forked from Deuxfleurs/garage
Added some comment
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2 changed files with 132 additions and 109 deletions
src/rpc
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@ -6,10 +6,10 @@ use std::cmp::{max, min};
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use std::collections::HashMap;
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use std::collections::VecDeque;
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//Vertex data structures used in all the graphs used in layout.rs.
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//usize parameters correspond to node/zone/partitions ids.
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//To understand the vertex roles below, please refer to the formal description
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//of the layout computation algorithm.
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///Vertex data structures used in all the graphs used in layout.rs.
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///usize parameters correspond to node/zone/partitions ids.
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///To understand the vertex roles below, please refer to the formal description
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///of the layout computation algorithm.
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#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
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pub enum Vertex {
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Source,
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@ -20,8 +20,7 @@ pub enum Vertex {
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Sink,
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}
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//Edge data structure for the flow algorithm.
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//The graph is stored as an adjacency list
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///Edge data structure for the flow algorithm.
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#[derive(Clone, Copy, Debug)]
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pub struct FlowEdge {
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cap: u32, //flow maximal capacity of the edge
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@ -30,8 +29,7 @@ pub struct FlowEdge {
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rev: usize, //index of the reversed edge (v, self) in the edge list of vertex v
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}
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//Edge data structure for the detection of negative cycles.
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//The graph is stored as a list of edges (u,v).
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///Edge data structure for the detection of negative cycles.
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#[derive(Clone, Copy, Debug)]
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pub struct WeightedEdge {
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w: i32, //weight of the edge
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@ -42,13 +40,14 @@ pub trait Edge: Clone + Copy {}
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impl Edge for FlowEdge {}
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impl Edge for WeightedEdge {}
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//Struct for the graph structure. We do encapsulation here to be able to both
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//provide user friendly Vertex enum to address vertices, and to use usize indices
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//and Vec instead of HashMap in the graph algorithm to optimize execution speed.
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///Struct for the graph structure. We do encapsulation here to be able to both
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///provide user friendly Vertex enum to address vertices, and to use internally usize
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///indices and Vec instead of HashMap in the graph algorithm to optimize execution speed.
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pub struct Graph<E: Edge> {
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vertextoid: HashMap<Vertex, usize>,
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idtovertex: Vec<Vertex>,
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//The graph is stored as an adjacency list
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graph: Vec<Vec<E>>,
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}
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@ -69,8 +68,8 @@ impl<E: Edge> Graph<E> {
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}
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impl Graph<FlowEdge> {
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//This function adds a directed edge to the graph with capacity c, and the
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//corresponding reversed edge with capacity 0.
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///This function adds a directed edge to the graph with capacity c, and the
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///corresponding reversed edge with capacity 0.
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pub fn add_edge(&mut self, u: Vertex, v: Vertex, c: u32) -> Result<(), String> {
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if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
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return Err("The graph does not contain the provided vertex.".to_string());
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@ -94,8 +93,8 @@ impl Graph<FlowEdge> {
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Ok(())
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}
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//This function returns the list of vertices that receive a positive flow from
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//vertex v.
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///This function returns the list of vertices that receive a positive flow from
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///vertex v.
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pub fn get_positive_flow_from(&self, v: Vertex) -> Result<Vec<Vertex>, String> {
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if !self.vertextoid.contains_key(&v) {
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return Err("The graph does not contain the provided vertex.".to_string());
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@ -110,7 +109,7 @@ impl Graph<FlowEdge> {
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Ok(result)
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}
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//This function returns the value of the flow incoming to v.
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///This function returns the value of the flow incoming to v.
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pub fn get_inflow(&self, v: Vertex) -> Result<i32, String> {
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if !self.vertextoid.contains_key(&v) {
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return Err("The graph does not contain the provided vertex.".to_string());
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@ -123,7 +122,7 @@ impl Graph<FlowEdge> {
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Ok(result)
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}
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//This function returns the value of the flow outgoing from v.
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///This function returns the value of the flow outgoing from v.
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pub fn get_outflow(&self, v: Vertex) -> Result<i32, String> {
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if !self.vertextoid.contains_key(&v) {
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return Err("The graph does not contain the provided vertex.".to_string());
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@ -136,14 +135,14 @@ impl Graph<FlowEdge> {
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Ok(result)
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}
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//This function computes the flow total value by computing the outgoing flow
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//from the source.
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///This function computes the flow total value by computing the outgoing flow
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///from the source.
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pub fn get_flow_value(&mut self) -> Result<i32, String> {
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self.get_outflow(Vertex::Source)
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}
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//This function shuffles the order of the edge lists. It keeps the ids of the
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//reversed edges consistent.
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///This function shuffles the order of the edge lists. It keeps the ids of the
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///reversed edges consistent.
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fn shuffle_edges(&mut self) {
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let mut rng = rand::thread_rng();
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for i in 0..self.graph.len() {
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@ -157,7 +156,7 @@ impl Graph<FlowEdge> {
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}
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}
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//Computes an upper bound of the flow n the graph
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///Computes an upper bound of the flow on the graph
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pub fn flow_upper_bound(&self) -> u32 {
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let idsource = self.vertextoid[&Vertex::Source];
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let mut flow_upper_bound = 0;
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@ -167,9 +166,9 @@ impl Graph<FlowEdge> {
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flow_upper_bound
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}
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//This function computes the maximal flow using Dinic's algorithm. It starts with
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//the flow values already present in the graph. So it is possible to add some edge to
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//the graph, compute a flow, add other edges, update the flow.
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///This function computes the maximal flow using Dinic's algorithm. It starts with
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///the flow values already present in the graph. So it is possible to add some edge to
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///the graph, compute a flow, add other edges, update the flow.
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pub fn compute_maximal_flow(&mut self) -> Result<(), String> {
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if !self.vertextoid.contains_key(&Vertex::Source) {
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return Err("The graph does not contain a source.".to_string());
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@ -270,11 +269,11 @@ impl Graph<FlowEdge> {
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Ok(())
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}
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//This function takes a flow, and a cost function on the edges, and tries to find an
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// equivalent flow with a better cost, by finding improving overflow cycles. It uses
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// as subroutine the Bellman Ford algorithm run up to path_length.
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// We assume that the cost of edge (u,v) is the opposite of the cost of (v,u), and only
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// one needs to be present in the cost function.
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///This function takes a flow, and a cost function on the edges, and tries to find an
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/// equivalent flow with a better cost, by finding improving overflow cycles. It uses
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/// as subroutine the Bellman Ford algorithm run up to path_length.
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/// We assume that the cost of edge (u,v) is the opposite of the cost of (v,u), and
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/// only one needs to be present in the cost function.
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pub fn optimize_flow_with_cost(
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&mut self,
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cost: &CostFunction,
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@ -309,7 +308,7 @@ impl Graph<FlowEdge> {
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Ok(())
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}
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//Construct the weighted graph G_f from the flow and the cost function
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///Construct the weighted graph G_f from the flow and the cost function
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fn build_cost_graph(&self, cost: &CostFunction) -> Result<Graph<WeightedEdge>, String> {
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let mut g = Graph::<WeightedEdge>::new(&self.idtovertex);
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let nb_vertices = self.idtovertex.len();
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@ -334,7 +333,7 @@ impl Graph<FlowEdge> {
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}
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impl Graph<WeightedEdge> {
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//This function adds a single directed weighted edge to the graph.
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///This function adds a single directed weighted edge to the graph.
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pub fn add_edge(&mut self, u: Vertex, v: Vertex, w: i32) -> Result<(), String> {
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if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
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return Err("The graph does not contain the provided vertex.".to_string());
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@ -345,12 +344,12 @@ impl Graph<WeightedEdge> {
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Ok(())
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}
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//This function lists the negative cycles it manages to find after path_length
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//iterations of the main loop of the Bellman-Ford algorithm. For the classical
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//algorithm, path_length needs to be equal to the number of vertices. However,
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//for particular graph structures like our case, the algorithm is still correct
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//when path_length is the length of the longest possible simple path.
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//See the formal description of the algorithm for more details.
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///This function lists the negative cycles it manages to find after path_length
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///iterations of the main loop of the Bellman-Ford algorithm. For the classical
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///algorithm, path_length needs to be equal to the number of vertices. However,
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///for particular graph structures like in our case, the algorithm is still correct
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///when path_length is the length of the longest possible simple path.
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///See the formal description of the algorithm for more details.
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fn list_negative_cycles(&self, path_length: usize) -> Vec<Vec<Vertex>> {
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let nb_vertices = self.graph.len();
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@ -384,8 +383,8 @@ impl Graph<WeightedEdge> {
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}
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}
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//This function returns the list of cycles of a directed 1 forest. It does not
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//check for the consistency of the input.
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///This function returns the list of cycles of a directed 1 forest. It does not
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///check for the consistency of the input.
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fn cycles_of_1_forest(forest: &[Option<usize>]) -> Vec<Vec<usize>> {
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let mut cycles = Vec::<Vec<usize>>::new();
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let mut time_of_discovery = vec![None; forest.len()];
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@ -17,6 +17,8 @@ use crate::ring::*;
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use std::convert::TryInto;
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const NB_PARTITIONS: usize = 1usize << PARTITION_BITS;
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//The Message type will be used to collect information on the algorithm.
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type Message = Vec<String>;
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@ -28,9 +30,11 @@ pub struct ClusterLayout {
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pub replication_factor: usize,
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//This attribute is only used to retain the previously computed partition size,
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//to know to what extent does it change with the layout update.
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///This attribute is only used to retain the previously computed partition size,
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///to know to what extent does it change with the layout update.
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pub partition_size: u32,
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///Parameters used to compute the assignation currently given by
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///ring_assignation_data
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pub parameters: LayoutParameters,
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pub roles: LwwMap<Uuid, NodeRoleV>,
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@ -48,8 +52,9 @@ pub struct ClusterLayout {
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#[serde(with = "serde_bytes")]
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pub ring_assignation_data: Vec<CompactNodeType>,
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/// Role changes which are staged for the next version of the layout
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/// Parameters to be used in the next partition assignation computation.
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pub staged_parameters: Lww<LayoutParameters>,
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/// Role changes which are staged for the next version of the layout
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pub staging: LwwMap<Uuid, NodeRoleV>,
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pub staging_hash: Hash,
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}
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@ -65,8 +70,6 @@ impl AutoCrdt for LayoutParameters {
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const WARN_IF_DIFFERENT: bool = true;
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}
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const NB_PARTITIONS: usize = 1usize << PARTITION_BITS;
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#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
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pub struct NodeRoleV(pub Option<NodeRole>);
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@ -77,12 +80,13 @@ impl AutoCrdt for NodeRoleV {
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/// The user-assigned roles of cluster nodes
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#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
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pub struct NodeRole {
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/// Datacenter at which this entry belong. This information might be used to perform a better
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/// geodistribution
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/// Datacenter at which this entry belong. This information is used to
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/// perform a better geodistribution
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pub zone: String,
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/// The (relative) capacity of the node
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/// The capacity of the node
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/// If this is set to None, the node does not participate in storing data for the system
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/// and is only active as an API gateway to other nodes
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// TODO : change the capacity to u64 and use byte unit input/output
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pub capacity: Option<u32>,
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/// A set of tags to recognize the node
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pub tags: Vec<String>,
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@ -110,6 +114,7 @@ impl NodeRole {
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}
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}
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//Implementation of the ClusterLayout methods unrelated to the assignation algorithm.
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impl ClusterLayout {
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pub fn new(replication_factor: usize) -> Self {
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//We set the default zone redundancy to be equal to the replication factor,
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@ -231,7 +236,7 @@ To know the correct value of the new layout version, invoke `garage layout show`
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}
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///Returns the uuids of the non_gateway nodes in self.node_id_vec.
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pub fn useful_nodes(&self) -> Vec<Uuid> {
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pub fn nongateway_nodes(&self) -> Vec<Uuid> {
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let mut result = Vec::<Uuid>::new();
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for uuid in self.node_id_vec.iter() {
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match self.node_role(uuid) {
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@ -291,13 +296,14 @@ To know the correct value of the new layout version, invoke `garage layout show`
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///Returns the sum of capacities of non gateway nodes in the cluster
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pub fn get_total_capacity(&self) -> Result<u32, Error> {
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let mut total_capacity = 0;
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for uuid in self.useful_nodes().iter() {
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for uuid in self.nongateway_nodes().iter() {
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total_capacity += self.get_node_capacity(uuid)?;
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}
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Ok(total_capacity)
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}
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/// Check a cluster layout for internal consistency
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/// (assignation, roles, parameters, partition size)
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/// returns true if consistent, false if error
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pub fn check(&self) -> bool {
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// Check that the hash of the staging data is correct
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@ -377,7 +383,7 @@ To know the correct value of the new layout version, invoke `garage layout show`
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//Check that the partition size stored is the one computed by the asignation
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//algorithm.
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let cl2 = self.clone();
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let (_, zone_to_id) = cl2.generate_useful_zone_ids().expect("Critical Error");
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let (_, zone_to_id) = cl2.generate_nongateway_zone_ids().expect("Critical Error");
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match cl2.compute_optimal_partition_size(&zone_to_id) {
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Ok(s) if s != self.partition_size => return false,
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Err(_) => return false,
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@ -388,6 +394,7 @@ To know the correct value of the new layout version, invoke `garage layout show`
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}
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}
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//Implementation of the ClusterLayout methods related to the assignation algorithm.
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impl ClusterLayout {
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/// This function calculates a new partition-to-node assignation.
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/// The computed assignation respects the node replication factor
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@ -397,16 +404,13 @@ impl ClusterLayout {
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/// the former assignation (if any) to minimize the amount of
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/// data to be moved.
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// Staged role changes must be merged with nodes roles before calling this function,
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// hence it must only be called from apply_staged_changes() and it is not public.
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// hence it must only be called from apply_staged_changes() and hence is not public.
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fn calculate_partition_assignation(&mut self) -> Result<Message, Error> {
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//The nodes might have been updated, some might have been deleted.
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//So we need to first update the list of nodes and retrieve the
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//assignation.
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//We update the node ids, since the node role list might have changed with the
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//changes in the layout. We retrieve the old_assignation reframed with the new ids
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//changes in the layout. We retrieve the old_assignation reframed with new ids
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let old_assignation_opt = self.update_node_id_vec()?;
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//We update the parameters
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self.parameters = self.staged_parameters.get().clone();
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let mut msg = Message::new();
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//We generate for once numerical ids for the zones of non gateway nodes,
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//to use them as indices in the flow graphs.
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let (id_to_zone, zone_to_id) = self.generate_useful_zone_ids()?;
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let (id_to_zone, zone_to_id) = self.generate_nongateway_zone_ids()?;
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let nb_useful_nodes = self.useful_nodes().len();
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if nb_useful_nodes < self.replication_factor {
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let nb_nongateway_nodes = self.nongateway_nodes().len();
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if nb_nongateway_nodes < self.replication_factor {
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return Err(Error::Message(format!(
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"The number of nodes with positive \
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capacity ({}) is smaller than the replication factor ({}).",
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nb_useful_nodes, self.replication_factor
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nb_nongateway_nodes, self.replication_factor
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)));
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}
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if id_to_zone.len() < self.parameters.zone_redundancy {
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@ -457,6 +461,7 @@ impl ClusterLayout {
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partition_size
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));
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}
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//We write the partition size.
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self.partition_size = partition_size;
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if partition_size < 100 {
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@ -467,14 +472,15 @@ impl ClusterLayout {
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);
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}
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//We compute a first flow/assignment that is heuristically close to the previous
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//assignment
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let mut gflow = self.compute_candidate_assignment(&zone_to_id, &old_assignation_opt)?;
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//We compute a first flow/assignation that is heuristically close to the previous
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//assignation
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let mut gflow = self.compute_candidate_assignation(&zone_to_id, &old_assignation_opt)?;
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if let Some(assoc) = &old_assignation_opt {
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//We minimize the distance to the previous assignment.
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//We minimize the distance to the previous assignation.
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self.minimize_rebalance_load(&mut gflow, &zone_to_id, assoc)?;
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}
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//We display statistics of the computation
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msg.append(&mut self.output_stat(
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&gflow,
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&old_assignation_opt,
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@ -538,14 +544,13 @@ impl ClusterLayout {
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// (2) We retrieve the old association
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//We rewrite the old association with the new indices. We only consider partition
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//to node assignations where the node is still in use.
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let nb_partitions = 1usize << PARTITION_BITS;
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let mut old_assignation = vec![Vec::<usize>::new(); nb_partitions];
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let mut old_assignation = vec![Vec::<usize>::new(); NB_PARTITIONS];
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if self.ring_assignation_data.is_empty() {
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//This is a new association
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return Ok(None);
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}
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if self.ring_assignation_data.len() != nb_partitions * self.replication_factor {
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if self.ring_assignation_data.len() != NB_PARTITIONS * self.replication_factor {
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return Err(Error::Message(
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"The old assignation does not have a size corresponding to \
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the old replication factor or the number of partitions."
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@ -580,11 +585,11 @@ impl ClusterLayout {
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///This function generates ids for the zone of the nodes appearing in
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///self.node_id_vec.
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fn generate_useful_zone_ids(&self) -> Result<(Vec<String>, HashMap<String, usize>), Error> {
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fn generate_nongateway_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() {
|
||||
for uuid in self.nongateway_nodes().iter() {
|
||||
if self.roles.get(uuid) == None {
|
||||
return Err(Error::Message(
|
||||
"The uuid was not found in the node roles (this should \
|
||||
|
@ -603,17 +608,16 @@ impl ClusterLayout {
|
|||
}
|
||||
|
||||
///This function computes by dichotomy the largest realizable partition size, given
|
||||
///the layout.
|
||||
///the layout roles and parameters.
|
||||
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)
|
||||
< (NB_PARTITIONS * self.replication_factor)
|
||||
.try_into()
|
||||
.unwrap()
|
||||
{
|
||||
|
@ -630,7 +634,7 @@ impl ClusterLayout {
|
|||
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)
|
||||
< (NB_PARTITIONS * self.replication_factor)
|
||||
.try_into()
|
||||
.unwrap()
|
||||
{
|
||||
|
@ -658,14 +662,21 @@ impl ClusterLayout {
|
|||
vertices
|
||||
}
|
||||
|
||||
///Generates the graph to compute the maximal flow corresponding to the optimal
|
||||
///partition assignation.
|
||||
///exclude_assoc is the set of (partition, node) association that we are forbidden
|
||||
///to use (hence we do not add the corresponding edge to the graph). This parameter
|
||||
///is used to compute a first flow that uses only edges appearing in the previous
|
||||
///assignation. This produces a solution that heuristically should be close to the
|
||||
///previous one.
|
||||
fn generate_flow_graph(
|
||||
&self,
|
||||
size: u32,
|
||||
partition_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());
|
||||
ClusterLayout::generate_graph_vertices(zone_to_id.len(), self.nongateway_nodes().len());
|
||||
let mut g = Graph::<FlowEdge>::new(&vertices);
|
||||
let nb_zones = zone_to_id.len();
|
||||
let redundancy = self.parameters.zone_redundancy;
|
||||
|
@ -685,10 +696,10 @@ impl ClusterLayout {
|
|||
)?;
|
||||
}
|
||||
}
|
||||
for n in 0..self.useful_nodes().len() {
|
||||
for n in 0..self.nongateway_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)?;
|
||||
g.add_edge(Vertex::N(n), Vertex::Sink, node_capacity / partition_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)?;
|
||||
|
@ -698,28 +709,34 @@ impl ClusterLayout {
|
|||
Ok(g)
|
||||
}
|
||||
|
||||
fn compute_candidate_assignment(
|
||||
///This function computes a first optimal assignation (in the form of a flow graph).
|
||||
fn compute_candidate_assignation(
|
||||
&self,
|
||||
zone_to_id: &HashMap<String, usize>,
|
||||
old_assoc_opt: &Option<Vec<Vec<usize>>>,
|
||||
prev_assign_opt: &Option<Vec<Vec<usize>>>,
|
||||
) -> Result<Graph<FlowEdge>, Error> {
|
||||
//We list the edges that are not used in the old association
|
||||
//We list the (partition,node) associations that are not used in the
|
||||
//previous assignation
|
||||
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() {
|
||||
if let Some(prev_assign) = prev_assign_opt {
|
||||
let nb_nodes = self.nongateway_nodes().len();
|
||||
for (p, prev_assign_p) in prev_assign.iter().enumerate() {
|
||||
for n in 0..nb_nodes {
|
||||
exclude_edge.insert((p, n));
|
||||
}
|
||||
for n in old_assoc_p.iter() {
|
||||
for n in prev_assign_p.iter() {
|
||||
exclude_edge.remove(&(p, *n));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//We compute the best flow using only the edges used in the old assoc
|
||||
//We compute the best flow using only the edges used in the previous assignation
|
||||
let mut g = self.generate_flow_graph(self.partition_size, zone_to_id, &exclude_edge)?;
|
||||
g.compute_maximal_flow()?;
|
||||
|
||||
//We add the excluded edges and compute the maximal flow with the full graph.
|
||||
//The algorithm is such that it will start with the flow that we just computed
|
||||
//and find ameliorating paths from that.
|
||||
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)?;
|
||||
|
@ -728,26 +745,35 @@ impl ClusterLayout {
|
|||
Ok(g)
|
||||
}
|
||||
|
||||
///This function updates the flow graph gflow to minimize the distance between
|
||||
///its corresponding assignation and the previous one
|
||||
fn minimize_rebalance_load(
|
||||
&self,
|
||||
gflow: &mut Graph<FlowEdge>,
|
||||
zone_to_id: &HashMap<String, usize>,
|
||||
old_assoc: &[Vec<usize>],
|
||||
prev_assign: &[Vec<usize>],
|
||||
) -> Result<(), Error> {
|
||||
//We define a cost function on the edges (pairs of vertices) corresponding
|
||||
//to the distance between the two assignations.
|
||||
let mut cost = CostFunction::new();
|
||||
for (p, assoc_p) in old_assoc.iter().enumerate() {
|
||||
for (p, assoc_p) in prev_assign.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();
|
||||
|
||||
//We compute the maximal length of a simple path in gflow. It is used in the
|
||||
//Bellman-Ford algorithm in optimize_flow_with_cost to set the number
|
||||
//of iterations.
|
||||
let nb_nodes = self.nongateway_nodes().len();
|
||||
let path_length = 4 * nb_nodes;
|
||||
gflow.optimize_flow_with_cost(&cost, path_length)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
///This function updates the assignation ring from the flow graph.
|
||||
fn update_ring_from_flow(
|
||||
&mut self,
|
||||
nb_zones: usize,
|
||||
|
@ -775,19 +801,18 @@ impl ClusterLayout {
|
|||
Ok(())
|
||||
}
|
||||
|
||||
//This function returns a message summing up the partition repartition of the new
|
||||
//layout.
|
||||
///This function returns a message summing up the partition repartition of the new
|
||||
///layout, and other statistics of the partition assignation computation.
|
||||
fn output_stat(
|
||||
&self,
|
||||
gflow: &Graph<FlowEdge>,
|
||||
old_assoc_opt: &Option<Vec<Vec<usize>>>,
|
||||
prev_assign_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 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("".into());
|
||||
|
@ -813,21 +838,21 @@ impl ClusterLayout {
|
|||
));
|
||||
|
||||
//We define and fill in the following tables
|
||||
let storing_nodes = self.useful_nodes();
|
||||
let storing_nodes = self.nongateway_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 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 {
|
||||
if let Some(prev_assign) = prev_assign_opt {
|
||||
let mut old_zones_of_p = Vec::<usize>::new();
|
||||
for n in old_assoc[p].iter() {
|
||||
for n in prev_assign[p].iter() {
|
||||
old_zones_of_p
|
||||
.push(zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?]);
|
||||
}
|
||||
|
@ -839,8 +864,8 @@ impl ClusterLayout {
|
|||
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) {
|
||||
if let Some(prev_assign) = prev_assign_opt {
|
||||
if !prev_assign[p].contains(&n) {
|
||||
new_partitions[n] += 1;
|
||||
}
|
||||
}
|
||||
|
@ -849,7 +874,7 @@ impl ClusterLayout {
|
|||
}
|
||||
}
|
||||
|
||||
if *old_assoc_opt == None {
|
||||
if *prev_assign_opt == None {
|
||||
new_partitions = stored_partitions.clone();
|
||||
new_partitions_zone = stored_partitions_zone.clone();
|
||||
}
|
||||
|
@ -857,7 +882,7 @@ impl ClusterLayout {
|
|||
//We display the statistics
|
||||
|
||||
msg.push("".into());
|
||||
if *old_assoc_opt != None {
|
||||
if *prev_assign_opt != None {
|
||||
let total_new_partitions: usize = new_partitions.iter().sum();
|
||||
msg.push(format!(
|
||||
"A total of {} new copies of partitions need to be \
|
||||
|
@ -950,9 +975,8 @@ mod tests {
|
|||
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()?;
|
||||
let (zones, zone_to_id) = cl.generate_nongateway_zone_ids()?;
|
||||
|
||||
if zones.is_empty() {
|
||||
return Ok(false);
|
||||
|
@ -961,12 +985,12 @@ mod tests {
|
|||
for z in zones.iter() {
|
||||
zone_token.insert(z.clone(), 0);
|
||||
}
|
||||
for uuid in cl.useful_nodes().iter() {
|
||||
for uuid in cl.nongateway_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),
|
||||
zone_token[&z] + min(NB_PARTITIONS, (c / over_size) as usize),
|
||||
);
|
||||
}
|
||||
|
||||
|
@ -978,15 +1002,15 @@ mod tests {
|
|||
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 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 {
|
||||
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)
|
||||
|
|
Loading…
Reference in a new issue