ match self.node_role(uuid) {
+ Some(role) if role.capacity != None => result.push(*uuid),
+ _ => (),
+ }
+ }
+ result
+ }
+
+ ///Given a node uuids, this function returns the label of its zone
+ pub fn get_node_zone(&self, uuid: &Uuid) -> Result<String, Error> {
+ match self.node_role(uuid) {
+ Some(role) => Ok(role.zone.clone()),
+ _ => Err(Error::Message(
+ "The Uuid does not correspond to a node present in the cluster.".into(),
+ )),
+ }
+ }
+
+ ///Given a node uuids, this function returns its capacity or fails if it does not have any
+ pub fn get_node_capacity(&self, uuid: &Uuid) -> Result<u32, Error> {
+ match self.node_role(uuid) {
+ Some(NodeRole {
+ capacity: Some(cap),
+ zone: _,
+ tags: _,
+ }) => Ok(*cap),
+ _ => Err(Error::Message(
+ "The Uuid does not correspond to a node present in the \
+ cluster or this node does not have a positive capacity."
+ .into(),
+ )),
+ }
+ }
+ ///Returns the sum of capacities of non gateway nodes in the cluster
+ pub fn get_total_capacity(&self) -> Result<u32, Error> {
+ let mut total_capacity = 0;
+ for uuid in self.useful_nodes().iter() {
+ total_capacity += self.get_node_capacity(uuid)?;
+ }
+ Ok(total_capacity)
+ }
/// Check a cluster layout for internal consistency
/// returns true if consistent, false if error
@@ -311,580 +319,689 @@ To know the correct value of the new layout version, invoke `garage layout show`
}
}
- //Check that every partition is associated to distinct nodes
- let rf = self.replication_factor;
- for p in 0..(1 << PARTITION_BITS) {
- let nodes_of_p = self.ring_assignation_data[rf*p..rf*(p+1)].to_vec();
- if nodes_of_p.iter().unique().count() != rf {
- return false;
- }
- //Check that every partition is spread over at least zone_redundancy zones.
- let zones_of_p = nodes_of_p.iter()
- .map(|n| self.get_node_zone(&self.node_id_vec[*n as usize])
- .expect("Zone not found."));
- let redundancy = self.parameters.zone_redundancy;
- if zones_of_p.unique().count() < redundancy {
- return false;
- }
- }
-
- //Check that the nodes capacities is consistent with the stored partitions
- let mut node_usage = vec![0; MAX_NODE_NUMBER];
- for n in self.ring_assignation_data.iter() {
- node_usage[*n as usize] += 1;
- }
- for (n, usage) in node_usage.iter().enumerate(){
- if *usage > 0 {
- let uuid = self.node_id_vec[n];
- if usage*self.partition_size > self.get_node_capacity(&uuid)
- .expect("Critical Error"){
- return false;
- }
- }
- }
-
- //Check that the partition size stored is the one computed by the asignation
- //algorithm.
- let cl2 = self.clone();
- let (_ , zone_to_id) = cl2.generate_useful_zone_ids().expect("Critical Error");
- let partition_size = cl2.compute_optimal_partition_size(&zone_to_id).expect("Critical Error");
- if partition_size != self.partition_size {
- return false;
- }
+ //Check that every partition is associated to distinct nodes
+ let rf = self.replication_factor;
+ for p in 0..(1 << PARTITION_BITS) {
+ let nodes_of_p = self.ring_assignation_data[rf * p..rf * (p + 1)].to_vec();
+ if nodes_of_p.iter().unique().count() != rf {
+ return false;
+ }
+ //Check that every partition is spread over at least zone_redundancy zones.
+ let zones_of_p = nodes_of_p.iter().map(|n| {
+ self.get_node_zone(&self.node_id_vec[*n as usize])
+ .expect("Zone not found.")
+ });
+ let redundancy = self.parameters.zone_redundancy;
+ if zones_of_p.unique().count() < redundancy {
+ return false;
+ }
+ }
+
+ //Check that the nodes capacities is consistent with the stored partitions
+ let mut node_usage = vec![0; MAX_NODE_NUMBER];
+ for n in self.ring_assignation_data.iter() {
+ node_usage[*n as usize] += 1;
+ }
+ for (n, usage) in node_usage.iter().enumerate() {
+ if *usage > 0 {
+ let uuid = self.node_id_vec[n];
+ if usage * self.partition_size
+ > self.get_node_capacity(&uuid).expect("Critical Error")
+ {
+ return false;
+ }
+ }
+ }
+ //Check that the partition size stored is the one computed by the asignation
+ //algorithm.
+ let cl2 = self.clone();
+ let (_, zone_to_id) = cl2.generate_useful_zone_ids().expect("Critical Error");
+ let partition_size = cl2
+ .compute_optimal_partition_size(&zone_to_id)
+ .expect("Critical Error");
+ if partition_size != self.partition_size {
+ return false;
+ }
true
}
-
}
impl ClusterLayout {
/// This function calculates a new partition-to-node assignation.
/// The computed assignation respects the node replication factor
- /// and the zone redundancy parameter It maximizes the capacity of a
+ /// and the zone redundancy parameter It 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.
- /// Staged changes must be merged with nodes roles before calling this function.
- pub fn calculate_partition_assignation(&mut self) -> Result<Message,Error> {
+ /// Staged changes must be merged with nodes roles before calling this function.
+ pub fn calculate_partition_assignation(&mut self) -> Result<Message, Error> {
//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.
-
- //We update the node ids, since the node role list might have changed with the
- //changes in the layout. We retrieve the old_assignation reframed with the new ids
- let old_assignation_opt = self.update_node_id_vec()?;
-
- let redundancy = self.staged_parameters.get().zone_redundancy;
-
-
- let mut msg = Message::new();
- msg.push(format!("Computation of a new cluster layout where partitions are \
- replicated {} times on at least {} distinct zones.", self.replication_factor, redundancy));
-
- //We generate for once numerical ids for the zones of non gateway nodes,
- //to use them as indices in the flow graphs.
- let (id_to_zone , zone_to_id) = self.generate_useful_zone_ids()?;
-
- let nb_useful_nodes = self.useful_nodes().len();
- msg.push(format!("The cluster contains {} nodes spread over {} zones.",
- nb_useful_nodes, id_to_zone.len()));
- if nb_useful_nodes < self.replication_factor{
- return Err(Error::Message(format!("The number of nodes with positive \
+
+ //We update the node ids, since the node role list might have changed with the
+ //changes in the layout. We retrieve the old_assignation reframed with the new ids
+ let old_assignation_opt = self.update_node_id_vec()?;
+
+ let redundancy = self.staged_parameters.get().zone_redundancy;
+
+ let mut msg = Message::new();
+ msg.push(format!(
+ "Computation of a new cluster layout where partitions are \
+ replicated {} times on at least {} distinct zones.",
+ self.replication_factor, redundancy
+ ));
+
+ //We generate for once numerical ids for the zones of non gateway nodes,
+ //to use them as indices in the flow graphs.
+ let (id_to_zone, zone_to_id) = self.generate_useful_zone_ids()?;
+
+ let nb_useful_nodes = self.useful_nodes().len();
+ msg.push(format!(
+ "The cluster contains {} nodes spread over {} zones.",
+ nb_useful_nodes,
+ id_to_zone.len()
+ ));
+ if nb_useful_nodes < self.replication_factor {
+ return Err(Error::Message(format!(
+ "The number of nodes with positive \
capacity ({}) is smaller than the replication factor ({}).",
- nb_useful_nodes, self.replication_factor)));
- }
- if id_to_zone.len() < redundancy {
- return Err(Error::Message(format!("The number of zones with non-gateway \
+ nb_useful_nodes, self.replication_factor
+ )));
+ }
+ if id_to_zone.len() < redundancy {
+ return Err(Error::Message(format!(
+ "The number of zones with non-gateway \
nodes ({}) is smaller than the redundancy parameter ({})",
- id_to_zone.len() , redundancy)));
- }
-
- //We compute the optimal partition size
- //Capacities should be given in a unit so that partition size is at least 100.
- //In this case, integer rounding plays a marginal role in the percentages of
- //optimality.
- let partition_size = self.compute_optimal_partition_size(&zone_to_id)?;
-
- if old_assignation_opt != None {
- msg.push(format!("Given the replication and redundancy constraint, the \
+ id_to_zone.len(),
+ redundancy
+ )));
+ }
+
+ //We compute the optimal partition size
+ //Capacities should be given in a unit so that partition size is at least 100.
+ //In this case, integer rounding plays a marginal role in the percentages of
+ //optimality.
+ let partition_size = self.compute_optimal_partition_size(&zone_to_id)?;
+
+ if old_assignation_opt != None {
+ msg.push(format!(
+ "Given the replication and redundancy constraint, the \
optimal size of a partition is {}. In the previous layout, it used to \
- be {} (the zone redundancy was {}).", partition_size, self.partition_size,
- self.parameters.zone_redundancy));
- }
- else {
- msg.push(format!("Given the replication and redundancy constraints, the \
- optimal size of a partition is {}.", partition_size));
- }
- self.partition_size = partition_size;
- self.parameters = self.staged_parameters.get().clone();
-
- if partition_size < 100 {
- msg.push("WARNING: The partition size is low (< 100), you might consider to \
- provide the nodes capacities in a smaller unit (e.g. Mb instead of Gb).".into());
- }
-
- //We compute a first flow/assignment that is heuristically close to the previous
- //assignment
- let mut gflow = self.compute_candidate_assignment( &zone_to_id, &old_assignation_opt)?;
- if let Some(assoc) = &old_assignation_opt {
- //We minimize the distance to the previous assignment.
- self.minimize_rebalance_load(&mut gflow, &zone_to_id, assoc)?;
- }
-
- msg.append(&mut self.output_stat(&gflow, &old_assignation_opt, &zone_to_id,&id_to_zone)?);
- msg.push("".to_string());
-
- //We update the layout structure
- self.update_ring_from_flow(id_to_zone.len() , &gflow)?;
- Ok(msg)
- }
+ be {} (the zone redundancy was {}).",
+ partition_size, self.partition_size, self.parameters.zone_redundancy
+ ));
+ } else {
+ msg.push(format!(
+ "Given the replication and redundancy constraints, the \
+ optimal size of a partition is {}.",
+ partition_size
+ ));
+ }
+ self.partition_size = partition_size;
+ self.parameters = self.staged_parameters.get().clone();
+
+ if partition_size < 100 {
+ msg.push(
+ "WARNING: The partition size is low (< 100), you might consider to \
+ provide the nodes capacities in a smaller unit (e.g. Mb instead of Gb)."
+ .into(),
+ );
+ }
+
+ //We compute a first flow/assignment that is heuristically close to the previous
+ //assignment
+ let mut gflow = self.compute_candidate_assignment(&zone_to_id, &old_assignation_opt)?;
+ if let Some(assoc) = &old_assignation_opt {
+ //We minimize the distance to the previous assignment.
+ self.minimize_rebalance_load(&mut gflow, &zone_to_id, assoc)?;
+ }
+
+ msg.append(&mut self.output_stat(
+ &gflow,
+ &old_assignation_opt,
+ &zone_to_id,
+ &id_to_zone,
+ )?);
+ msg.push("".to_string());
+
+ //We update the layout structure
+ self.update_ring_from_flow(id_to_zone.len(), &gflow)?;
+ Ok(msg)
+ }
/// 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 if 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_node_id_vec(&mut self) -> Result< Option< Vec<Vec<usize> > > ,Error> {
- // (1) We compute the new node list
- //Non gateway nodes should be coded on 8bits, hence they must be first in the list
- //We build the new node ids
- let mut new_non_gateway_nodes: Vec<Uuid> = self.roles.items().iter()
- .filter(|(_, _, v)| matches!(&v.0, Some(r) if r.capacity != None))
- .map(|(k, _, _)| *k).collect();
-
- if new_non_gateway_nodes.len() > MAX_NODE_NUMBER {
- return Err(Error::Message(format!("There are more than {} non-gateway nodes in the new \
- layout. This is not allowed.", MAX_NODE_NUMBER) ));
- }
-
- let mut new_gateway_nodes: Vec<Uuid> = self.roles.items().iter()
- .filter(|(_, _, v)| matches!(v, NodeRoleV(Some(r)) if r.capacity == None))
- .map(|(k, _, _)| *k).collect();
-
- let mut new_node_id_vec = Vec::<Uuid>::new();
- new_node_id_vec.append(&mut new_non_gateway_nodes);
- new_node_id_vec.append(&mut new_gateway_nodes);
-
- let old_node_id_vec = self.node_id_vec.clone();
- self.node_id_vec = new_node_id_vec.clone();
-
- // (2) We retrieve the old association
- //We rewrite the old association with the new indices. We only consider partition
- //to node assignations where the node is still in use.
- let nb_partitions = 1usize << PARTITION_BITS;
- let mut old_assignation = vec![ Vec::<usize>::new() ; nb_partitions];
-
- if self.ring_assignation_data.is_empty() {
- //This is a new association
- return Ok(None);
- }
- if self.ring_assignation_data.len() != nb_partitions * self.replication_factor {
- return Err(Error::Message("The old assignation does not have a size corresponding to \
- the old replication factor or the number of partitions.".into()));
- }
-
- //We build a translation table between the uuid and new ids
- let mut uuid_to_new_id = HashMap::<Uuid, usize>::new();
-
- //We add the indices of only the new non-gateway nodes that can be used in the
- //association ring
- for (i, uuid) in new_node_id_vec.iter().enumerate() {
- uuid_to_new_id.insert(*uuid, i );
- }
-
- let rf= self.replication_factor;
- for (p, old_assign_p) in old_assignation.iter_mut().enumerate() {
- for old_id in &self.ring_assignation_data[p*rf..(p+1)*rf] {
- let uuid = old_node_id_vec[*old_id as usize];
- if uuid_to_new_id.contains_key(&uuid) {
- old_assign_p.push(uuid_to_new_id[&uuid]);
- }
- }
- }
-
- //We write the ring
- self.ring_assignation_data = Vec::<CompactNodeType>::new();
-
- if !self.check() {
- return Err(Error::Message("Critical error: The computed layout happens to be incorrect".into()));
- }
-
- Ok(Some(old_assignation))
+ fn update_node_id_vec(&mut self) -> Result<Option<Vec<Vec<usize>>>, Error> {
+ // (1) We compute the new node list
+ //Non gateway nodes should be coded on 8bits, hence they must be first in the list
+ //We build the new node ids
+ let mut new_non_gateway_nodes: Vec<Uuid> = self
+ .roles
+ .items()
+ .iter()
+ .filter(|(_, _, v)| matches!(&v.0, Some(r) if r.capacity != None))
+ .map(|(k, _, _)| *k)
+ .collect();
+
+ if new_non_gateway_nodes.len() > MAX_NODE_NUMBER {
+ return Err(Error::Message(format!(
+ "There are more than {} non-gateway nodes in the new \
+ layout. This is not allowed.",
+ MAX_NODE_NUMBER
+ )));
+ }
+
+ let mut new_gateway_nodes: Vec<Uuid> = self
+ .roles
+ .items()
+ .iter()
+ .filter(|(_, _, v)| matches!(v, NodeRoleV(Some(r)) if r.capacity == None))
+ .map(|(k, _, _)| *k)
+ .collect();
+
+ let mut new_node_id_vec = Vec::<Uuid>::new();
+ new_node_id_vec.append(&mut new_non_gateway_nodes);
+ new_node_id_vec.append(&mut new_gateway_nodes);
+
+ let old_node_id_vec = self.node_id_vec.clone();
+ self.node_id_vec = new_node_id_vec.clone();
+
+ // (2) We retrieve the old association
+ //We rewrite the old association with the new indices. We only consider partition
+ //to node assignations where the node is still in use.
+ let nb_partitions = 1usize << PARTITION_BITS;
+ let mut old_assignation = vec![Vec::<usize>::new(); nb_partitions];
+
+ if self.ring_assignation_data.is_empty() {
+ //This is a new association
+ return Ok(None);
+ }
+ if self.ring_assignation_data.len() != nb_partitions * self.replication_factor {
+ return Err(Error::Message(
+ "The old assignation does not have a size corresponding to \
+ the old replication factor or the number of partitions."
+ .into(),
+ ));
+ }
+
+ //We build a translation table between the uuid and new ids
+ let mut uuid_to_new_id = HashMap::<Uuid, usize>::new();
+
+ //We add the indices of only the new non-gateway nodes that can be used in the
+ //association ring
+ for (i, uuid) in new_node_id_vec.iter().enumerate() {
+ uuid_to_new_id.insert(*uuid, i);
+ }
+
+ let rf = self.replication_factor;
+ for (p, old_assign_p) in old_assignation.iter_mut().enumerate() {
+ for old_id in &self.ring_assignation_data[p * rf..(p + 1) * rf] {
+ let uuid = old_node_id_vec[*old_id as usize];
+ if uuid_to_new_id.contains_key(&uuid) {
+ old_assign_p.push(uuid_to_new_id[&uuid]);
+ }
+ }
+ }
+
+ //We write the ring
+ self.ring_assignation_data = Vec::<CompactNodeType>::new();
+
+ if !self.check() {
+ return Err(Error::Message(
+ "Critical error: The computed layout happens to be incorrect".into(),
+ ));
+ }
+
+ Ok(Some(old_assignation))
}
+ ///This function generates ids for the zone of the nodes appearing in
+ ///self.node_id_vec.
+ fn generate_useful_zone_ids(&self) -> Result<(Vec<String>, HashMap<String, usize>), Error> {
+ let mut id_to_zone = Vec::<String>::new();
+ let mut zone_to_id = HashMap::<String, usize>::new();
+
+ for uuid in self.useful_nodes().iter() {
+ if self.roles.get(uuid) == None {
+ return Err(Error::Message(
+ "The uuid was not found in the node roles (this should \
+ not happen, it might be a critical error)."
+ .into(),
+ ));
+ }
+ if let Some(r) = self.node_role(uuid) {
+ if !zone_to_id.contains_key(&r.zone) && r.capacity != None {
+ zone_to_id.insert(r.zone.clone(), id_to_zone.len());
+ id_to_zone.push(r.zone.clone());
+ }
+ }
+ }
+ Ok((id_to_zone, zone_to_id))
+ }
- ///This function generates ids for the zone of the nodes appearing in
- ///self.node_id_vec.
- fn generate_useful_zone_ids(&self) -> Result<(Vec<String>, HashMap<String, usize>),Error>{
- let mut id_to_zone = Vec::<String>::new();
- let mut zone_to_id = HashMap::<String,usize>::new();
-
- for uuid in self.useful_nodes().iter() {
- if self.roles.get(uuid) == None {
- return Err(Error::Message("The uuid was not found in the node roles (this should \
- not happen, it might be a critical error).".into()));
- }
- if let Some(r) = self.node_role(uuid) {
- if !zone_to_id.contains_key(&r.zone) && r.capacity != None {
- zone_to_id.insert(r.zone.clone() , id_to_zone.len());
- id_to_zone.push(r.zone.clone());
- }
- }
- }
- Ok((id_to_zone, zone_to_id))
- }
-
- ///This function computes by dichotomy the largest realizable partition size, given
- ///the layout.
- fn compute_optimal_partition_size(&self, zone_to_id: &HashMap<String, usize>) -> Result<u32,Error>{
- let nb_partitions = 1usize << PARTITION_BITS;
- let empty_set = HashSet::<(usize,usize)>::new();
- let mut g = self.generate_flow_graph(1, zone_to_id, &empty_set)?;
- g.compute_maximal_flow()?;
- if g.get_flow_value()? < (nb_partitions*self.replication_factor).try_into().unwrap() {
- return Err(Error::Message("The storage capacity of he cluster is to small. It is \
- impossible to store partitions of size 1.".into()));
- }
-
- let mut s_down = 1;
- let mut s_up = self.get_total_capacity()?;
- while s_down +1 < s_up {
- g = self.generate_flow_graph((s_down+s_up)/2, zone_to_id, &empty_set)?;
- g.compute_maximal_flow()?;
- if g.get_flow_value()? < (nb_partitions*self.replication_factor).try_into().unwrap() {
- s_up = (s_down+s_up)/2;
- }
- else {
- s_down = (s_down+s_up)/2;
- }
- }
-
- Ok(s_down)
- }
-
- fn generate_graph_vertices(nb_zones : usize, nb_nodes : usize) -> Vec<Vertex> {
- let mut vertices = vec![Vertex::Source, Vertex::Sink];
- for p in 0..NB_PARTITIONS {
- vertices.push(Vertex::Pup(p));
- vertices.push(Vertex::Pdown(p));
- for z in 0..nb_zones {
- vertices.push(Vertex::PZ(p, z));
- }
- }
- for n in 0..nb_nodes {
- vertices.push(Vertex::N(n));
- }
- vertices
- }
-
- fn generate_flow_graph(&self, size: u32, zone_to_id: &HashMap<String, usize>, exclude_assoc : &HashSet<(usize,usize)>) -> Result<Graph<FlowEdge>, Error> {
- let vertices = ClusterLayout::generate_graph_vertices(zone_to_id.len(),
- self.useful_nodes().len());
- let mut g= Graph::<FlowEdge>::new(&vertices);
- let nb_zones = zone_to_id.len();
- let redundancy = self.staged_parameters.get().zone_redundancy;
- for p in 0..NB_PARTITIONS {
- g.add_edge(Vertex::Source, Vertex::Pup(p), redundancy as u32)?;
- g.add_edge(Vertex::Source, Vertex::Pdown(p), (self.replication_factor - redundancy) as u32)?;
- for z in 0..nb_zones {
- g.add_edge(Vertex::Pup(p) , Vertex::PZ(p,z) , 1)?;
- g.add_edge(Vertex::Pdown(p) , Vertex::PZ(p,z) ,
- self.replication_factor as u32)?;
- }
- }
- for n in 0..self.useful_nodes().len() {
- let node_capacity = self.get_node_capacity(&self.node_id_vec[n])?;
- let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[n])?];
- g.add_edge(Vertex::N(n), Vertex::Sink, node_capacity/size)?;
- for p in 0..NB_PARTITIONS {
- if !exclude_assoc.contains(&(p,n)) {
- g.add_edge(Vertex::PZ(p, node_zone), Vertex::N(n), 1)?;
- }
- }
- }
- Ok(g)
- }
-
-
- fn compute_candidate_assignment(&self, zone_to_id: &HashMap<String, usize>,
- old_assoc_opt : &Option<Vec< Vec<usize> >>) -> Result<Graph<FlowEdge>, Error > {
-
- //We list the edges that are not used in the old association
- let mut exclude_edge = HashSet::<(usize,usize)>::new();
- if let Some(old_assoc) = old_assoc_opt {
- let nb_nodes = self.useful_nodes().len();
- for (p, old_assoc_p) in old_assoc.iter().enumerate() {
- for n in 0..nb_nodes {
- exclude_edge.insert((p,n));
- }
- for n in old_assoc_p.iter() {
- exclude_edge.remove(&(p,*n));
- }
- }
- }
-
- //We compute the best flow using only the edges used in the old assoc
- let mut g = self.generate_flow_graph(self.partition_size, zone_to_id, &exclude_edge )?;
- g.compute_maximal_flow()?;
- for (p,n) in exclude_edge.iter() {
- let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?];
- g.add_edge(Vertex::PZ(*p,node_zone), Vertex::N(*n), 1)?;
- }
- g.compute_maximal_flow()?;
- Ok(g)
- }
-
- fn minimize_rebalance_load(&self, gflow: &mut Graph<FlowEdge>, zone_to_id: &HashMap<String, usize>, old_assoc : &[Vec<usize> ]) -> Result<(), Error > {
- let mut cost = CostFunction::new();
- for (p, assoc_p) in old_assoc.iter().enumerate(){
- for n in assoc_p.iter() {
- let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?];
- cost.insert((Vertex::PZ(p,node_zone), Vertex::N(*n)), -1);
- }
- }
- let nb_nodes = self.useful_nodes().len();
- let path_length = 4*nb_nodes;
- gflow.optimize_flow_with_cost(&cost, path_length)?;
-
- Ok(())
- }
-
- fn update_ring_from_flow(&mut self, nb_zones : usize, gflow: &Graph<FlowEdge> ) -> Result<(), Error>{
- self.ring_assignation_data = Vec::<CompactNodeType>::new();
- for p in 0..NB_PARTITIONS {
- for z in 0..nb_zones {
- let assoc_vertex = gflow.get_positive_flow_from(Vertex::PZ(p,z))?;
- for vertex in assoc_vertex.iter() {
- if let Vertex::N(n) = vertex {
- self.ring_assignation_data.push((*n).try_into().unwrap());
- }
- }
- }
- }
-
- if self.ring_assignation_data.len() != NB_PARTITIONS*self.replication_factor {
- return Err(Error::Message("Critical Error : the association ring we produced does not \
- have the right size.".into()));
- }
- Ok(())
- }
-
-
- //This function returns a message summing up the partition repartition of the new
- //layout.
- fn output_stat(&self , gflow : &Graph<FlowEdge>,
- old_assoc_opt : &Option< Vec<Vec<usize>> >,
- zone_to_id: &HashMap<String, usize>,
- id_to_zone : &[String]) -> Result<Message, Error>{
- let mut msg = Message::new();
-
+ ///This function computes by dichotomy the largest realizable partition size, given
+ ///the layout.
+ fn compute_optimal_partition_size(
+ &self,
+ zone_to_id: &HashMap<String, usize>,
+ ) -> Result<u32, Error> {
let nb_partitions = 1usize << PARTITION_BITS;
- let used_cap = self.partition_size * nb_partitions as u32 *
- self.replication_factor as u32;
- let total_cap = self.get_total_capacity()?;
- let percent_cap = 100.0*(used_cap as f32)/(total_cap as f32);
- msg.push(format!("Available capacity / Total cluster capacity: {} / {} ({:.1} %)",
- used_cap , total_cap , percent_cap ));
- msg.push("".into());
- msg.push("If the percentage is to low, it might be that the \
+ let empty_set = HashSet::<(usize, usize)>::new();
+ let mut g = self.generate_flow_graph(1, zone_to_id, &empty_set)?;
+ g.compute_maximal_flow()?;
+ if g.get_flow_value()?
+ < (nb_partitions * self.replication_factor)
+ .try_into()
+ .unwrap()
+ {
+ return Err(Error::Message(
+ "The storage capacity of he cluster is to small. It is \
+ impossible to store partitions of size 1."
+ .into(),
+ ));
+ }
+
+ let mut s_down = 1;
+ let mut s_up = self.get_total_capacity()?;
+ while s_down + 1 < s_up {
+ g = self.generate_flow_graph((s_down + s_up) / 2, zone_to_id, &empty_set)?;
+ g.compute_maximal_flow()?;
+ if g.get_flow_value()?
+ < (nb_partitions * self.replication_factor)
+ .try_into()
+ .unwrap()
+ {
+ s_up = (s_down + s_up) / 2;
+ } else {
+ s_down = (s_down + s_up) / 2;
+ }
+ }
+
+ Ok(s_down)
+ }
+
+ fn generate_graph_vertices(nb_zones: usize, nb_nodes: usize) -> Vec<Vertex> {
+ let mut vertices = vec![Vertex::Source, Vertex::Sink];
+ for p in 0..NB_PARTITIONS {
+ vertices.push(Vertex::Pup(p));
+ vertices.push(Vertex::Pdown(p));
+ for z in 0..nb_zones {
+ vertices.push(Vertex::PZ(p, z));
+ }
+ }
+ for n in 0..nb_nodes {
+ vertices.push(Vertex::N(n));
+ }
+ vertices
+ }
+
+ fn generate_flow_graph(
+ &self,
+ size: u32,
+ zone_to_id: &HashMap<String, usize>,
+ exclude_assoc: &HashSet<(usize, usize)>,
+ ) -> Result<Graph<FlowEdge>, Error> {
+ let vertices =
+ ClusterLayout::generate_graph_vertices(zone_to_id.len(), self.useful_nodes().len());
+ let mut g = Graph::<FlowEdge>::new(&vertices);
+ let nb_zones = zone_to_id.len();
+ let redundancy = self.staged_parameters.get().zone_redundancy;
+ for p in 0..NB_PARTITIONS {
+ g.add_edge(Vertex::Source, Vertex::Pup(p), redundancy as u32)?;
+ g.add_edge(
+ Vertex::Source,
+ Vertex::Pdown(p),
+ (self.replication_factor - redundancy) as u32,
+ )?;
+ for z in 0..nb_zones {
+ g.add_edge(Vertex::Pup(p), Vertex::PZ(p, z), 1)?;
+ g.add_edge(
+ Vertex::Pdown(p),
+ Vertex::PZ(p, z),
+ self.replication_factor as u32,
+ )?;
+ }
+ }
+ for n in 0..self.useful_nodes().len() {
+ let node_capacity = self.get_node_capacity(&self.node_id_vec[n])?;
+ let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[n])?];
+ g.add_edge(Vertex::N(n), Vertex::Sink, node_capacity / size)?;
+ for p in 0..NB_PARTITIONS {
+ if !exclude_assoc.contains(&(p, n)) {
+ g.add_edge(Vertex::PZ(p, node_zone), Vertex::N(n), 1)?;
+ }
+ }
+ }
+ Ok(g)
+ }
+
+ fn compute_candidate_assignment(
+ &self,
+ zone_to_id: &HashMap<String, usize>,
+ old_assoc_opt: &Option<Vec<Vec<usize>>>,
+ ) -> Result<Graph<FlowEdge>, Error> {
+ //We list the edges that are not used in the old association
+ let mut exclude_edge = HashSet::<(usize, usize)>::new();
+ if let Some(old_assoc) = old_assoc_opt {
+ let nb_nodes = self.useful_nodes().len();
+ for (p, old_assoc_p) in old_assoc.iter().enumerate() {
+ for n in 0..nb_nodes {
+ exclude_edge.insert((p, n));
+ }
+ for n in old_assoc_p.iter() {
+ exclude_edge.remove(&(p, *n));
+ }
+ }
+ }
+
+ //We compute the best flow using only the edges used in the old assoc
+ let mut g = self.generate_flow_graph(self.partition_size, zone_to_id, &exclude_edge)?;
+ g.compute_maximal_flow()?;
+ for (p, n) in exclude_edge.iter() {
+ let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?];
+ g.add_edge(Vertex::PZ(*p, node_zone), Vertex::N(*n), 1)?;
+ }
+ g.compute_maximal_flow()?;
+ Ok(g)
+ }
+
+ fn minimize_rebalance_load(
+ &self,
+ gflow: &mut Graph<FlowEdge>,
+ zone_to_id: &HashMap<String, usize>,
+ old_assoc: &[Vec<usize>],
+ ) -> Result<(), Error> {
+ let mut cost = CostFunction::new();
+ for (p, assoc_p) in old_assoc.iter().enumerate() {
+ for n in assoc_p.iter() {
+ let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?];
+ cost.insert((Vertex::PZ(p, node_zone), Vertex::N(*n)), -1);
+ }
+ }
+ let nb_nodes = self.useful_nodes().len();
+ let path_length = 4 * nb_nodes;
+ gflow.optimize_flow_with_cost(&cost, path_length)?;
+
+ Ok(())
+ }
+
+ fn update_ring_from_flow(
+ &mut self,
+ nb_zones: usize,
+ gflow: &Graph<FlowEdge>,
+ ) -> Result<(), Error> {
+ self.ring_assignation_data = Vec::<CompactNodeType>::new();
+ for p in 0..NB_PARTITIONS {
+ for z in 0..nb_zones {
+ let assoc_vertex = gflow.get_positive_flow_from(Vertex::PZ(p, z))?;
+ for vertex in assoc_vertex.iter() {
+ if let Vertex::N(n) = vertex {
+ self.ring_assignation_data.push((*n).try_into().unwrap());
+ }
+ }
+ }
+ }
+
+ if self.ring_assignation_data.len() != NB_PARTITIONS * self.replication_factor {
+ return Err(Error::Message(
+ "Critical Error : the association ring we produced does not \
+ have the right size."
+ .into(),
+ ));
+ }
+ Ok(())
+ }
+
+ //This function returns a message summing up the partition repartition of the new
+ //layout.
+ fn output_stat(
+ &self,
+ gflow: &Graph<FlowEdge>,
+ old_assoc_opt: &Option<Vec<Vec<usize>>>,
+ zone_to_id: &HashMap<String, usize>,
+ id_to_zone: &[String],
+ ) -> Result<Message, Error> {
+ let mut msg = Message::new();
+
+ let nb_partitions = 1usize << PARTITION_BITS;
+ let used_cap = self.partition_size * nb_partitions as u32 * self.replication_factor as u32;
+ let total_cap = self.get_total_capacity()?;
+ let percent_cap = 100.0 * (used_cap as f32) / (total_cap as f32);
+ msg.push(format!(
+ "Available capacity / Total cluster capacity: {} / {} ({:.1} %)",
+ used_cap, total_cap, percent_cap
+ ));
+ msg.push("".into());
+ msg.push(
+ "If the percentage is to low, it might be that the \
replication/redundancy constraints force the use of nodes/zones with small \
storage capacities. \
You might want to rebalance the storage capacities or relax the constraints. \
- See the detailed statistics below and look for saturated nodes/zones.".into());
- msg.push(format!("Recall that because of the replication factor, the actual available \
- storage capacity is {} / {} = {}.",
- used_cap , self.replication_factor ,
- used_cap/self.replication_factor as u32));
-
- //We define and fill in the following tables
- let storing_nodes = self.useful_nodes();
- let mut new_partitions = vec![0; storing_nodes.len()];
- let mut stored_partitions = vec![0; storing_nodes.len()];
-
- let mut new_partitions_zone = vec![0; id_to_zone.len()];
- let mut stored_partitions_zone = vec![0; id_to_zone.len()];
-
- for p in 0..nb_partitions {
- for z in 0..id_to_zone.len() {
- let pz_nodes = gflow.get_positive_flow_from(Vertex::PZ(p,z))?;
- if !pz_nodes.is_empty() {
- stored_partitions_zone[z] += 1;
- if let Some(old_assoc) = old_assoc_opt {
- let mut old_zones_of_p = Vec::<usize>::new();
- for n in old_assoc[p].iter() {
- old_zones_of_p.push(
- zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?]);
- }
- if !old_zones_of_p.contains(&z) {
- new_partitions_zone[z] += 1;
- }
- }
- }
- for vert in pz_nodes.iter() {
- if let Vertex::N(n) = *vert {
- stored_partitions[n] += 1;
- if let Some(old_assoc) = old_assoc_opt {
- if !old_assoc[p].contains(&n) {
- new_partitions[n] += 1;
- }
- }
- }
- }
- }
- }
-
- if *old_assoc_opt == None {
- new_partitions = stored_partitions.clone();
- new_partitions_zone = stored_partitions_zone.clone();
- }
-
- //We display the statistics
-
- msg.push("".into());
- if *old_assoc_opt != None {
- let total_new_partitions : usize = new_partitions.iter().sum();
- msg.push(format!("A total of {} new copies of partitions need to be \
- transferred.", total_new_partitions));
- }
- msg.push("".into());
- msg.push("==== DETAILED STATISTICS BY ZONES AND NODES ====".into());
-
- for z in 0..id_to_zone.len(){
- let mut nodes_of_z = Vec::<usize>::new();
- for n in 0..storing_nodes.len(){
- if self.get_node_zone(&self.node_id_vec[n])? == id_to_zone[z] {
- nodes_of_z.push(n);
- }
- }
- let replicated_partitions : usize = nodes_of_z.iter()
- .map(|n| stored_partitions[*n]).sum();
- msg.push("".into());
-
- msg.push(format!("Zone {}: {} distinct partitions stored ({} new, \
- {} partition copies) ", id_to_zone[z], stored_partitions_zone[z],
- new_partitions_zone[z], replicated_partitions));
-
- let available_cap_z : u32 = self.partition_size*replicated_partitions as u32;
- let mut total_cap_z = 0;
- for n in nodes_of_z.iter() {
- total_cap_z += self.get_node_capacity(&self.node_id_vec[*n])?;
- }
- let percent_cap_z = 100.0*(available_cap_z as f32)/(total_cap_z as f32);
- msg.push(format!(" Available capacity / Total capacity: {}/{} ({:.1}%).",
- available_cap_z, total_cap_z, percent_cap_z));
-
- for n in nodes_of_z.iter() {
- let available_cap_n = stored_partitions[*n] as u32 *self.partition_size;
- let total_cap_n =self.get_node_capacity(&self.node_id_vec[*n])?;
- let tags_n = (self.node_role(&self.node_id_vec[*n])
- .ok_or("Node not found."))?.tags_string();
- msg.push(format!(" Node {}: {} partitions ({} new) ; \
- available/total capacity: {} / {} ({:.1}%) ; tags:{}",
- &self.node_id_vec[*n].to_vec()[0..2].to_vec().encode_hex::<String>(),
- stored_partitions[*n],
- new_partitions[*n], available_cap_n, total_cap_n,
- (available_cap_n as f32)/(total_cap_n as f32)*100.0 ,
- tags_n));
- }
- }
-
- Ok(msg)
- }
-
+ See the detailed statistics below and look for saturated nodes/zones."
+ .into(),
+ );
+ msg.push(format!(
+ "Recall that because of the replication factor, the actual available \
+ storage capacity is {} / {} = {}.",
+ used_cap,
+ self.replication_factor,
+ used_cap / self.replication_factor as u32
+ ));
+
+ //We define and fill in the following tables
+ let storing_nodes = self.useful_nodes();
+ let mut new_partitions = vec![0; storing_nodes.len()];
+ let mut stored_partitions = vec![0; storing_nodes.len()];
+
+ let mut new_partitions_zone = vec![0; id_to_zone.len()];
+ let mut stored_partitions_zone = vec![0; id_to_zone.len()];
+
+ for p in 0..nb_partitions {
+ for z in 0..id_to_zone.len() {
+ let pz_nodes = gflow.get_positive_flow_from(Vertex::PZ(p, z))?;
+ if !pz_nodes.is_empty() {
+ stored_partitions_zone[z] += 1;
+ if let Some(old_assoc) = old_assoc_opt {
+ let mut old_zones_of_p = Vec::<usize>::new();
+ for n in old_assoc[p].iter() {
+ old_zones_of_p
+ .push(zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?]);
+ }
+ if !old_zones_of_p.contains(&z) {
+ new_partitions_zone[z] += 1;
+ }
+ }
+ }
+ for vert in pz_nodes.iter() {
+ if let Vertex::N(n) = *vert {
+ stored_partitions[n] += 1;
+ if let Some(old_assoc) = old_assoc_opt {
+ if !old_assoc[p].contains(&n) {
+ new_partitions[n] += 1;
+ }
+ }
+ }
+ }
+ }
+ }
+
+ if *old_assoc_opt == None {
+ new_partitions = stored_partitions.clone();
+ new_partitions_zone = stored_partitions_zone.clone();
+ }
+
+ //We display the statistics
+
+ msg.push("".into());
+ if *old_assoc_opt != None {
+ let total_new_partitions: usize = new_partitions.iter().sum();
+ msg.push(format!(
+ "A total of {} new copies of partitions need to be \
+ transferred.",
+ total_new_partitions
+ ));
+ }
+ msg.push("".into());
+ msg.push("==== DETAILED STATISTICS BY ZONES AND NODES ====".into());
+
+ for z in 0..id_to_zone.len() {
+ let mut nodes_of_z = Vec::<usize>::new();
+ for n in 0..storing_nodes.len() {
+ if self.get_node_zone(&self.node_id_vec[n])? == id_to_zone[z] {
+ nodes_of_z.push(n);
+ }
+ }
+ let replicated_partitions: usize =
+ nodes_of_z.iter().map(|n| stored_partitions[*n]).sum();
+ msg.push("".into());
+
+ msg.push(format!(
+ "Zone {}: {} distinct partitions stored ({} new, \
+ {} partition copies) ",
+ id_to_zone[z],
+ stored_partitions_zone[z],
+ new_partitions_zone[z],
+ replicated_partitions
+ ));
+
+ let available_cap_z: u32 = self.partition_size * replicated_partitions as u32;
+ let mut total_cap_z = 0;
+ for n in nodes_of_z.iter() {
+ total_cap_z += self.get_node_capacity(&self.node_id_vec[*n])?;
+ }
+ let percent_cap_z = 100.0 * (available_cap_z as f32) / (total_cap_z as f32);
+ msg.push(format!(
+ " Available capacity / Total capacity: {}/{} ({:.1}%).",
+ available_cap_z, total_cap_z, percent_cap_z
+ ));
+
+ for n in nodes_of_z.iter() {
+ let available_cap_n = stored_partitions[*n] as u32 * self.partition_size;
+ let total_cap_n = self.get_node_capacity(&self.node_id_vec[*n])?;
+ let tags_n = (self
+ .node_role(&self.node_id_vec[*n])
+ .ok_or("Node not found."))?
+ .tags_string();
+ msg.push(format!(
+ " Node {}: {} partitions ({} new) ; \
+ available/total capacity: {} / {} ({:.1}%) ; tags:{}",
+ &self.node_id_vec[*n].to_vec()[0..2]
+ .to_vec()
+ .encode_hex::<String>(),
+ stored_partitions[*n],
+ new_partitions[*n],
+ available_cap_n,
+ total_cap_n,
+ (available_cap_n as f32) / (total_cap_n as f32) * 100.0,
+ tags_n
+ ));
+ }
+ }
+
+ Ok(msg)
+ }
}
//====================================================================================
#[cfg(test)]
mod tests {
- use super::{*,Error};
- use std::cmp::min;
-
-
- //This function checks that the partition size S computed is at least better than the
- //one given by a very naive algorithm. To do so, we try to run the naive algorithm
- //assuming a partion size of S+1. If we succed, it means that the optimal assignation
- //was not optimal. The naive algorithm is the following :
- //- we compute the max number of partitions associated to every node, capped at the
- //partition number. It gives the number of tokens of every node.
- //- every zone has a number of tokens equal to the sum of the tokens of its nodes.
- //- we cycle over the partitions and associate zone tokens while respecting the
- //zone redundancy constraint.
- //NOTE: the naive algorithm is not optimal. Counter example:
- //take nb_partition = 3 ; replication_factor = 5; redundancy = 4;
- //number of tokens by zone : (A, 4), (B,1), (C,4), (D, 4), (E, 2)
- //With these parameters, the naive algo fails, whereas there is a solution:
- //(A,A,C,D,E) , (A,B,C,D,D) (A,C,C,D,E)
- fn check_against_naive(cl: &ClusterLayout) -> Result<bool,Error> {
- let over_size = cl.partition_size +1;
- let mut zone_token = HashMap::<String, usize>::new();
- let nb_partitions = 1usize << PARTITION_BITS;
-
- let (zones, zone_to_id) = cl.generate_useful_zone_ids()?;
-
- if zones.is_empty() {
- return Ok(false);
- }
-
- for z in zones.iter() {
- zone_token.insert(z.clone(), 0);
- }
- for uuid in cl.useful_nodes().iter() {
- let z = cl.get_node_zone(uuid)?;
- let c = cl.get_node_capacity(uuid)?;
- zone_token.insert(z.clone(), zone_token[&z] + min(nb_partitions , (c/over_size) as usize));
- }
-
- //For every partition, we count the number of zone already associated and
- //the name of the last zone associated
-
- let mut id_zone_token = vec![0; zones.len()];
- for (z,t) in zone_token.iter() {
- id_zone_token[zone_to_id[z]] = *t;
- }
-
- let mut nb_token = vec![0; nb_partitions];
- let mut last_zone = vec![zones.len(); nb_partitions];
-
- let mut curr_zone = 0;
-
- let redundancy = cl.parameters.zone_redundancy;
-
- for replic in 0..cl.replication_factor {
- for p in 0..nb_partitions {
- while id_zone_token[curr_zone] == 0 ||
- (last_zone[p] == curr_zone
- && redundancy - nb_token[p] <= cl.replication_factor - replic) {
- curr_zone += 1;
- if curr_zone >= zones.len() {
- return Ok(true);
- }
- }
- id_zone_token[curr_zone] -= 1;
- if last_zone[p] != curr_zone {
- nb_token[p] += 1;
- last_zone[p] = curr_zone;
- }
- }
- }
-
- return Ok(false);
- }
-
- fn show_msg(msg : &Message) {
- for s in msg.iter(){
- println!("{}",s);
- }
- }
+ use super::{Error, *};
+ use std::cmp::min;
+
+ //This function checks that the partition size S computed is at least better than the
+ //one given by a very naive algorithm. To do so, we try to run the naive algorithm
+ //assuming a partion size of S+1. If we succed, it means that the optimal assignation
+ //was not optimal. The naive algorithm is the following :
+ //- we compute the max number of partitions associated to every node, capped at the
+ //partition number. It gives the number of tokens of every node.
+ //- every zone has a number of tokens equal to the sum of the tokens of its nodes.
+ //- we cycle over the partitions and associate zone tokens while respecting the
+ //zone redundancy constraint.
+ //NOTE: the naive algorithm is not optimal. Counter example:
+ //take nb_partition = 3 ; replication_factor = 5; redundancy = 4;
+ //number of tokens by zone : (A, 4), (B,1), (C,4), (D, 4), (E, 2)
+ //With these parameters, the naive algo fails, whereas there is a solution:
+ //(A,A,C,D,E) , (A,B,C,D,D) (A,C,C,D,E)
+ fn check_against_naive(cl: &ClusterLayout) -> Result<bool, Error> {
+ let over_size = cl.partition_size + 1;
+ let mut zone_token = HashMap::<String, usize>::new();
+ let nb_partitions = 1usize << PARTITION_BITS;
+
+ let (zones, zone_to_id) = cl.generate_useful_zone_ids()?;
+
+ if zones.is_empty() {
+ return Ok(false);
+ }
+
+ for z in zones.iter() {
+ zone_token.insert(z.clone(), 0);
+ }
+ for uuid in cl.useful_nodes().iter() {
+ let z = cl.get_node_zone(uuid)?;
+ let c = cl.get_node_capacity(uuid)?;
+ zone_token.insert(
+ z.clone(),
+ zone_token[&z] + min(nb_partitions, (c / over_size) as usize),
+ );
+ }
+
+ //For every partition, we count the number of zone already associated and
+ //the name of the last zone associated
+
+ let mut id_zone_token = vec![0; zones.len()];
+ for (z, t) in zone_token.iter() {
+ id_zone_token[zone_to_id[z]] = *t;
+ }
+
+ let mut nb_token = vec![0; nb_partitions];
+ let mut last_zone = vec![zones.len(); nb_partitions];
+
+ let mut curr_zone = 0;
+
+ let redundancy = cl.parameters.zone_redundancy;
+
+ for replic in 0..cl.replication_factor {
+ for p in 0..nb_partitions {
+ while id_zone_token[curr_zone] == 0
+ || (last_zone[p] == curr_zone
+ && redundancy - nb_token[p] <= cl.replication_factor - replic)
+ {
+ curr_zone += 1;
+ if curr_zone >= zones.len() {
+ return Ok(true);
+ }
+ }
+ id_zone_token[curr_zone] -= 1;
+ if last_zone[p] != curr_zone {
+ nb_token[p] += 1;
+ last_zone[p] = curr_zone;
+ }
+ }
+ }
+
+ return Ok(false);
+ }
+
+ fn show_msg(msg: &Message) {
+ for s in msg.iter() {
+ println!("{}", s);
+ }
+ }
fn update_layout(
cl: &mut ClusterLayout,
node_id_vec: &Vec<u8>,
node_capacity_vec: &Vec<u32>,
node_zone_vec: &Vec<String>,
- zone_redundancy: usize
+ zone_redundancy: usize,
) {
for i in 0..node_id_vec.len() {
if let Some(x) = FixedBytes32::try_from(&[i as u8; 32]) {
@@ -901,12 +1018,12 @@ mod tests {
);
cl.roles.merge(&update);
}
- cl.staged_parameters = Lww::<LayoutParameters>::new(LayoutParameters{zone_redundancy});
+ cl.staged_parameters = Lww::<LayoutParameters>::new(LayoutParameters { zone_redundancy });
}
#[test]
fn test_assignation() {
- let mut node_id_vec = vec![1, 2, 3];
+ 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()
@@ -936,11 +1053,12 @@ mod tests {
assert!(cl.check());
assert!(matches!(check_against_naive(&cl), Ok(true)));
- node_capacity_vec = vec![4000000, 4000000, 2000000, 7000000, 1000000, 9000000, 2000000, 10000, 2000000];
+ node_capacity_vec = vec![
+ 4000000, 4000000, 2000000, 7000000, 1000000, 9000000, 2000000, 10000, 2000000,
+ ];
update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 1);
show_msg(&cl.calculate_partition_assignation().unwrap());
assert!(cl.check());
assert!(matches!(check_against_naive(&cl), Ok(true)));
-
}
}
diff --git a/src/rpc/lib.rs b/src/rpc/lib.rs
index 1036a8e1..17e92dd7 100644
--- a/src/rpc/lib.rs
+++ b/src/rpc/lib.rs
@@ -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;
diff --git a/src/rpc/system.rs b/src/rpc/system.rs
index 655d21de..9e0bfa11 100644
--- a/src/rpc/system.rs
+++ b/src/rpc/system.rs
@@ -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();