6 files changed, 1436 insertions, 457 deletions

diff --git a/src/rpc/Cargo.toml b/src/rpc/Cargo.toml
index f0fde7a7..69e81f38 100644
--- a/src/rpc/Cargo.toml
+++ b/src/rpc/Cargo.toml
@@ -18,10 +18,12 @@ garage_util.workspace = true
 
 arc-swap = "1.0"
 bytes = "1.0"
+bytesize = "1.1"
 gethostname = "0.2"
 hex = "0.4"
 tracing = "0.1"
 rand = "0.8"
+itertools="0.10"
 sodiumoxide = { version = "0.2.5-0", package = "kuska-sodiumoxide" }
 systemstat = "0.2.3"
 
@@ -45,7 +47,7 @@ tokio = { version = "1.0", default-features = false, features = ["rt", "rt-multi
 tokio-stream = { version = "0.1", features = ["net"] }
 opentelemetry = "0.17"
 
-netapp = { version = "0.5.2", features = ["telemetry"] }
+netapp = { version = "0.5.3", features = ["telemetry"] }
 
 [features]
 kubernetes-discovery = [ "kube", "k8s-openapi", "schemars" ]
diff --git a/src/rpc/graph_algo.rs b/src/rpc/graph_algo.rs
new file mode 100644
index 00000000..65450d64
--- /dev/null
+++ b/src/rpc/graph_algo.rs
@@ -0,0 +1,411 @@
+//! This module deals with graph algorithms.
+//! It is used in layout.rs to build the partition to node assignment.
+
+use rand::prelude::SliceRandom;
+use std::cmp::{max, min};
+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,
+}
+
+/// Edge data structure for the flow algorithm.
+#[derive(Clone, Copy, Debug)]
+pub struct FlowEdge {
+	cap: u64,    // flow maximal capacity of the edge
+	flow: i64,   // 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.
+#[derive(Clone, Copy, Debug)]
+pub struct WeightedEdge {
+	w: i64, // weight of the edge
+	dest: usize,
+}
+
+pub trait Edge: Clone + Copy {}
+impl Edge for FlowEdge {}
+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 internally usize
+/// indices and Vec instead of HashMap in the graph algorithm to optimize execution speed.
+pub struct Graph<E: Edge> {
+	vertex_to_id: HashMap<Vertex, usize>,
+	id_to_vertex: Vec<Vertex>,
+
+	// The graph is stored as an adjacency list
+	graph: Vec<Vec<E>>,
+}
+
+pub type CostFunction = HashMap<(Vertex, Vertex), i64>;
+
+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> {
+			vertex_to_id: map,
+			id_to_vertex: vertices.to_vec(),
+			graph: vec![Vec::<E>::new(); vertices.len()],
+		}
+	}
+
+	fn get_vertex_id(&self, v: &Vertex) -> Result<usize, String> {
+		self.vertex_to_id
+			.get(v)
+			.cloned()
+			.ok_or_else(|| format!("The graph does not contain vertex {:?}", v))
+	}
+}
+
+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: u64) -> Result<(), String> {
+		let idu = self.get_vertex_id(&u)?;
+		let idv = self.get_vertex_id(&v)?;
+		if idu == idv {
+			return Err("Cannot add edge from vertex to itself in flow graph".into());
+		}
+
+		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> {
+		let idv = self.get_vertex_id(&v)?;
+		let mut result = Vec::<Vertex>::new();
+		for edge in self.graph[idv].iter() {
+			if edge.flow > 0 {
+				result.push(self.id_to_vertex[edge.dest]);
+			}
+		}
+		Ok(result)
+	}
+
+	/// This function returns the value of the flow incoming to v.
+	pub fn get_inflow(&self, v: Vertex) -> Result<i64, String> {
+		let idv = self.get_vertex_id(&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<i64, String> {
+		let idv = self.get_vertex_id(&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<i64, 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 on the graph
+	pub fn flow_upper_bound(&self) -> Result<u64, String> {
+		let idsource = self.get_vertex_id(&Vertex::Source)?;
+		let mut flow_upper_bound = 0;
+		for edge in self.graph[idsource].iter() {
+			flow_upper_bound += edge.cap;
+		}
+		Ok(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> {
+		let idsource = self.get_vertex_id(&Vertex::Source)?;
+		let idsink = self.get_vertex_id(&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
+		// assignment, 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 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 i64 - 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 = Vec::new();
+
+			lifo.push((idsource, flow_upper_bound));
+
+			while let Some((id, f)) = lifo.last().cloned() {
+				if id == idsink {
+					// The DFS reached the sink, we can add a
+					// residual flow.
+					lifo.pop();
+					while let Some((id, _)) = lifo.pop() {
+						let nbd = next_nbd[id];
+						self.graph[id][nbd].flow += f as i64;
+						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 i64;
+					}
+					lifo.push((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();
+					if let Some((parent, _)) = lifo.last() {
+						next_nbd[*parent] += 1;
+					}
+					continue;
+				}
+				// else we can try to send flow from id to its nbd
+				let new_flow = min(
+					f as i64,
+					self.graph[id][nbd].cap as i64 - self.graph[id][nbd].flow,
+				) as u64;
+				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((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.vertex_to_id[&c[i]];
+					let idv = self.vertex_to_id[&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.id_to_vertex);
+		let nb_vertices = self.id_to_vertex.len();
+		for i in 0..nb_vertices {
+			for edge in self.graph[i].iter() {
+				if edge.cap as i64 - edge.flow > 0 {
+					// It is possible to send overflow through this edge
+					let u = self.id_to_vertex[i];
+					let v = self.id_to_vertex[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: i64) -> Result<(), String> {
+		let idu = self.get_vertex_id(&u)?;
+		let idv = self.get_vertex_id(&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 in 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.id_to_vertex[*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
+}
diff --git a/src/rpc/layout.rs b/src/rpc/layout.rs
index 1030e3a6..c2655e59 100644
--- a/src/rpc/layout.rs
+++ b/src/rpc/layout.rs
@@ -1,87 +1,260 @@
 use std::cmp::Ordering;
-use std::collections::{HashMap, HashSet};
+use std::collections::HashMap;
+use std::collections::HashSet;
 
-use serde::{Deserialize, Serialize};
+use bytesize::ByteSize;
+use itertools::Itertools;
 
-use garage_util::crdt::{AutoCrdt, Crdt, LwwMap};
+use garage_util::crdt::{AutoCrdt, Crdt, Lww, LwwMap};
 use garage_util::data::*;
 use garage_util::encode::nonversioned_encode;
 use garage_util::error::*;
 
+use crate::graph_algo::*;
+
 use crate::ring::*;
 
-/// The layout of the cluster, i.e. the list of roles
-/// which are assigned to each cluster node
-#[derive(Clone, Debug, Serialize, Deserialize)]
-pub struct ClusterLayout {
-	pub version: u64,
-
-	pub replication_factor: usize,
-	pub roles: LwwMap<Uuid, NodeRoleV>,
-
-	/// node_id_vec: a vector of node IDs with a role assigned
-	/// 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
-	/// 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>,
-	/// the assignation of data partitions to node, the values
-	/// are indices in node_id_vec
-	#[serde(with = "serde_bytes")]
-	pub ring_assignation_data: Vec<CompactNodeType>,
-
-	/// Role changes which are staged for the next version of the layout
-	pub staging: LwwMap<Uuid, NodeRoleV>,
-	pub staging_hash: Hash,
+use std::convert::TryInto;
+
+const NB_PARTITIONS: usize = 1usize << PARTITION_BITS;
+
+// The Message type will be used to collect information on the algorithm.
+type Message = Vec<String>;
+
+mod v08 {
+	use crate::ring::CompactNodeType;
+	use garage_util::crdt::LwwMap;
+	use garage_util::data::{Hash, Uuid};
+	use serde::{Deserialize, Serialize};
+
+	/// The layout of the cluster, i.e. the list of roles
+	/// which are assigned to each cluster node
+	#[derive(Clone, Debug, Serialize, Deserialize)]
+	pub struct ClusterLayout {
+		pub version: u64,
+
+		pub replication_factor: usize,
+		pub roles: LwwMap<Uuid, NodeRoleV>,
+
+		/// node_id_vec: a vector of node IDs with a role assigned
+		/// 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
+		/// 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>,
+		/// the assignation of data partitions to node, the values
+		/// are indices in node_id_vec
+		#[serde(with = "serde_bytes")]
+		pub ring_assignation_data: Vec<CompactNodeType>,
+
+		/// Role changes which are staged for the next version of the layout
+		pub staging: LwwMap<Uuid, NodeRoleV>,
+		pub staging_hash: Hash,
+	}
+
+	#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
+	pub struct NodeRoleV(pub Option<NodeRole>);
+
+	/// The user-assigned roles of cluster nodes
+	#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
+	pub struct NodeRole {
+		/// Datacenter at which this entry belong. This information is used to
+		/// perform a better geodistribution
+		pub zone: String,
+		/// The capacity of the node
+		/// If this is set to None, the node does not participate in storing data for the system
+		/// and is only active as an API gateway to other nodes
+		pub capacity: Option<u64>,
+		/// A set of tags to recognize the node
+		pub tags: Vec<String>,
+	}
+
+	impl garage_util::migrate::InitialFormat for ClusterLayout {}
 }
 
-impl garage_util::migrate::InitialFormat for ClusterLayout {}
+mod v09 {
+	use super::v08;
+	use crate::ring::CompactNodeType;
+	use garage_util::crdt::{Lww, LwwMap};
+	use garage_util::data::{Hash, Uuid};
+	use serde::{Deserialize, Serialize};
+	pub use v08::{NodeRole, NodeRoleV};
+
+	/// The layout of the cluster, i.e. the list of roles
+	/// which are assigned to each cluster node
+	#[derive(Clone, Debug, Serialize, Deserialize)]
+	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: u64,
+		/// Parameters used to compute the assignment currently given by
+		/// ring_assignment_data
+		pub parameters: LayoutParameters,
+
+		pub roles: LwwMap<Uuid, NodeRoleV>,
+
+		/// see comment in v08::ClusterLayout
+		pub node_id_vec: Vec<Uuid>,
+		/// see comment in v08::ClusterLayout
+		#[serde(with = "serde_bytes")]
+		pub ring_assignment_data: Vec<CompactNodeType>,
+
+		/// Parameters to be used in the next partition assignment computation.
+		pub staging_parameters: Lww<LayoutParameters>,
+		/// Role changes which are staged for the next version of the layout
+		pub staging_roles: LwwMap<Uuid, NodeRoleV>,
+		pub staging_hash: Hash,
+	}
 
-#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
-pub struct NodeRoleV(pub Option<NodeRole>);
+	/// This struct is used to set the parameters to be used in the assignment computation
+	/// algorithm. It is stored as a Crdt.
+	#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Copy, Debug, Serialize, Deserialize)]
+	pub struct LayoutParameters {
+		pub zone_redundancy: usize,
+	}
 
-impl AutoCrdt for NodeRoleV {
+	impl garage_util::migrate::Migrate for ClusterLayout {
+		const VERSION_MARKER: &'static [u8] = b"G09layout";
+
+		type Previous = v08::ClusterLayout;
+
+		fn migrate(previous: Self::Previous) -> Self {
+			use itertools::Itertools;
+			use std::collections::HashSet;
+
+			// In the old layout, capacities are in an arbitrary unit,
+			// but in the new layout they are in bytes.
+			// Here we arbitrarily multiply everything by 1G,
+			// such that 1 old capacity unit = 1GB in the new units.
+			// This is totally arbitrary and won't work for most users.
+			let cap_mul = 1024 * 1024 * 1024;
+			let roles = multiply_all_capacities(previous.roles, cap_mul);
+			let staging_roles = multiply_all_capacities(previous.staging, cap_mul);
+			let node_id_vec = previous.node_id_vec;
+
+			// Determine partition size
+			let mut tmp = previous.ring_assignation_data.clone();
+			tmp.sort();
+			let partition_size = tmp
+				.into_iter()
+				.dedup_with_count()
+				.map(|(npart, node)| {
+					roles
+						.get(&node_id_vec[node as usize])
+						.and_then(|p| p.0.as_ref().and_then(|r| r.capacity))
+						.unwrap_or(0) / npart as u64
+				})
+				.min()
+				.unwrap_or(0);
+
+			// Determine zone redundancy parameter
+			let zone_redundancy = std::cmp::min(
+				previous.replication_factor,
+				roles
+					.items()
+					.iter()
+					.filter_map(|(_, _, r)| r.0.as_ref().map(|p| p.zone.as_str()))
+					.collect::<HashSet<&str>>()
+					.len(),
+			);
+			let parameters = LayoutParameters { zone_redundancy };
+
+			let mut res = Self {
+				version: previous.version,
+				replication_factor: previous.replication_factor,
+				partition_size,
+				parameters,
+				roles,
+				node_id_vec,
+				ring_assignment_data: previous.ring_assignation_data,
+				staging_parameters: Lww::new(parameters),
+				staging_roles,
+				staging_hash: [0u8; 32].into(),
+			};
+			res.staging_hash = res.calculate_staging_hash();
+			res
+		}
+	}
+
+	fn multiply_all_capacities(
+		old_roles: LwwMap<Uuid, NodeRoleV>,
+		mul: u64,
+	) -> LwwMap<Uuid, NodeRoleV> {
+		let mut new_roles = LwwMap::new();
+		for (node, ts, role) in old_roles.items() {
+			let mut role = role.clone();
+			if let NodeRoleV(Some(NodeRole {
+				capacity: Some(ref mut cap),
+				..
+			})) = role
+			{
+				*cap = *cap * mul;
+			}
+			new_roles.merge_raw(node, *ts, &role);
+		}
+		new_roles
+	}
+}
+
+pub use v09::*;
+
+impl AutoCrdt for LayoutParameters {
 	const WARN_IF_DIFFERENT: bool = true;
 }
 
-/// The user-assigned roles of cluster nodes
-#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
-pub struct NodeRole {
-	/// Datacenter at which this entry belong. This information might be used to perform a better
-	/// geodistribution
-	pub zone: String,
-	/// The (relative) capacity of the node
-	/// If this is set to None, the node does not participate in storing data for the system
-	/// and is only active as an API gateway to other nodes
-	pub capacity: Option<u32>,
-	/// A set of tags to recognize the node
-	pub tags: Vec<String>,
+impl AutoCrdt for NodeRoleV {
+	const WARN_IF_DIFFERENT: bool = true;
 }
 
 impl NodeRole {
 	pub fn capacity_string(&self) -> String {
 		match self.capacity {
-			Some(c) => format!("{}", c),
+			Some(c) => ByteSize::b(c).to_string_as(false),
 			None => "gateway".to_string(),
 		}
 	}
+
+	pub fn tags_string(&self) -> String {
+		self.tags.join(",")
+	}
 }
 
+// Implementation of the ClusterLayout methods unrelated to the assignment algorithm.
 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 staging_parameters = Lww::<LayoutParameters>::new(parameters.clone());
+
 		let empty_lwwmap = LwwMap::new();
-		let empty_lwwmap_hash = blake2sum(&nonversioned_encode(&empty_lwwmap).unwrap()[..]);
 
-		ClusterLayout {
+		let mut ret = ClusterLayout {
 			version: 0,
 			replication_factor,
+			partition_size: 0,
 			roles: LwwMap::new(),
 			node_id_vec: Vec::new(),
-			ring_assignation_data: Vec::new(),
-			staging: empty_lwwmap,
-			staging_hash: empty_lwwmap_hash,
-		}
+			ring_assignment_data: Vec::new(),
+			parameters,
+			staging_parameters,
+			staging_roles: empty_lwwmap,
+			staging_hash: [0u8; 32].into(),
+		};
+		ret.staging_hash = ret.calculate_staging_hash();
+		ret
+	}
+
+	fn calculate_staging_hash(&self) -> Hash {
+		let hashed_tuple = (&self.staging_roles, &self.staging_parameters);
+		blake2sum(&nonversioned_encode(&hashed_tuple).unwrap()[..])
 	}
 
 	pub fn merge(&mut self, other: &ClusterLayout) -> bool {
@@ -91,9 +264,10 @@ impl ClusterLayout {
 				true
 			}
 			Ordering::Equal => {
-				self.staging.merge(&other.staging);
+				self.staging_parameters.merge(&other.staging_parameters);
+				self.staging_roles.merge(&other.staging_roles);
 
-				let new_staging_hash = blake2sum(&nonversioned_encode(&self.staging).unwrap()[..]);
+				let new_staging_hash = self.calculate_staging_hash();
 				let changed = new_staging_hash != self.staging_hash;
 
 				self.staging_hash = new_staging_hash;
@@ -104,7 +278,7 @@ impl ClusterLayout {
 		}
 	}
 
-	pub fn apply_staged_changes(mut self, version: Option<u64>) -> Result<Self, Error> {
+	pub fn apply_staged_changes(mut self, version: Option<u64>) -> Result<(Self, Message), Error> {
 		match version {
 			None => {
 				let error = r#"
@@ -120,19 +294,18 @@ To know the correct value of the new layout version, invoke `garage layout show`
 			}
 		}
 
-		self.roles.merge(&self.staging);
+		self.roles.merge(&self.staging_roles);
 		self.roles.retain(|(_, _, v)| v.0.is_some());
+		self.parameters = self.staging_parameters.get().clone();
 
-		if !self.calculate_partition_assignation() {
-			return Err(Error::Message("Could not calculate new assignation of partitions to nodes. This can happen if there are less nodes than the desired number of copies of your data (see the replication_mode configuration parameter).".into()));
-		}
+		self.staging_roles.clear();
+		self.staging_hash = self.calculate_staging_hash();
 
-		self.staging.clear();
-		self.staging_hash = blake2sum(&nonversioned_encode(&self.staging).unwrap()[..]);
+		let msg = self.calculate_partition_assignment()?;
 
 		self.version += 1;
 
-		Ok(self)
+		Ok((self, msg))
 	}
 
 	pub fn revert_staged_changes(mut self, version: Option<u64>) -> Result<Self, Error> {
@@ -151,8 +324,9 @@ To know the correct value of the new layout version, invoke `garage layout show`
 			}
 		}
 
-		self.staging.clear();
-		self.staging_hash = blake2sum(&nonversioned_encode(&self.staging).unwrap()[..]);
+		self.staging_roles.clear();
+		self.staging_parameters.update(self.parameters.clone());
+		self.staging_hash = self.calculate_staging_hash();
 
 		self.version += 1;
 
@@ -177,13 +351,81 @@ 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.
+	fn nongateway_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
+	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<u64, 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 number of partitions associated to this node in the ring
+	pub fn get_node_usage(&self, uuid: &Uuid) -> Result<usize, Error> {
+		for (i, id) in self.node_id_vec.iter().enumerate() {
+			if id == uuid {
+				let mut count = 0;
+				for nod in self.ring_assignment_data.iter() {
+					if i as u8 == *nod {
+						count += 1
+					}
+				}
+				return Ok(count);
+			}
+		}
+		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
+	fn get_total_capacity(&self) -> Result<u64, Error> {
+		let mut total_capacity = 0;
+		for uuid in self.nongateway_nodes().iter() {
+			total_capacity += self.get_node_capacity(uuid)?;
+		}
+		Ok(total_capacity)
+	}
+
 	/// Check a cluster layout for internal consistency
+	/// (assignment, roles, parameters, partition size)
 	/// returns true if consistent, false if error
-	pub fn check(&self) -> bool {
+	pub fn check(&self) -> Result<(), String> {
 		// Check that the hash of the staging data is correct
-		let staging_hash = blake2sum(&nonversioned_encode(&self.staging).unwrap()[..]);
+		let staging_hash = self.calculate_staging_hash();
 		if staging_hash != self.staging_hash {
-			return false;
+			return Err("staging_hash is incorrect".into());
 		}
 
 		// Check that node_id_vec contains the correct list of nodes
@@ -198,472 +440,794 @@ To know the correct value of the new layout version, invoke `garage layout show`
 		let mut node_id_vec = self.node_id_vec.clone();
 		node_id_vec.sort();
 		if expected_nodes != node_id_vec {
-			return false;
+			return Err(format!("node_id_vec does not contain the correct set of nodes\nnode_id_vec: {:?}\nexpected: {:?}", node_id_vec, expected_nodes));
 		}
 
-		// Check that the assignation data has the correct length
-		if self.ring_assignation_data.len() != (1 << PARTITION_BITS) * self.replication_factor {
-			return false;
+		// Check that the assignment data has the correct length
+		let expected_assignment_data_len = (1 << PARTITION_BITS) * self.replication_factor;
+		if self.ring_assignment_data.len() != expected_assignment_data_len {
+			return Err(format!(
+				"ring_assignment_data has incorrect length {} instead of {}",
+				self.ring_assignment_data.len(),
+				expected_assignment_data_len
+			));
 		}
 
 		// Check that the assigned nodes are correct identifiers
 		// of nodes that are assigned a role
 		// and that role is not the role of a gateway nodes
-		for x in self.ring_assignation_data.iter() {
+		for x in self.ring_assignment_data.iter() {
 			if *x as usize >= self.node_id_vec.len() {
-				return false;
+				return Err(format!(
+					"ring_assignment_data contains invalid node id {}",
+					*x
+				));
 			}
 			let node = self.node_id_vec[*x as usize];
 			match self.roles.get(&node) {
 				Some(NodeRoleV(Some(x))) if x.capacity.is_some() => (),
-				_ => return false,
+				_ => return Err("ring_assignment_data contains id of a gateway node".into()),
 			}
 		}
 
-		true
-	}
+		// 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_assignment_data[rf * p..rf * (p + 1)].to_vec();
+			if nodes_of_p.iter().unique().count() != rf {
+				return Err(format!("partition does not contain {} unique node ids", rf));
+			}
+			// 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.")
+				})
+				.collect::<Vec<_>>();
+			let redundancy = self.parameters.zone_redundancy;
+			if zones_of_p.iter().unique().count() < redundancy {
+				return Err(format!(
+					"nodes of partition are in less than {} distinct zones",
+					redundancy
+				));
+			}
+		}
 
-	/// Calculate an assignation of partitions to nodes
-	pub fn calculate_partition_assignation(&mut self) -> bool {
-		let (configured_nodes, zones) = self.configured_nodes_and_zones();
-		let n_zones = zones.len();
+		// 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_assignment_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];
+				let partusage = usage * self.partition_size;
+				let nodecap = self.get_node_capacity(&uuid).unwrap();
+				if partusage > nodecap {
+					return Err(format!(
+						"node usage ({}) is bigger than node capacity ({})",
+						usage * self.partition_size,
+						nodecap
+					));
+				}
+			}
+		}
 
-		println!("Calculating updated partition assignation, this may take some time...");
-		println!();
+		// Check that the partition size stored is the one computed by the asignation
+		// algorithm.
+		let cl2 = self.clone();
+		let (_, zone_to_id) = cl2.generate_nongateway_zone_ids().unwrap();
+		match cl2.compute_optimal_partition_size(&zone_to_id) {
+			Ok(s) if s != self.partition_size => {
+				return Err(format!(
+					"partition_size ({}) is different than optimal value ({})",
+					self.partition_size, s
+				))
+			}
+			Err(e) => return Err(format!("could not calculate optimal partition size: {}", e)),
+			_ => (),
+		}
 
-		// Get old partition assignation
-		let old_partitions = self.parse_assignation_data();
+		Ok(())
+	}
+}
 
-		// Start new partition assignation with nodes from old assignation where it is relevant
-		let mut partitions = old_partitions
-			.iter()
-			.map(|old_part| {
-				let mut new_part = PartitionAss::new();
-				for node in old_part.nodes.iter() {
-					if let Some(role) = node.1 {
-						if role.capacity.is_some() {
-							new_part.add(None, n_zones, node.0, role);
-						}
-					}
-				}
-				new_part
-			})
-			.collect::<Vec<_>>();
+// Implementation of the ClusterLayout methods related to the assignment algorithm.
+impl ClusterLayout {
+	/// This function calculates a new partition-to-node assignment.
+	/// The computed assignment respects the node replication factor
+	/// and the zone redundancy parameter It maximizes the capacity of a
+	/// partition (assuming all partitions have the same size).
+	/// Among such optimal assignment, it minimizes the distance to
+	/// the former assignment (if any) to minimize the amount of
+	/// data to be moved.
+	/// Staged role changes must be merged with nodes roles before calling this function,
+	/// hence it must only be called from apply_staged_changes() and hence is not public.
+	fn calculate_partition_assignment(&mut self) -> Result<Message, Error> {
+		// We update the node ids, since the node role list might have changed with the
+		// changes in the layout. We retrieve the old_assignment reframed with new ids
+		let old_assignment_opt = self.update_node_id_vec()?;
+
+		let mut msg = Message::new();
+		msg.push("==== COMPUTATION OF A NEW PARTITION ASSIGNATION ====".into());
+		msg.push("".into());
+		msg.push(format!(
+			"Partitions are \
+        replicated {} times on at least {} distinct zones.",
+			self.replication_factor, self.parameters.zone_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_nongateway_zone_ids()?;
+
+		let nb_nongateway_nodes = self.nongateway_nodes().len();
+		if nb_nongateway_nodes < self.replication_factor {
+			return Err(Error::Message(format!(
+				"The number of nodes with positive \
+            capacity ({}) is smaller than the replication factor ({}).",
+				nb_nongateway_nodes, self.replication_factor
+			)));
+		}
+		if id_to_zone.len() < self.parameters.zone_redundancy {
+			return Err(Error::Message(format!(
+				"The number of zones with non-gateway \
+            nodes ({}) is smaller than the redundancy parameter ({})",
+				id_to_zone.len(),
+				self.parameters.zone_redundancy
+			)));
+		}
 
-		// In various cases, not enough nodes will have been added for all partitions
-		// in the step above (e.g. due to node removals, or new zones being added).
-		// Here we add more nodes to make a complete (but sub-optimal) assignation,
-		// using an initial partition assignation that is calculated using the multi-dc maglev trick
-		match self.initial_partition_assignation() {
-			Some(initial_partitions) => {
-				for (part, ipart) in partitions.iter_mut().zip(initial_partitions.iter()) {
-					for _ in 0..2 {
-						for (id, info) in ipart.nodes.iter() {
-							if part.nodes.len() < self.replication_factor {
-								part.add(None, n_zones, id, info.unwrap());
-							}
-						}
-					}
-					assert!(part.nodes.len() == self.replication_factor);
-				}
-			}
-			None => {
-				// Not enough nodes in cluster to build a correct assignation.
-				// Signal it by returning an error.
-				return false;
-			}
+		// 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_assignment_opt != None {
+			msg.push(format!(
+				"Optimal size of a partition: {} (was {} in the previous layout).",
+				ByteSize::b(partition_size).to_string_as(false),
+				ByteSize::b(self.partition_size).to_string_as(false)
+			));
+		} else {
+			msg.push(format!(
+				"Given the replication and redundancy constraints, the \
+                optimal size of a partition is {}.",
+				ByteSize::b(partition_size).to_string_as(false)
+			));
+		}
+		// We write the partition size.
+		self.partition_size = partition_size;
+
+		if partition_size < 100 {
+			msg.push(
+				"WARNING: The partition size is low (< 100), make sure the capacities of your nodes are correct and are of at least a few MB"
+					.into(),
+			);
 		}
 
-		// Calculate how many partitions each node should ideally store,
-		// and how many partitions they are storing with the current assignation
-		// This defines our target for which we will optimize in the following loop.
-		let total_capacity = configured_nodes
-			.iter()
-			.map(|(_, info)| info.capacity.unwrap_or(0))
-			.sum::<u32>() as usize;
-		let total_partitions = self.replication_factor * (1 << PARTITION_BITS);
-		let target_partitions_per_node = configured_nodes
-			.iter()
-			.map(|(id, info)| {
-				(
-					*id,
-					info.capacity.unwrap_or(0) as usize * total_partitions / total_capacity,
-				)
-			})
-			.collect::<HashMap<&Uuid, usize>>();
-
-		let mut partitions_per_node = self.partitions_per_node(&partitions[..]);
-
-		println!("Target number of partitions per node:");
-		for (node, npart) in target_partitions_per_node.iter() {
-			println!("{:?}\t{}", node, npart);
-		}
-		println!();
-
-		// Shuffle partitions between nodes so that nodes will reach (or better approach)
-		// their target number of stored partitions
-		loop {
-			let mut option = None;
-			for (i, part) in partitions.iter_mut().enumerate() {
-				for (irm, (idrm, _)) in part.nodes.iter().enumerate() {
-					let errratio = |node, parts| {
-						let tgt = *target_partitions_per_node.get(node).unwrap() as f32;
-						(parts - tgt) / tgt
-					};
-					let square = |x| x * x;
-
-					let partsrm = partitions_per_node.get(*idrm).cloned().unwrap_or(0) as f32;
-
-					for (idadd, infoadd) in configured_nodes.iter() {
-						// skip replacing a node by itself
-						// and skip replacing by gateway nodes
-						if idadd == idrm || infoadd.capacity.is_none() {
-							continue;
-						}
+		// 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_assignment_opt)?;
+		if let Some(assoc) = &old_assignment_opt {
+			// We minimize the distance to the previous assignment.
+			self.minimize_rebalance_load(&mut gflow, &zone_to_id, assoc)?;
+		}
 
-						// We want to try replacing node idrm by node idadd
-						// if that brings us close to our goal.
-						let partsadd = partitions_per_node.get(*idadd).cloned().unwrap_or(0) as f32;
-						let oldcost = square(errratio(*idrm, partsrm) - errratio(*idadd, partsadd));
-						let newcost =
-							square(errratio(*idrm, partsrm - 1.) - errratio(*idadd, partsadd + 1.));
-						if newcost >= oldcost {
-							// not closer to our goal
-							continue;
-						}
-						let gain = oldcost - newcost;
+		// We display statistics of the computation
+		msg.extend(self.output_stat(&gflow, &old_assignment_opt, &zone_to_id, &id_to_zone)?);
+		msg.push("".to_string());
 
-						let mut newpart = part.clone();
+		// We update the layout structure
+		self.update_ring_from_flow(id_to_zone.len(), &gflow)?;
 
-						newpart.nodes.remove(irm);
-						if !newpart.add(None, n_zones, idadd, infoadd) {
-							continue;
-						}
-						assert!(newpart.nodes.len() == self.replication_factor);
+		if let Err(e) = self.check() {
+			return Err(Error::Message(
+				format!("Layout check returned an error: {}\nOriginal result of computation: <<<<\n{}\n>>>>", e, msg.join("\n"))
+			));
+		}
 
-						if !old_partitions[i]
-							.is_valid_transition_to(&newpart, self.replication_factor)
-						{
-							continue;
-						}
+		Ok(msg)
+	}
 
-						if option
-							.as_ref()
-							.map(|(old_gain, _, _, _, _)| gain > *old_gain)
-							.unwrap_or(true)
-						{
-							option = Some((gain, i, idadd, idrm, newpart));
-						}
-					}
-				}
-			}
-			if let Some((_gain, i, idadd, idrm, newpart)) = option {
-				*partitions_per_node.entry(idadd).or_insert(0) += 1;
-				*partitions_per_node.get_mut(idrm).unwrap() -= 1;
-				partitions[i] = newpart;
-			} else {
-				break;
-			}
+	/// The LwwMap of node roles might have changed. This function updates the node_id_vec
+	/// and returns the assignment 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_assignment
+	/// do modify assignment_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 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
+			)));
 		}
 
-		// Check we completed the assignation correctly
-		// (this is a set of checks for the algorithm's consistency)
-		assert!(partitions.len() == (1 << PARTITION_BITS));
-		assert!(partitions
+		let new_gateway_nodes: Vec<Uuid> = self
+			.roles
+			.items()
 			.iter()
-			.all(|p| p.nodes.len() == self.replication_factor));
-
-		let new_partitions_per_node = self.partitions_per_node(&partitions[..]);
-		assert!(new_partitions_per_node == partitions_per_node);
-
-		// Show statistics
-		println!("New number of partitions per node:");
-		for (node, npart) in partitions_per_node.iter() {
-			let tgt = *target_partitions_per_node.get(node).unwrap();
-			let pct = 100f32 * (*npart as f32) / (tgt as f32);
-			println!("{:?}\t{}\t({}% of {})", node, npart, pct as i32, tgt);
-		}
-		println!();
-
-		let mut diffcount = HashMap::new();
-		for (oldpart, newpart) in old_partitions.iter().zip(partitions.iter()) {
-			let nminus = oldpart.txtplus(newpart);
-			let nplus = newpart.txtplus(oldpart);
-			if nminus != "[...]" || nplus != "[...]" {
-				let tup = (nminus, nplus);
-				*diffcount.entry(tup).or_insert(0) += 1;
-			}
+			.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.extend(new_non_gateway_nodes);
+		new_node_id_vec.extend(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 assignments where the node is still in use.
+		if self.ring_assignment_data.is_empty() {
+			// This is a new association
+			return Ok(None);
 		}
-		if diffcount.is_empty() {
-			println!("No data will be moved between nodes.");
-		} else {
-			let mut diffcount = diffcount.into_iter().collect::<Vec<_>>();
-			diffcount.sort();
-			println!("Number of partitions that move:");
-			for ((nminus, nplus), npart) in diffcount {
-				println!("\t{}\t{} -> {}", npart, nminus, nplus);
-			}
+
+		if self.ring_assignment_data.len() != NB_PARTITIONS * self.replication_factor {
+			return Err(Error::Message(
+				"The old assignment 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);
 		}
-		println!();
 
-		// Calculate and save new assignation data
-		let (nodes, assignation_data) =
-			self.compute_assignation_data(&configured_nodes[..], &partitions[..]);
+		let mut old_assignment = vec![Vec::<usize>::new(); NB_PARTITIONS];
+		let rf = self.replication_factor;
 
-		self.node_id_vec = nodes;
-		self.ring_assignation_data = assignation_data;
+		for (p, old_assign_p) in old_assignment.iter_mut().enumerate() {
+			for old_id in &self.ring_assignment_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]);
+				}
+			}
+		}
 
-		true
+		// We write the ring
+		self.ring_assignment_data = Vec::<CompactNodeType>::new();
+
+		Ok(Some(old_assignment))
 	}
 
-	fn initial_partition_assignation(&self) -> Option<Vec<PartitionAss<'_>>> {
-		let (configured_nodes, zones) = self.configured_nodes_and_zones();
-		let n_zones = zones.len();
+	/// This function generates ids for the zone of the nodes appearing in
+	/// self.node_id_vec.
+	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.nongateway_nodes().iter() {
+			let r = self.node_role(uuid).unwrap();
+			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))
+	}
 
-		// Create a vector of partition indices (0 to 2**PARTITION_BITS-1)
-		let partitions_idx = (0usize..(1usize << PARTITION_BITS)).collect::<Vec<_>>();
+	/// This function computes by dichotomy the largest realizable partition size, given
+	/// the layout roles and parameters.
+	fn compute_optimal_partition_size(
+		&self,
+		zone_to_id: &HashMap<String, usize>,
+	) -> Result<u64, Error> {
+		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) as i64 {
+			return Err(Error::Message(
+				"The storage capacity of he cluster is to small. It is \
+                       impossible to store partitions of size 1."
+					.into(),
+			));
+		}
 
-		// Prepare ring
-		let mut partitions: Vec<PartitionAss> = partitions_idx
-			.iter()
-			.map(|_i| PartitionAss::new())
-			.collect::<Vec<_>>();
+		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) as i64 {
+				s_up = (s_down + s_up) / 2;
+			} else {
+				s_down = (s_down + s_up) / 2;
+			}
+		}
 
-		// Create MagLev priority queues for each node
-		let mut queues = configured_nodes
-			.iter()
-			.filter(|(_id, info)| info.capacity.is_some())
-			.map(|(node_id, node_info)| {
-				let mut parts = partitions_idx
-					.iter()
-					.map(|i| {
-						let part_data =
-							[&u16::to_be_bytes(*i as u16)[..], node_id.as_slice()].concat();
-						(*i, fasthash(&part_data[..]))
-					})
-					.collect::<Vec<_>>();
-				parts.sort_by_key(|(_i, h)| *h);
-				let parts_i = parts.iter().map(|(i, _h)| *i).collect::<Vec<_>>();
-				(node_id, node_info, parts_i, 0)
-			})
-			.collect::<Vec<_>>();
+		Ok(s_down)
+	}
 
-		let max_capacity = configured_nodes
-			.iter()
-			.filter_map(|(_, node_info)| node_info.capacity)
-			.fold(0, std::cmp::max);
-
-		// Fill up ring
-		for rep in 0..self.replication_factor {
-			queues.sort_by_key(|(ni, _np, _q, _p)| {
-				let queue_data = [&u16::to_be_bytes(rep as u16)[..], ni.as_slice()].concat();
-				fasthash(&queue_data[..])
-			});
-
-			for (_, _, _, pos) in queues.iter_mut() {
-				*pos = 0;
+	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
+	}
 
-			let mut remaining = partitions_idx.len();
-			while remaining > 0 {
-				let remaining0 = remaining;
-				for i_round in 0..max_capacity {
-					for (node_id, node_info, q, pos) in queues.iter_mut() {
-						if i_round >= node_info.capacity.unwrap() {
-							continue;
-						}
-						for (pos2, &qv) in q.iter().enumerate().skip(*pos) {
-							if partitions[qv].add(Some(rep + 1), n_zones, node_id, node_info) {
-								remaining -= 1;
-								*pos = pos2 + 1;
-								break;
-							}
-						}
-					}
-				}
-				if remaining == remaining0 {
-					// No progress made, exit
-					return None;
+	/// Generates the graph to compute the maximal flow corresponding to the optimal
+	/// partition assignment.
+	/// 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
+	/// assignment. This produces a solution that heuristically should be close to the
+	/// previous one.
+	fn generate_flow_graph(
+		&self,
+		partition_size: u64,
+		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.nongateway_nodes().len());
+		let mut g = Graph::<FlowEdge>::new(&vertices);
+		let nb_zones = zone_to_id.len();
+		let redundancy = self.parameters.zone_redundancy;
+		for p in 0..NB_PARTITIONS {
+			g.add_edge(Vertex::Source, Vertex::Pup(p), redundancy as u64)?;
+			g.add_edge(
+				Vertex::Source,
+				Vertex::Pdown(p),
+				(self.replication_factor - redundancy) as u64,
+			)?;
+			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 u64,
+				)?;
+			}
+		}
+		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 / 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)?;
 				}
 			}
 		}
-
-		Some(partitions)
+		Ok(g)
 	}
 
-	fn configured_nodes_and_zones(&self) -> (Vec<(&Uuid, &NodeRole)>, HashSet<&str>) {
-		let configured_nodes = self
-			.roles
-			.items()
-			.iter()
-			.filter(|(_id, _, info)| info.0.is_some())
-			.map(|(id, _, info)| (id, info.0.as_ref().unwrap()))
-			.collect::<Vec<(&Uuid, &NodeRole)>>();
+	/// This function computes a first optimal assignment (in the form of a flow graph).
+	fn compute_candidate_assignment(
+		&self,
+		zone_to_id: &HashMap<String, usize>,
+		prev_assign_opt: &Option<Vec<Vec<usize>>>,
+	) -> Result<Graph<FlowEdge>, Error> {
+		// We list the (partition,node) associations that are not used in the
+		// previous assignment
+		let mut exclude_edge = HashSet::<(usize, usize)>::new();
+		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 prev_assign_p.iter() {
+					exclude_edge.remove(&(p, *n));
+				}
+			}
+		}
 
-		let zones = configured_nodes
-			.iter()
-			.filter(|(_id, info)| info.capacity.is_some())
-			.map(|(_id, info)| info.zone.as_str())
-			.collect::<HashSet<&str>>();
+		// We compute the best flow using only the edges used in the previous assignment
+		let mut g = self.generate_flow_graph(self.partition_size, zone_to_id, &exclude_edge)?;
+		g.compute_maximal_flow()?;
 
-		(configured_nodes, zones)
+		// 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)?;
+		}
+		g.compute_maximal_flow()?;
+		Ok(g)
 	}
 
-	fn compute_assignation_data<'a>(
+	/// This function updates the flow graph gflow to minimize the distance between
+	/// its corresponding assignment and the previous one
+	fn minimize_rebalance_load(
 		&self,
-		configured_nodes: &[(&'a Uuid, &'a NodeRole)],
-		partitions: &[PartitionAss<'a>],
-	) -> (Vec<Uuid>, Vec<CompactNodeType>) {
-		assert!(partitions.len() == (1 << PARTITION_BITS));
-
-		// Make a canonical order for nodes
-		let mut nodes = configured_nodes
-			.iter()
-			.filter(|(_id, info)| info.capacity.is_some())
-			.map(|(id, _)| **id)
-			.collect::<Vec<_>>();
-		let nodes_rev = nodes
-			.iter()
-			.enumerate()
-			.map(|(i, id)| (*id, i as CompactNodeType))
-			.collect::<HashMap<Uuid, CompactNodeType>>();
-
-		let mut assignation_data = vec![];
-		for partition in partitions.iter() {
-			assert!(partition.nodes.len() == self.replication_factor);
-			for (id, _) in partition.nodes.iter() {
-				assignation_data.push(*nodes_rev.get(id).unwrap());
+		gflow: &mut Graph<FlowEdge>,
+		zone_to_id: &HashMap<String, 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 assignments.
+		let mut cost = CostFunction::new();
+		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);
 			}
 		}
 
-		nodes.extend(
-			configured_nodes
-				.iter()
-				.filter(|(_id, info)| info.capacity.is_none())
-				.map(|(id, _)| **id),
-		);
+		// 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)?;
 
-		(nodes, assignation_data)
+		Ok(())
 	}
 
-	fn parse_assignation_data(&self) -> Vec<PartitionAss<'_>> {
-		if self.ring_assignation_data.len() == self.replication_factor * (1 << PARTITION_BITS) {
-			// If the previous assignation data is correct, use that
-			let mut partitions = vec![];
-			for i in 0..(1 << PARTITION_BITS) {
-				let mut part = PartitionAss::new();
-				for node_i in self.ring_assignation_data
-					[i * self.replication_factor..(i + 1) * self.replication_factor]
-					.iter()
-				{
-					let node_id = &self.node_id_vec[*node_i as usize];
-
-					if let Some(NodeRoleV(Some(info))) = self.roles.get(node_id) {
-						part.nodes.push((node_id, Some(info)));
-					} else {
-						part.nodes.push((node_id, None));
+	/// This function updates the assignment ring from the flow graph.
+	fn update_ring_from_flow(
+		&mut self,
+		nb_zones: usize,
+		gflow: &Graph<FlowEdge>,
+	) -> Result<(), Error> {
+		self.ring_assignment_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_assignment_data.push((*n).try_into().unwrap());
 					}
 				}
-				partitions.push(part);
 			}
-			partitions
-		} else {
-			// Otherwise start fresh
-			(0..(1 << PARTITION_BITS))
-				.map(|_| PartitionAss::new())
-				.collect()
 		}
+
+		if self.ring_assignment_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(())
 	}
 
-	fn partitions_per_node<'a>(&self, partitions: &[PartitionAss<'a>]) -> HashMap<&'a Uuid, usize> {
-		let mut partitions_per_node = HashMap::<&Uuid, usize>::new();
-		for p in partitions.iter() {
-			for (id, _) in p.nodes.iter() {
-				*partitions_per_node.entry(*id).or_insert(0) += 1;
+	/// This function returns a message summing up the partition repartition of the new
+	/// layout, and other statistics of the partition assignment computation.
+	fn output_stat(
+		&self,
+		gflow: &Graph<FlowEdge>,
+		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 used_cap = self.partition_size * NB_PARTITIONS as u64 * self.replication_factor as u64;
+		let total_cap = self.get_total_capacity()?;
+		let percent_cap = 100.0 * (used_cap as f32) / (total_cap as f32);
+		msg.push("".into());
+		msg.push(format!(
+			"Usable capacity / Total cluster capacity: {} / {} ({:.1} %)",
+			ByteSize::b(used_cap).to_string_as(false),
+			ByteSize::b(total_cap).to_string_as(false),
+			percent_cap
+		));
+		msg.push("".into());
+		msg.push(
+			"If the percentage is too 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 {} / {} = {}.",
+			ByteSize::b(used_cap).to_string_as(false),
+			self.replication_factor,
+			ByteSize::b(used_cap / self.replication_factor as u64).to_string_as(false)
+		));
+
+		// We define and fill in the following tables
+		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 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(prev_assign) = prev_assign_opt {
+						let mut old_zones_of_p = Vec::<usize>::new();
+						for n in prev_assign[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(prev_assign) = prev_assign_opt {
+							if !prev_assign[p].contains(&n) {
+								new_partitions[n] += 1;
+							}
+						}
+					}
+				}
+			}
+		}
+
+		if *prev_assign_opt == None {
+			new_partitions = stored_partitions.clone();
+			new_partitions_zone = stored_partitions_zone.clone();
+		}
+
+		// We display the statistics
+
+		msg.push("".into());
+		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 \
+                             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: u64 = self.partition_size * replicated_partitions as u64;
+			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!(
+				"  Usable capacity / Total capacity: {} / {} ({:.1}%).",
+				ByteSize::b(available_cap_z).to_string_as(false),
+				ByteSize::b(total_cap_z).to_string_as(false),
+				percent_cap_z
+			));
+
+			for n in nodes_of_z.iter() {
+				let available_cap_n = stored_partitions[*n] as u64 * 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) ; \
+                                 usable/total capacity: {} / {} ({:.1}%) ; tags:{}",
+					self.node_id_vec[*n],
+					stored_partitions[*n],
+					new_partitions[*n],
+					ByteSize::b(available_cap_n).to_string_as(false),
+					ByteSize::b(total_cap_n).to_string_as(false),
+					(available_cap_n as f32) / (total_cap_n as f32) * 100.0,
+					tags_n
+				));
 			}
 		}
-		partitions_per_node
+
+		Ok(msg)
 	}
 }
 
-// ---- Internal structs for partition assignation in layout ----
+// ====================================================================================
+
+#[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 assignment
+	// 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 (zones, zone_to_id) = cl.generate_nongateway_zone_ids()?;
+
+		if zones.is_empty() {
+			return Ok(false);
+		}
 
-#[derive(Clone)]
-struct PartitionAss<'a> {
-	nodes: Vec<(&'a Uuid, Option<&'a NodeRole>)>,
-}
+		for z in zones.iter() {
+			zone_token.insert(z.clone(), 0);
+		}
+		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),
+			);
+		}
 
-impl<'a> PartitionAss<'a> {
-	fn new() -> Self {
-		Self { nodes: Vec::new() }
-	}
+		// For every partition, we count the number of zone already associated and
+		// the name of the last zone associated
 
-	fn nplus(&self, other: &PartitionAss<'a>) -> usize {
-		self.nodes
-			.iter()
-			.filter(|x| !other.nodes.contains(x))
-			.count()
-	}
+		let mut id_zone_token = vec![0; zones.len()];
+		for (z, t) in zone_token.iter() {
+			id_zone_token[zone_to_id[z]] = *t;
+		}
 
-	fn txtplus(&self, other: &PartitionAss<'a>) -> String {
-		let mut nodes = self
-			.nodes
-			.iter()
-			.filter(|x| !other.nodes.contains(x))
-			.map(|x| format!("{:?}", x.0))
-			.collect::<Vec<_>>();
-		nodes.sort();
-		if self.nodes.iter().any(|x| other.nodes.contains(x)) {
-			nodes.push("...".into());
+		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;
+				}
+			}
 		}
-		format!("[{}]", nodes.join(" "))
-	}
 
-	fn is_valid_transition_to(&self, other: &PartitionAss<'a>, replication_factor: usize) -> bool {
-		let min_keep_nodes_per_part = (replication_factor + 1) / 2;
-		let n_removed = self.nplus(other);
+		return Ok(false);
+	}
 
-		if self.nodes.len() <= min_keep_nodes_per_part {
-			n_removed == 0
-		} else {
-			n_removed <= self.nodes.len() - min_keep_nodes_per_part
+	fn show_msg(msg: &Message) {
+		for s in msg.iter() {
+			println!("{}", s);
 		}
 	}
 
-	// add is a key function in creating a PartitionAss, i.e. the list of nodes
-	// to which a partition is assigned. It tries to add a certain node id to the
-	// assignation, but checks that doing so is compatible with the NECESSARY
-	// condition that the partition assignation must be dispersed over different
-	// zones (datacenters) if enough zones exist. This is why it takes a n_zones
-	// parameter, which is the total number of zones that have existing nodes:
-	// if nodes in the assignation already cover all n_zones zones, then any node
-	// that is not yet in the assignation can be added. Otherwise, only nodes
-	// that are in a new zone can be added.
-	fn add(
-		&mut self,
-		target_len: Option<usize>,
-		n_zones: usize,
-		node: &'a Uuid,
-		role: &'a NodeRole,
-	) -> bool {
-		if let Some(tl) = target_len {
-			if self.nodes.len() != tl - 1 {
-				return false;
+	fn update_layout(
+		cl: &mut ClusterLayout,
+		node_id_vec: &Vec<u8>,
+		node_capacity_vec: &Vec<u64>,
+		node_zone_vec: &Vec<String>,
+		zone_redundancy: usize,
+	) {
+		for i in 0..node_id_vec.len() {
+			if let Some(x) = FixedBytes32::try_from(&[i as u8; 32]) {
+				cl.node_id_vec.push(x);
 			}
-		}
 
-		let p_zns = self
-			.nodes
-			.iter()
-			.map(|(_id, info)| info.unwrap().zone.as_str())
-			.collect::<HashSet<&str>>();
-		if (p_zns.len() < n_zones && !p_zns.contains(&role.zone.as_str()))
-			|| (p_zns.len() == n_zones && !self.nodes.iter().any(|(id, _)| *id == node))
-		{
-			self.nodes.push((node, Some(role)));
-			true
-		} else {
-			false
+			let update = cl.staging_roles.update_mutator(
+				cl.node_id_vec[i],
+				NodeRoleV(Some(NodeRole {
+					zone: (node_zone_vec[i].to_string()),
+					capacity: (Some(node_capacity_vec[i])),
+					tags: (vec![]),
+				})),
+			);
+			cl.staging_roles.merge(&update);
 		}
+		cl.staging_parameters
+			.update(LayoutParameters { zone_redundancy });
+		cl.staging_hash = cl.calculate_staging_hash();
+	}
+
+	#[test]
+	fn test_assignment() {
+		let mut node_id_vec = vec![1, 2, 3];
+		let mut node_capacity_vec = vec![4000, 1000, 2000];
+		let mut node_zone_vec = vec!["A", "B", "C"]
+			.into_iter()
+			.map(|x| x.to_string())
+			.collect();
+
+		let mut cl = ClusterLayout::new(3);
+		update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 3);
+		let v = cl.version;
+		let (mut cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+		show_msg(&msg);
+		assert_eq!(cl.check(), Ok(()));
+		assert!(matches!(check_against_naive(&cl), Ok(true)));
+
+		node_id_vec = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
+		node_capacity_vec = vec![4000, 1000, 1000, 3000, 1000, 1000, 2000, 10000, 2000];
+		node_zone_vec = vec!["A", "B", "C", "C", "C", "B", "G", "H", "I"]
+			.into_iter()
+			.map(|x| x.to_string())
+			.collect();
+		update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 2);
+		let v = cl.version;
+		let (mut cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+		show_msg(&msg);
+		assert_eq!(cl.check(), Ok(()));
+		assert!(matches!(check_against_naive(&cl), Ok(true)));
+
+		node_capacity_vec = vec![4000, 1000, 2000, 7000, 1000, 1000, 2000, 10000, 2000];
+		update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 3);
+		let v = cl.version;
+		let (mut cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+		show_msg(&msg);
+		assert_eq!(cl.check(), Ok(()));
+		assert!(matches!(check_against_naive(&cl), Ok(true)));
+
+		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);
+		let v = cl.version;
+		let (cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+		show_msg(&msg);
+		assert_eq!(cl.check(), Ok(()));
+		assert!(matches!(check_against_naive(&cl), Ok(true)));
 	}
 }
diff --git a/src/rpc/lib.rs b/src/rpc/lib.rs
index 5aec92c0..a5f8fc6e 100644
--- a/src/rpc/lib.rs
+++ b/src/rpc/lib.rs
@@ -11,6 +11,7 @@ mod consul;
 #[cfg(feature = "kubernetes-discovery")]
 mod kubernetes;
 
+pub mod graph_algo;
 pub mod layout;
 pub mod replication_mode;
 pub mod ring;
diff --git a/src/rpc/ring.rs b/src/rpc/ring.rs
index 73a126a2..6a2e5c72 100644
--- a/src/rpc/ring.rs
+++ b/src/rpc/ring.rs
@@ -40,6 +40,7 @@ pub struct Ring {
 // Type to store compactly the id of a node in the system
 // Change this to u16 the day we want to have more than 256 nodes in a cluster
 pub type CompactNodeType = u8;
+pub const MAX_NODE_NUMBER: usize = 256;
 
 // The maximum number of times an object might get replicated
 // This must be at least 3 because Garage supports 3-way replication
@@ -62,12 +63,12 @@ struct RingEntry {
 impl Ring {
 	pub(crate) fn new(layout: ClusterLayout, replication_factor: usize) -> Self {
 		if replication_factor != layout.replication_factor {
-			warn!("Could not build ring: replication factor does not match between local configuration and network role assignation.");
+			warn!("Could not build ring: replication factor does not match between local configuration and network role assignment.");
 			return Self::empty(layout, replication_factor);
 		}
 
-		if layout.ring_assignation_data.len() != replication_factor * (1 << PARTITION_BITS) {
-			warn!("Could not build ring: network role assignation data has invalid length");
+		if layout.ring_assignment_data.len() != replication_factor * (1 << PARTITION_BITS) {
+			warn!("Could not build ring: network role assignment data has invalid length");
 			return Self::empty(layout, replication_factor);
 		}
 
@@ -77,7 +78,7 @@ impl Ring {
 				let top = (i as u16) << (16 - PARTITION_BITS);
 				let mut nodes_buf = [0u8; MAX_REPLICATION];
 				nodes_buf[..replication_factor].copy_from_slice(
-					&layout.ring_assignation_data
+					&layout.ring_assignment_data
 						[replication_factor * i..replication_factor * (i + 1)],
 				);
 				RingEntry {
diff --git a/src/rpc/system.rs b/src/rpc/system.rs
index b42e49fc..c549d8fc 100644
--- a/src/rpc/system.rs
+++ b/src/rpc/system.rs
@@ -666,9 +666,9 @@ impl System {
 		let update_ring = self.update_ring.lock().await;
 		let mut layout: ClusterLayout = self.ring.borrow().layout.clone();
 
-		let prev_layout_check = layout.check();
+		let prev_layout_check = layout.check().is_ok();
 		if layout.merge(adv) {
-			if prev_layout_check && !layout.check() {
+			if prev_layout_check && !layout.check().is_ok() {
 				error!("New cluster layout is invalid, discarding.");
 				return Err(Error::Message(
 					"New cluster layout is invalid, discarding.".into(),
@@ -724,7 +724,7 @@ impl System {
 
 	async fn discovery_loop(self: &Arc<Self>, mut stop_signal: watch::Receiver<bool>) {
 		while !*stop_signal.borrow() {
-			let not_configured = !self.ring.borrow().layout.check();
+			let not_configured = !self.ring.borrow().layout.check().is_ok();
 			let no_peers = self.fullmesh.get_peer_list().len() < self.replication_factor;
 			let expected_n_nodes = self.ring.borrow().layout.num_nodes();
 			let bad_peers = self