MABEAM Cluster Distribution: Scaling Multi-Agent Systems Across the BEAM
Overview
MABEAM’s cluster distribution capabilities enable multi-agent variable orchestration to scale across multiple BEAM nodes, leveraging Erlang’s distributed computing strengths for truly distributed cognitive control planes. This transforms local multi-agent systems into cluster-wide intelligent networks.
Distributed Architecture
Foundation.MABEAM.Cluster - Cluster Management
defmodule Foundation.MABEAM.Cluster do
@moduledoc """
Cluster-wide coordination and management for distributed MABEAM systems.
Provides node discovery, agent migration, distributed variable synchronization,
and cluster-aware fault tolerance for multi-agent systems.
"""
use GenServer
alias Foundation.MABEAM.{Core, Types}
alias Foundation.{Events, Telemetry}
@type cluster_state :: %{
nodes: %{node() => node_info()},
distributed_variables: %{atom() => distributed_variable()},
agent_locations: %{atom() => node()},
migration_queue: [migration_request()],
cluster_topology: cluster_topology(),
partition_detector: pid(),
consensus_coordinator: pid()
}
@type node_info :: %{
status: node_status(),
capabilities: [atom()],
resource_capacity: Types.resource_allocation(),
current_load: float(),
agent_count: non_neg_integer(),
last_heartbeat: DateTime.t()
}
@type node_status :: :active | :joining | :leaving | :partitioned | :failed
@type distributed_variable :: %{
id: atom(),
master_node: node(),
replica_nodes: [node()],
consistency_level: consistency_level(),
synchronization_strategy: sync_strategy(),
conflict_resolution: conflict_resolution_strategy()
}
@type consistency_level :: :eventual | :strong | :causal | :session
@type sync_strategy :: :gossip | :leader_follower | :multi_master | :blockchain
@type conflict_resolution_strategy :: :last_writer_wins | :vector_clocks | :consensus | :merge_function
## Public API
@doc """
Initialize cluster management for MABEAM.
"""
@spec start_link(keyword()) :: GenServer.on_start()
def start_link(opts \\ []) do
GenServer.start_link(__MODULE__, opts, name: __MODULE__)
end
@doc """
Join a MABEAM cluster.
"""
@spec join_cluster([node()]) :: :ok | {:error, term()}
def join_cluster(seed_nodes) do
GenServer.call(__MODULE__, {:join_cluster, seed_nodes})
end
@doc """
Distribute an orchestration variable across cluster nodes.
"""
@spec distribute_variable(Types.orchestration_variable(), keyword()) ::
{:ok, distributed_variable()} | {:error, term()}
def distribute_variable(variable, opts \\ []) do
GenServer.call(__MODULE__, {:distribute_variable, variable, opts})
end
@doc """
Migrate an agent to a different node.
"""
@spec migrate_agent(atom(), node()) :: {:ok, pid()} | {:error, term()}
def migrate_agent(agent_id, target_node) do
GenServer.call(__MODULE__, {:migrate_agent, agent_id, target_node})
end
@doc """
Get cluster-wide status and health information.
"""
@spec cluster_status() :: {:ok, cluster_status()} | {:error, term()}
def cluster_status() do
GenServer.call(__MODULE__, :cluster_status)
end
@doc """
Handle network partition detection and recovery.
"""
@spec handle_partition([node()]) :: :ok
def handle_partition(partitioned_nodes) do
GenServer.cast(__MODULE__, {:partition_detected, partitioned_nodes})
end
## GenServer Implementation
@impl true
def init(opts) do
# Set up cluster monitoring
:net_kernel.monitor_nodes(true, [:nodedown_reason])
# Initialize partition detector
{:ok, partition_detector} = start_partition_detector(opts)
# Initialize consensus coordinator
{:ok, consensus_coordinator} = start_consensus_coordinator(opts)
state = %{
nodes: %{node() => create_local_node_info()},
distributed_variables: %{},
agent_locations: %{},
migration_queue: [],
cluster_topology: :ring, # Default topology
partition_detector: partition_detector,
consensus_coordinator: consensus_coordinator
}
# Set up telemetry
setup_cluster_telemetry()
{:ok, state}
end
@impl true
def handle_call({:join_cluster, seed_nodes}, _from, state) do
case attempt_cluster_join(seed_nodes, state) do
{:ok, new_state} ->
# Announce our presence to the cluster
broadcast_node_announcement(new_state)
{:reply, :ok, new_state}
{:error, reason} ->
{:reply, {:error, reason}, state}
end
end
@impl true
def handle_call({:distribute_variable, variable, opts}, _from, state) do
case create_distributed_variable(variable, opts, state) do
{:ok, distributed_var, new_state} ->
# Synchronize variable across selected nodes
:ok = synchronize_variable_to_replicas(distributed_var)
{:reply, {:ok, distributed_var}, new_state}
{:error, reason} ->
{:reply, {:error, reason}, state}
end
end
@impl true
def handle_call({:migrate_agent, agent_id, target_node}, _from, state) do
case perform_agent_migration(agent_id, target_node, state) do
{:ok, new_pid, new_state} ->
{:reply, {:ok, new_pid}, new_state}
{:error, reason} ->
{:reply, {:error, reason}, state}
end
end
@impl true
def handle_info({:nodedown, node, reason}, state) do
# Handle node failure
new_state = handle_node_failure(node, reason, state)
{:noreply, new_state}
end
@impl true
def handle_info({:nodeup, node}, state) do
# Handle node joining
new_state = handle_node_joining(node, state)
{:noreply, new_state}
end
## Private Implementation
defp attempt_cluster_join(seed_nodes, state) do
# Try to connect to seed nodes
connected_nodes = Enum.filter(seed_nodes, fn node ->
Node.ping(node) == :pong
end)
case connected_nodes do
[] -> {:error, :no_reachable_seed_nodes}
nodes ->
# Request cluster information from connected nodes
cluster_info = request_cluster_info(nodes)
# Update our state with cluster information
new_state = merge_cluster_info(state, cluster_info)
{:ok, new_state}
end
end
defp create_distributed_variable(variable, opts, state) do
replication_factor = Keyword.get(opts, :replication_factor, 3)
consistency_level = Keyword.get(opts, :consistency_level, :eventual)
# Select nodes for replication
available_nodes = Map.keys(state.nodes)
replica_nodes = select_replica_nodes(available_nodes, replication_factor, opts)
distributed_var = %{
id: variable.id,
master_node: node(), # Current node is master
replica_nodes: replica_nodes,
consistency_level: consistency_level,
synchronization_strategy: Keyword.get(opts, :sync_strategy, :gossip),
conflict_resolution: Keyword.get(opts, :conflict_resolution, :vector_clocks)
}
new_state = %{state |
distributed_variables: Map.put(state.distributed_variables, variable.id, distributed_var)
}
{:ok, distributed_var, new_state}
end
defp perform_agent_migration(agent_id, target_node, state) do
case Map.get(state.agent_locations, agent_id) do
nil -> {:error, :agent_not_found}
current_node when current_node == target_node -> {:error, :already_on_target_node}
current_node ->
# Perform hot migration
case hot_migrate_agent(agent_id, current_node, target_node) do
{:ok, new_pid} ->
new_state = %{state |
agent_locations: Map.put(state.agent_locations, agent_id, target_node)
}
# Emit migration event
Events.emit(:mabeam_agent_migrated, %{
agent_id: agent_id,
from_node: current_node,
to_node: target_node,
new_pid: new_pid
})
{:ok, new_pid, new_state}
{:error, reason} ->
{:error, reason}
end
end
end
defp hot_migrate_agent(agent_id, source_node, target_node) do
# 1. Serialize agent state on source node
case :rpc.call(source_node, Foundation.MABEAM.AgentRegistry, :serialize_agent, [agent_id]) do
{:ok, serialized_state} ->
# 2. Start agent on target node with serialized state
case :rpc.call(target_node, Foundation.MABEAM.AgentRegistry, :start_agent_from_state,
[agent_id, serialized_state]) do
{:ok, new_pid} ->
# 3. Stop agent on source node
:rpc.call(source_node, Foundation.MABEAM.AgentRegistry, :stop_agent, [agent_id])
{:ok, new_pid}
{:error, reason} ->
{:error, {:failed_to_start_on_target, reason}}
end
{:error, reason} ->
{:error, {:failed_to_serialize, reason}}
end
end
end
Distributed Variable Synchronization
defmodule Foundation.MABEAM.Cluster.VariableSync do
@moduledoc """
Distributed variable synchronization across cluster nodes.
Implements various consistency models and conflict resolution strategies
for maintaining variable state across the cluster.
"""
@doc """
Synchronize a variable update across all replica nodes.
"""
@spec synchronize_update(atom(), term(), keyword()) :: :ok | {:error, term()}
def synchronize_update(variable_id, new_value, opts \\ []) do
consistency_level = Keyword.get(opts, :consistency_level, :eventual)
case consistency_level do
:eventual -> eventual_consistency_update(variable_id, new_value, opts)
:strong -> strong_consistency_update(variable_id, new_value, opts)
:causal -> causal_consistency_update(variable_id, new_value, opts)
:session -> session_consistency_update(variable_id, new_value, opts)
end
end
defp eventual_consistency_update(variable_id, new_value, opts) do
# Use gossip protocol for eventual consistency
replica_nodes = get_replica_nodes(variable_id)
# Create update message with vector clock
update_msg = %{
variable_id: variable_id,
value: new_value,
timestamp: System.monotonic_time(:microsecond),
vector_clock: generate_vector_clock(variable_id),
source_node: node()
}
# Gossip to random subset of replicas
gossip_fanout = Keyword.get(opts, :gossip_fanout, 3)
target_nodes = Enum.take_random(replica_nodes, gossip_fanout)
Enum.each(target_nodes, fn target_node ->
:rpc.cast(target_node, __MODULE__, :handle_gossip_update, [update_msg])
end)
:ok
end
defp strong_consistency_update(variable_id, new_value, opts) do
# Use consensus protocol for strong consistency
replica_nodes = get_replica_nodes(variable_id)
# Propose update to all replicas
proposal = %{
variable_id: variable_id,
value: new_value,
proposal_id: generate_proposal_id(),
proposer: node()
}
# Collect acknowledgments
acks = collect_consensus_acks(replica_nodes, proposal, opts)
# Check for majority
majority_threshold = div(length(replica_nodes), 2) + 1
if length(acks) >= majority_threshold do
# Commit the update
commit_update(replica_nodes, proposal)
:ok
else
{:error, :insufficient_consensus}
end
end
@doc """
Handle incoming gossip update from another node.
"""
def handle_gossip_update(update_msg) do
# Check if this update is newer using vector clocks
case is_newer_update?(update_msg) do
true ->
# Apply the update locally
apply_variable_update(update_msg.variable_id, update_msg.value)
# Continue gossiping to other nodes
continue_gossip(update_msg)
false ->
# Ignore stale update
:ignored
end
end
@doc """
Resolve conflicts when multiple nodes update the same variable.
"""
@spec resolve_conflict(atom(), [variable_update()]) :: {:ok, term()} | {:error, term()}
def resolve_conflict(variable_id, conflicting_updates) do
strategy = get_conflict_resolution_strategy(variable_id)
case strategy do
:last_writer_wins -> resolve_by_timestamp(conflicting_updates)
:vector_clocks -> resolve_by_vector_clocks(conflicting_updates)
:consensus -> resolve_by_consensus(variable_id, conflicting_updates)
:merge_function -> resolve_by_merge_function(variable_id, conflicting_updates)
end
end
defp resolve_by_vector_clocks(conflicting_updates) do
# Find the update with the most recent vector clock
latest_update = Enum.max_by(conflicting_updates, fn update ->
vector_clock_compare(update.vector_clock)
end)
{:ok, latest_update.value}
end
defp resolve_by_consensus(variable_id, conflicting_updates) do
# Use cluster consensus to resolve conflict
replica_nodes = get_replica_nodes(variable_id)
# Propose each conflicting value and vote
votes = Enum.map(conflicting_updates, fn update ->
vote_count = collect_conflict_votes(replica_nodes, update)
{update, vote_count}
end)
# Select the value with most votes
{winning_update, _vote_count} = Enum.max_by(votes, fn {_update, count} -> count end)
{:ok, winning_update.value}
end
end
Cluster-Aware Agent Load Balancing
defmodule Foundation.MABEAM.Cluster.LoadBalancer do
@moduledoc """
Intelligent load balancing for agents across cluster nodes.
Considers node capabilities, current load, network topology,
and agent requirements for optimal placement.
"""
@doc """
Find the optimal node for placing a new agent.
"""
@spec find_optimal_node(Types.agent_config(), keyword()) :: {:ok, node()} | {:error, term()}
def find_optimal_node(agent_config, opts \\ []) do
available_nodes = get_available_nodes()
# Score each node based on multiple factors
scored_nodes = Enum.map(available_nodes, fn node ->
score = calculate_node_score(node, agent_config, opts)
{node, score}
end)
# Select the highest scoring node
case Enum.max_by(scored_nodes, fn {_node, score} -> score end, fn -> nil end) do
nil -> {:error, :no_suitable_nodes}
{optimal_node, _score} -> {:ok, optimal_node}
end
end
@doc """
Rebalance agents across cluster nodes based on current load.
"""
@spec rebalance_cluster() :: {:ok, [migration_plan()]} | {:error, term()}
def rebalance_cluster() do
# Get current cluster state
cluster_state = get_cluster_state()
# Calculate optimal agent distribution
optimal_distribution = calculate_optimal_distribution(cluster_state)
# Generate migration plan
migration_plan = generate_migration_plan(cluster_state, optimal_distribution)
# Execute migrations
execute_migration_plan(migration_plan)
end
defp calculate_node_score(node, agent_config, opts) do
# Base score factors
resource_score = calculate_resource_score(node, agent_config)
load_score = calculate_load_score(node)
capability_score = calculate_capability_score(node, agent_config)
network_score = calculate_network_score(node, opts)
affinity_score = calculate_affinity_score(node, agent_config, opts)
# Weighted combination
weights = Keyword.get(opts, :scoring_weights, %{
resource: 0.3,
load: 0.25,
capability: 0.2,
network: 0.15,
affinity: 0.1
})
resource_score * weights.resource +
load_score * weights.load +
capability_score * weights.capability +
network_score * weights.network +
affinity_score * weights.affinity
end
defp calculate_resource_score(node, agent_config) do
node_resources = get_node_resources(node)
required_resources = agent_config.resource_requirements
# Check if node has sufficient resources
resource_ratios = [
node_resources.memory / required_resources.memory,
node_resources.cpu_weight / required_resources.cpu_weight
]
# Return minimum ratio (bottleneck resource)
Enum.min(resource_ratios)
end
defp calculate_load_score(node) do
current_load = get_node_load(node)
# Higher load = lower score
max(0.0, 1.0 - current_load)
end
defp calculate_capability_score(node, agent_config) do
node_capabilities = get_node_capabilities(node)
required_capabilities = Map.get(agent_config, :required_capabilities, [])
# Check if node supports all required capabilities
if Enum.all?(required_capabilities, &(&1 in node_capabilities)) do
1.0
else
0.0
end
end
defp calculate_network_score(node, opts) do
# Consider network latency and bandwidth to other nodes
preferred_nodes = Keyword.get(opts, :preferred_nodes, [])
if node in preferred_nodes do
1.0
else
# Calculate based on network topology
network_distance = calculate_network_distance(node, preferred_nodes)
max(0.0, 1.0 - network_distance / 10.0) # Normalize distance
end
end
defp calculate_affinity_score(node, agent_config, opts) do
# Consider agent affinity and anti-affinity rules
affinity_rules = Map.get(agent_config, :affinity_rules, [])
anti_affinity_rules = Map.get(agent_config, :anti_affinity_rules, [])
affinity_score = calculate_affinity_match(node, affinity_rules)
anti_affinity_penalty = calculate_anti_affinity_penalty(node, anti_affinity_rules)
affinity_score - anti_affinity_penalty
end
end
Cluster Fault Tolerance
defmodule Foundation.MABEAM.Cluster.FaultTolerance do
@moduledoc """
Advanced fault tolerance mechanisms for distributed MABEAM systems.
Handles node failures, network partitions, and split-brain scenarios
while maintaining agent availability and data consistency.
"""
@doc """
Handle node failure and initiate recovery procedures.
"""
@spec handle_node_failure(node(), term()) :: :ok
def handle_node_failure(failed_node, failure_reason) do
# 1. Identify affected agents
affected_agents = get_agents_on_node(failed_node)
# 2. Initiate agent recovery
Enum.each(affected_agents, &recover_agent/1)
# 3. Update distributed variables
update_variable_replicas_after_failure(failed_node)
# 4. Rebalance remaining agents
schedule_rebalancing()
# 5. Emit failure event
Foundation.Events.emit(:mabeam_node_failed, %{
node: failed_node,
reason: failure_reason,
affected_agents: length(affected_agents),
recovery_initiated: true
})
:ok
end
@doc """
Handle network partition and implement split-brain prevention.
"""
@spec handle_network_partition([node()]) :: :ok
def handle_network_partition(partitioned_nodes) do
# Determine which partition has quorum
total_nodes = get_total_cluster_nodes()
our_partition_size = length(Node.list()) + 1 # Include current node
has_quorum = our_partition_size > div(total_nodes, 2)
if has_quorum do
# We have quorum - continue operating
continue_with_quorum(partitioned_nodes)
else
# No quorum - enter read-only mode
enter_readonly_mode(partitioned_nodes)
end
end
defp recover_agent(agent_id) do
# Find the best node for agent recovery
case Foundation.MABEAM.Cluster.LoadBalancer.find_optimal_node(get_agent_config(agent_id)) do
{:ok, recovery_node} ->
# Attempt to recover agent on the selected node
case recover_agent_on_node(agent_id, recovery_node) do
{:ok, new_pid} ->
# Update agent location
Foundation.MABEAM.Cluster.update_agent_location(agent_id, recovery_node)
# Emit recovery event
Foundation.Events.emit(:mabeam_agent_recovered, %{
agent_id: agent_id,
recovery_node: recovery_node,
new_pid: new_pid
})
{:error, reason} ->
# Recovery failed - try next best node
schedule_agent_recovery_retry(agent_id, reason)
end
{:error, :no_suitable_nodes} ->
# No suitable nodes available - agent remains unavailable
mark_agent_unavailable(agent_id)
end
end
defp continue_with_quorum(partitioned_nodes) do
# Mark partitioned nodes as temporarily unavailable
Enum.each(partitioned_nodes, &mark_node_partitioned/1)
# Continue normal operations with remaining nodes
# Variables will use remaining replicas
# Agents on partitioned nodes will be recovered on available nodes
# Set up partition healing detection
schedule_partition_healing_check(partitioned_nodes)
end
defp enter_readonly_mode(partitioned_nodes) do
# Enter read-only mode to prevent split-brain
set_cluster_mode(:readonly)
# Stop accepting new agent placements
# Stop variable updates
# Continue serving read requests
# Wait for partition to heal
schedule_partition_healing_check(partitioned_nodes)
end
@doc """
Implement automatic cluster healing after partition recovery.
"""
@spec heal_partition([node()]) :: :ok
def heal_partition(recovered_nodes) do
# 1. Verify all nodes are reachable
reachable_nodes = Enum.filter(recovered_nodes, fn node ->
Node.ping(node) == :pong
end)
# 2. Synchronize cluster state
synchronize_cluster_state(reachable_nodes)
# 3. Resolve any conflicts that occurred during partition
resolve_partition_conflicts(reachable_nodes)
# 4. Resume normal operations
set_cluster_mode(:normal)
# 5. Rebalance agents if needed
schedule_post_partition_rebalancing()
:ok
end
defp synchronize_cluster_state(nodes) do
# Collect state from all nodes
node_states = Enum.map(nodes, fn node ->
state = :rpc.call(node, Foundation.MABEAM.Cluster, :get_local_state, [])
{node, state}
end)
# Merge states using conflict resolution
merged_state = merge_cluster_states(node_states)
# Propagate merged state to all nodes
Enum.each(nodes, fn node ->
:rpc.call(node, Foundation.MABEAM.Cluster, :update_state, [merged_state])
end)
end
defp resolve_partition_conflicts(nodes) do
# Find conflicting variable updates
conflicts = detect_variable_conflicts(nodes)
# Resolve each conflict using configured strategy
Enum.each(conflicts, fn conflict ->
case Foundation.MABEAM.Cluster.VariableSync.resolve_conflict(
conflict.variable_id,
conflict.conflicting_updates
) do
{:ok, resolved_value} ->
# Apply resolved value to all nodes
apply_conflict_resolution(nodes, conflict.variable_id, resolved_value)
{:error, reason} ->
# Log conflict resolution failure
Logger.error("Failed to resolve conflict for #{conflict.variable_id}: #{reason}")
end
end)
end
end
Cluster Deployment Strategies
Auto-Scaling Clusters
defmodule Foundation.MABEAM.Cluster.AutoScaler do
@moduledoc """
Automatic cluster scaling based on load and performance metrics.
"""
@doc """
Monitor cluster load and automatically scale up/down.
"""
@spec monitor_and_scale() :: :ok
def monitor_and_scale() do
cluster_metrics = collect_cluster_metrics()
case determine_scaling_action(cluster_metrics) do
:scale_up -> scale_up_cluster(cluster_metrics)
:scale_down -> scale_down_cluster(cluster_metrics)
:no_action -> :ok
end
end
defp determine_scaling_action(metrics) do
avg_cpu = metrics.average_cpu_usage
avg_memory = metrics.average_memory_usage
queue_length = metrics.agent_queue_length
cond do
avg_cpu > 0.8 or avg_memory > 0.8 or queue_length > 10 -> :scale_up
avg_cpu < 0.3 and avg_memory < 0.3 and queue_length == 0 -> :scale_down
true -> :no_action
end
end
end
Benefits of Cluster Distribution
- Horizontal Scalability: Add nodes to handle more agents and variables
- Fault Tolerance: Survive node failures with automatic recovery
- Geographic Distribution: Deploy agents across multiple data centers
- Resource Optimization: Balance load across heterogeneous hardware
- Hot Code Updates: Update system components without downtime
Integration with Existing Infrastructure
Kubernetes Integration
apiVersion: apps/v1
kind: Deployment
metadata:
name: mabeam-cluster
spec:
replicas: 3
selector:
matchLabels:
app: mabeam
template:
metadata:
labels:
app: mabeam
spec:
containers:
- name: mabeam-node
image: mabeam:latest
env:
- name: CLUSTER_DISCOVERY
value: "kubernetes"
- name: NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
ports:
- containerPort: 4369 # EPMD
- containerPort: 9100 # Distributed Erlang
Docker Compose
version: '3.8'
services:
mabeam-node-1:
image: mabeam:latest
environment:
- NODE_NAME=mabeam1@mabeam-node-1
- CLUSTER_NODES=mabeam1@mabeam-node-1,mabeam2@mabeam-node-2,mabeam3@mabeam-node-3
ports:
- "4369:4369"
- "9100:9100"
mabeam-node-2:
image: mabeam:latest
environment:
- NODE_NAME=mabeam2@mabeam-node-2
- CLUSTER_NODES=mabeam1@mabeam-node-1,mabeam2@mabeam-node-2,mabeam3@mabeam-node-3
ports:
- "4370:4369"
- "9101:9100"
mabeam-node-3:
image: mabeam:latest
environment:
- NODE_NAME=mabeam3@mabeam-node-3
- CLUSTER_NODES=mabeam1@mabeam-node-1,mabeam2@mabeam-node-2,mabeam3@mabeam-node-3
ports:
- "4371:4369"
- "9102:9100"
Next Steps
- MABEAM_06_IMPLEMENTATION.md: Implementation plan and migration strategy
- Implementation of cluster distribution capabilities
- Testing with multi-node deployments
- Performance benchmarking across cluster configurations
- Integration with cloud orchestration platforms