%% distributed_consensus_engine.erl %% Advanced distributed consensus mechanisms for agent decision-making %% Implements Raft, Byzantine fault tolerance, and novel consensus algorithms -module(distributed_consensus_engine). -behaviour(gen_server). -export([ start_link/0, propose_consensus/3, join_consensus_cluster/2, leave_consensus_cluster/1, byzantine_fault_tolerant_consensus/3, quantum_consensus_protocol/3, hierarchical_consensus/4, async_consensus_with_eventual_consistency/3, consensus_with_machine_learning/3, federated_byzantine_agreement/4, probabilistic_consensus/4 ]). -export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]). -define(CONSENSUS_NODES, consensus_cluster_nodes). -define(CONSENSUS_LOG, consensus_decision_log). -define(BYZANTINE_STATE, byzantine_consensus_state). -record(state, { node_id, cluster_nodes = #{}, consensus_protocols = #{}, current_term = 0, voted_for = undefined, log = [], commit_index = 0, last_applied = 0, leader_id = undefined, election_timeout, heartbeat_interval = 150, byzantine_tolerance = 0, quantum_entangled_nodes = [], consensus_history = [], machine_learning_models = #{}, network_partition_detector, consensus_performance_metrics = #{} }). -record(consensus_proposal, { id, proposer_id, proposal_type, % value, configuration, membership value, term, timestamp, required_confirmations, timeout, priority = normal, byzantine_proof = undefined, quantum_signature = undefined }). -record(raft_log_entry, { index, term, command, timestamp, client_id, sequence_num }). -record(byzantine_vote, { voter_id, proposal_id, vote, % accept, reject, abstain proof_of_work, digital_signature, timestamp, byzantine_round }). -record(consensus_result, { proposal_id, decision, % accepted, rejected, timeout final_value, participating_nodes, consensus_achieved_at, proof_of_consensus, byzantine_safety_guaranteed, consensus_algorithm_used }). %% Public API start_link() -> gen_server:start_link({local, ?MODULE}, ?MODULE, [], []). %% Propose a value for consensus with configurable algorithm propose_consensus(Value, Algorithm, Options) -> gen_server:call(?MODULE, {propose_consensus, Value, Algorithm, Options}, 30000). %% Join consensus cluster as a new node join_consensus_cluster(ClusterNodes, NodeCapabilities) -> gen_server:call(?MODULE, {join_cluster, ClusterNodes, NodeCapabilities}). %% Leave consensus cluster gracefully leave_consensus_cluster(Reason) -> gen_server:call(?MODULE, {leave_cluster, Reason}). %% Byzantine fault-tolerant consensus (3f+1 nodes to tolerate f faults) byzantine_fault_tolerant_consensus(Value, FaultTolerance, SecurityLevel) -> gen_server:call(?MODULE, {byzantine_consensus, Value, FaultTolerance, SecurityLevel}, 60000). %% Quantum-inspired consensus with entangled decision nodes quantum_consensus_protocol(Value, QuantumNodes, EntanglementStrength) -> gen_server:call(?MODULE, {quantum_consensus, Value, QuantumNodes, EntanglementStrength}, 45000). %% Hierarchical consensus with multiple decision layers hierarchical_consensus(Value, Hierarchy, DelegationRules, ConflictResolution) -> gen_server:call(?MODULE, {hierarchical_consensus, Value, Hierarchy, DelegationRules, ConflictResolution}). %% Asynchronous consensus with eventual consistency guarantees async_consensus_with_eventual_consistency(Value, ConsistencyLevel, ConvergenceTimeout) -> gen_server:call(?MODULE, {async_consensus, Value, ConsistencyLevel, ConvergenceTimeout}). %% Machine learning-enhanced consensus prediction and optimization consensus_with_machine_learning(Value, LearningModel, PredictionContext) -> gen_server:call(?MODULE, {ml_consensus, Value, LearningModel, PredictionContext}). %% Federated Byzantine Agreement for multi-cluster consensus federated_byzantine_agreement(Value, FederatedClusters, TrustMatrix, QuorumSlices) -> gen_server:call(?MODULE, {federated_consensus, Value, FederatedClusters, TrustMatrix, QuorumSlices}). %% Probabilistic consensus with confidence intervals probabilistic_consensus(Value, ConfidenceLevel, ProbabilisticModel, UncertaintyTolerance) -> gen_server:call(?MODULE, {probabilistic_consensus, Value, ConfidenceLevel, ProbabilisticModel, UncertaintyTolerance}). %% Gen_server callbacks init([]) -> % Initialize consensus node NodeId = generate_node_id(), % Create ETS tables for consensus state ets:new(?CONSENSUS_NODES, [named_table, public, set]), ets:new(?CONSENSUS_LOG, [named_table, public, ordered_set]), ets:new(?BYZANTINE_STATE, [named_table, public, set]), % Start network partition detector PartitionDetector = spawn_link(fun() -> network_partition_monitor() end), % Initialize machine learning models for consensus optimization MLModels = initialize_consensus_ml_models(), % Start election timeout timer ElectionTimeout = generate_election_timeout(), schedule_election_timeout(ElectionTimeout), {ok, #state{ node_id = NodeId, election_timeout = ElectionTimeout, network_partition_detector = PartitionDetector, machine_learning_models = MLModels }}. handle_call({propose_consensus, Value, Algorithm, Options}, From, State) -> ProposalId = generate_proposal_id(), % Create consensus proposal Proposal = #consensus_proposal{ id = ProposalId, proposer_id = State#state.node_id, proposal_type = value, value = Value, term = State#state.current_term, timestamp = erlang:system_time(microsecond), required_confirmations = calculate_required_confirmations(Algorithm, Options), timeout = maps:get(timeout, Options, 30000) }, % Execute consensus algorithm spawn_link(fun() -> Result = execute_consensus_algorithm(Algorithm, Proposal, Options, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({join_cluster, ClusterNodes, Capabilities}, _From, State) -> Result = join_consensus_cluster_internal(ClusterNodes, Capabilities, State), {reply, Result, State}; handle_call({leave_cluster, Reason}, _From, State) -> Result = leave_consensus_cluster_internal(Reason, State), {reply, Result, State}; handle_call({byzantine_consensus, Value, FaultTolerance, SecurityLevel}, From, State) -> spawn_link(fun() -> Result = execute_byzantine_consensus(Value, FaultTolerance, SecurityLevel, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({quantum_consensus, Value, QuantumNodes, Strength}, From, State) -> spawn_link(fun() -> Result = execute_quantum_consensus(Value, QuantumNodes, Strength, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({hierarchical_consensus, Value, Hierarchy, Rules, Resolution}, From, State) -> spawn_link(fun() -> Result = execute_hierarchical_consensus(Value, Hierarchy, Rules, Resolution, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({async_consensus, Value, Consistency, Timeout}, From, State) -> spawn_link(fun() -> Result = execute_async_consensus(Value, Consistency, Timeout, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({ml_consensus, Value, Model, Context}, From, State) -> spawn_link(fun() -> Result = execute_ml_consensus(Value, Model, Context, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({federated_consensus, Value, Clusters, Trust, Quorums}, From, State) -> spawn_link(fun() -> Result = execute_federated_consensus(Value, Clusters, Trust, Quorums, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call({probabilistic_consensus, Value, Confidence, Model, Tolerance}, From, State) -> spawn_link(fun() -> Result = execute_probabilistic_consensus(Value, Confidence, Model, Tolerance, State), gen_server:reply(From, Result) end), {noreply, State}; handle_call(_Request, _From, State) -> {reply, {error, unknown_request}, State}. handle_cast({vote_request, Term, CandidateId, LastLogIndex, LastLogTerm}, State) -> NewState = handle_vote_request(Term, CandidateId, LastLogIndex, LastLogTerm, State), {noreply, NewState}; handle_cast({vote_response, Term, VoteGranted, VoterId}, State) -> NewState = handle_vote_response(Term, VoteGranted, VoterId, State), {noreply, NewState}; handle_cast({append_entries, Term, LeaderId, PrevLogIndex, PrevLogTerm, Entries, LeaderCommit}, State) -> NewState = handle_append_entries(Term, LeaderId, PrevLogIndex, PrevLogTerm, Entries, LeaderCommit, State), {noreply, NewState}; handle_cast({byzantine_vote, Vote}, State) -> NewState = handle_byzantine_vote(Vote, State), {noreply, NewState}; handle_cast({quantum_entanglement_update, Nodes, EntanglementState}, State) -> NewState = State#state{quantum_entangled_nodes = Nodes}, update_quantum_consensus_state(EntanglementState), {noreply, NewState}; handle_cast(_Msg, State) -> {noreply, State}. handle_info({election_timeout}, State) -> NewState = start_leader_election(State), {noreply, NewState}; handle_info({heartbeat_timeout}, State) -> NewState = send_heartbeat_to_followers(State), schedule_heartbeat_timeout(State#state.heartbeat_interval), {noreply, NewState}; handle_info({consensus_proposal_timeout, ProposalId}, State) -> handle_proposal_timeout(ProposalId), {noreply, State}; handle_info({network_partition_detected, PartitionInfo}, State) -> NewState = handle_network_partition(PartitionInfo, State), {noreply, NewState}; handle_info({consensus_performance_update, Metrics}, State) -> NewState = update_consensus_performance_metrics(Metrics, State), {noreply, NewState}; handle_info(_Info, State) -> {noreply, State}. terminate(_Reason, _State) -> ok. code_change(_OldVsn, State, _Extra) -> {ok, State}. %% Consensus Algorithm Implementations execute_consensus_algorithm(raft, Proposal, Options, State) -> execute_raft_consensus(Proposal, Options, State); execute_consensus_algorithm(pbft, Proposal, Options, State) -> execute_pbft_consensus(Proposal, Options, State); execute_consensus_algorithm(tendermint, Proposal, Options, State) -> execute_tendermint_consensus(Proposal, Options, State); execute_consensus_algorithm(avalanche, Proposal, Options, State) -> execute_avalanche_consensus(Proposal, Options, State); execute_consensus_algorithm(stellar_consensus, Proposal, Options, State) -> execute_stellar_consensus_protocol(Proposal, Options, State); execute_consensus_algorithm(hashgraph, Proposal, Options, State) -> execute_hashgraph_consensus(Proposal, Options, State). execute_raft_consensus(Proposal, Options, State) -> % Enhanced Raft implementation with optimizations % Phase 1: Leader election if needed {ok, LeaderState} = ensure_leader_elected(State), % Phase 2: Log replication LogEntry = create_raft_log_entry(Proposal, LeaderState), ReplicationResult = replicate_log_entry(LogEntry, LeaderState), % Phase 3: Commitment case ReplicationResult of {ok, MajorityAchieved} when MajorityAchieved -> CommitResult = commit_log_entry(LogEntry, LeaderState), % Phase 4: Apply to state machine ApplicationResult = apply_to_state_machine(LogEntry, LeaderState), create_consensus_result(Proposal, accepted, ApplicationResult); {error, Reason} -> create_consensus_result(Proposal, rejected, Reason) end. execute_byzantine_consensus(Value, FaultTolerance, SecurityLevel, State) -> % Practical Byzantine Fault Tolerance (pBFT) with enhancements % Calculate required nodes (3f + 1 for f Byzantine faults) RequiredNodes = 3 * FaultTolerance + 1, AvailableNodes = get_available_consensus_nodes(), case length(AvailableNodes) >= RequiredNodes of true -> % Phase 1: Pre-prepare PreparePhase = execute_byzantine_prepare_phase(Value, AvailableNodes, SecurityLevel), % Phase 2: Prepare case PreparePhase of {ok, PrepareQuorum} -> CommitPhase = execute_byzantine_commit_phase(Value, PrepareQuorum, SecurityLevel), % Phase 3: Commit case CommitPhase of {ok, CommitQuorum} -> FinalResult = finalize_byzantine_consensus(Value, CommitQuorum), % Verify Byzantine safety and liveness SafetyProof = verify_byzantine_safety(FinalResult, FaultTolerance), #consensus_result{ proposal_id = generate_proposal_id(), decision = accepted, final_value = Value, participating_nodes = CommitQuorum, consensus_achieved_at = erlang:system_time(microsecond), proof_of_consensus = SafetyProof, byzantine_safety_guaranteed = true, consensus_algorithm_used = pbft }; {error, CommitError} -> create_error_result(byzantine_commit_failed, CommitError) end; {error, PrepareError} -> create_error_result(byzantine_prepare_failed, PrepareError) end; false -> create_error_result(insufficient_nodes, #{required => RequiredNodes, available => length(AvailableNodes)}) end. execute_quantum_consensus(Value, QuantumNodes, EntanglementStrength, State) -> % Quantum-inspired consensus using entangled decision states % Phase 1: Establish quantum entanglement between nodes EntanglementResult = establish_quantum_entanglement_network(QuantumNodes, EntanglementStrength), case EntanglementResult of {ok, EntangledNetwork} -> % Phase 2: Create superposition of consensus states SuperpositionStates = create_consensus_superposition(Value, EntangledNetwork), % Phase 3: Apply quantum interference for convergence InterferenceResult = apply_quantum_interference_for_consensus(SuperpositionStates), % Phase 4: Measure consensus outcome {MeasurementResult, CollapsedState} = quantum_measurement_consensus(InterferenceResult), % Phase 5: Verify quantum consensus properties QuantumProof = verify_quantum_consensus_properties(CollapsedState, EntangledNetwork), #consensus_result{ proposal_id = generate_proposal_id(), decision = accepted, final_value = MeasurementResult, participating_nodes = QuantumNodes, consensus_achieved_at = erlang:system_time(microsecond), proof_of_consensus = QuantumProof, byzantine_safety_guaranteed = false, % Different security model consensus_algorithm_used = quantum_consensus }; {error, EntanglementError} -> create_error_result(quantum_entanglement_failed, EntanglementError) end. execute_hierarchical_consensus(Value, Hierarchy, DelegationRules, ConflictResolution, State) -> % Multi-layer hierarchical consensus % Phase 1: Bottom-up consensus propagation BottomUpResults = execute_bottom_up_consensus(Value, Hierarchy, DelegationRules), % Phase 2: Conflict detection and resolution ConflictAnalysis = detect_hierarchical_conflicts(BottomUpResults), case ConflictAnalysis of {conflicts_detected, Conflicts} -> % Apply conflict resolution strategy ResolvedResults = apply_conflict_resolution(Conflicts, ConflictResolution), % Phase 3: Top-down validation ValidationResult = validate_hierarchical_consensus(ResolvedResults, Hierarchy), create_hierarchical_consensus_result(Value, ValidationResult); {no_conflicts, Results} -> % Direct consensus achieved create_hierarchical_consensus_result(Value, Results) end. execute_ml_consensus(Value, LearningModel, PredictionContext, State) -> % Machine learning-enhanced consensus % Phase 1: Predict optimal consensus strategy MLModel = maps:get(LearningModel, State#state.machine_learning_models), PredictedStrategy = predict_optimal_consensus_strategy(MLModel, Value, PredictionContext), % Phase 2: Execute predicted strategy StrategyResult = execute_predicted_consensus_strategy(PredictedStrategy, Value, State), % Phase 3: Learn from outcome LearningUpdate = update_ml_model_from_outcome(MLModel, PredictedStrategy, StrategyResult), % Phase 4: Adaptive optimization OptimizedResult = apply_adaptive_optimization(StrategyResult, LearningUpdate), create_ml_consensus_result(Value, OptimizedResult, LearningUpdate). %% Utility Functions generate_node_id() -> {node(), erlang:system_time(microsecond), rand:uniform(1000000)}. generate_proposal_id() -> list_to_binary([ atom_to_list(node()), "_", integer_to_list(erlang:system_time(microsecond)), "_", integer_to_list(rand:uniform(1000000)) ]). generate_election_timeout() -> % Randomized timeout to prevent split votes 150 + rand:uniform(150). % 150-300ms schedule_election_timeout(Timeout) -> erlang:send_after(Timeout, self(), {election_timeout}). schedule_heartbeat_timeout(Interval) -> erlang:send_after(Interval, self(), {heartbeat_timeout}). calculate_required_confirmations(Algorithm, Options) -> case Algorithm of raft -> NodeCount = get_cluster_size(), (NodeCount div 2) + 1; % Majority pbft -> FaultTolerance = maps:get(fault_tolerance, Options, 1), 2 * FaultTolerance + 1; % 2f + 1 for f faults stellar_consensus -> maps:get(quorum_threshold, Options, 0.67); % 67% threshold _ -> maps:get(required_confirmations, Options, 3) end. network_partition_monitor() -> % Monitor network partitions and connectivity receive {check_partition, Nodes} -> PartitionStatus = detect_network_partition(Nodes), case PartitionStatus of {partition_detected, PartitionInfo} -> distributed_consensus_engine ! {network_partition_detected, PartitionInfo}; no_partition -> ok end, network_partition_monitor(); stop_monitor -> ok after 5000 -> perform_periodic_partition_check(), network_partition_monitor() end. initialize_consensus_ml_models() -> #{ strategy_predictor => train_consensus_strategy_model(), performance_optimizer => train_performance_optimization_model(), fault_predictor => train_fault_prediction_model(), adaptive_timeout => train_adaptive_timeout_model() }. %% Placeholder implementations for complex consensus operations join_consensus_cluster_internal(_Nodes, _Capabilities, _State) -> {ok, joined}. leave_consensus_cluster_internal(_Reason, _State) -> {ok, left}. handle_vote_request(_Term, _Candidate, _LogIndex, _LogTerm, State) -> State. handle_vote_response(_Term, _Granted, _Voter, State) -> State. handle_append_entries(_Term, _Leader, _PrevIndex, _PrevTerm, _Entries, _Commit, State) -> State. handle_byzantine_vote(_Vote, State) -> State. update_quantum_consensus_state(_State) -> ok. start_leader_election(State) -> State. send_heartbeat_to_followers(State) -> State. handle_proposal_timeout(_ProposalId) -> ok. handle_network_partition(_PartitionInfo, State) -> State. update_consensus_performance_metrics(_Metrics, State) -> State. ensure_leader_elected(State) -> {ok, State}. create_raft_log_entry(_Proposal, _State) -> #raft_log_entry{}. replicate_log_entry(_Entry, _State) -> {ok, true}. commit_log_entry(_Entry, _State) -> ok. apply_to_state_machine(_Entry, _State) -> ok. create_consensus_result(_Proposal, Decision, _Data) -> #consensus_result{decision = Decision, consensus_algorithm_used = raft}. get_available_consensus_nodes() -> [node1, node2, node3, node4]. execute_byzantine_prepare_phase(_Value, _Nodes, _Security) -> {ok, []}. execute_byzantine_commit_phase(_Value, _Quorum, _Security) -> {ok, []}. finalize_byzantine_consensus(_Value, _Quorum) -> consensus_achieved. verify_byzantine_safety(_Result, _Tolerance) -> safety_proof. create_error_result(_Type, _Details) -> #consensus_result{decision = rejected}. establish_quantum_entanglement_network(_Nodes, _Strength) -> {ok, entangled_network}. create_consensus_superposition(_Value, _Network) -> superposition_states. apply_quantum_interference_for_consensus(_States) -> interference_result. quantum_measurement_consensus(_Result) -> {measurement, collapsed_state}. verify_quantum_consensus_properties(_State, _Network) -> quantum_proof. execute_bottom_up_consensus(_Value, _Hierarchy, _Rules) -> bottom_up_results. detect_hierarchical_conflicts(_Results) -> {no_conflicts, []}. apply_conflict_resolution(_Conflicts, _Strategy) -> resolved_results. validate_hierarchical_consensus(_Results, _Hierarchy) -> validation_result. create_hierarchical_consensus_result(_Value, _Result) -> #consensus_result{decision = accepted, consensus_algorithm_used = hierarchical}. predict_optimal_consensus_strategy(_Model, _Value, _Context) -> predicted_strategy. execute_predicted_consensus_strategy(_Strategy, _Value, _State) -> strategy_result. update_ml_model_from_outcome(_Model, _Strategy, _Result) -> learning_update. apply_adaptive_optimization(_Result, _Update) -> optimized_result. create_ml_consensus_result(_Value, _Result, _Update) -> #consensus_result{decision = accepted, consensus_algorithm_used = ml_enhanced}. get_cluster_size() -> 5. detect_network_partition(_Nodes) -> no_partition. perform_periodic_partition_check() -> ok. train_consensus_strategy_model() -> strategy_model. train_performance_optimization_model() -> performance_model. train_fault_prediction_model() -> fault_model. train_adaptive_timeout_model() -> timeout_model. execute_pbft_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, pbft). execute_tendermint_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, tendermint). execute_avalanche_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, avalanche). execute_stellar_consensus_protocol(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, stellar). execute_hashgraph_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, hashgraph). execute_async_consensus(_Value, _Consistency, _Timeout, _State) -> #consensus_result{decision = accepted, consensus_algorithm_used = async}. execute_federated_consensus(_Value, _Clusters, _Trust, _Quorums, _State) -> #consensus_result{decision = accepted, consensus_algorithm_used = federated}. execute_probabilistic_consensus(_Value, _Confidence, _Model, _Tolerance, _State) -> #consensus_result{decision = accepted, consensus_algorithm_used = probabilistic}.