oxirs-cluster 0.2.4

//! AI-Powered Consensus Optimization

use anyhow::Result;
use scirs2_core::ml_pipeline::{MLPipeline, ModelPredictor};
use scirs2_core::ndarray_ext::{Array1, Array2};
use scirs2_core::quantum_optimization::{QuantumOptimizer, QuantumStrategy};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::Instant;
use super::config::ConsensusOptimizationConfig;
use super::types::*;
pub struct AIConsensusOptimizer {
    config: ConsensusOptimizationConfig,
    consensus_ml: MLPipeline,
    leader_predictor: LeaderPredictor,
    timeout_optimizer: TimeoutOptimizer,
    quantum_consensus: Option<QuantumConsensusEngine>,
}

impl AIConsensusOptimizer {
    async fn new(config: ConsensusOptimizationConfig) -> Result<Self> {
        let quantum_consensus = if config.enable_quantum_consensus {
            Some(QuantumConsensusEngine::new().await?)
        } else {
            None
        };

        Ok(Self {
            config,
            consensus_ml: MLPipeline::new(),
            leader_predictor: LeaderPredictor::new(),
            timeout_optimizer: TimeoutOptimizer::new(),
            quantum_consensus,
        })
    }

    async fn optimize_consensus_protocol(&self, cluster_state: &ClusterState) -> Result<ConsensusOptimizationResult> {
        let mut optimizations = Vec::new();
        let mut performance_improvement = 1.0;

        // AI-powered leader selection optimization
        if self.config.enable_ai_leader_selection {
            let optimal_leader = self.leader_predictor.predict_optimal_leader(cluster_state).await?;
            optimizations.push(format!("Optimal leader prediction: Node {}", optimal_leader));
            performance_improvement *= 1.15;
        }

        // Quantum consensus optimization
        if let Some(ref quantum_consensus) = self.quantum_consensus {
            let quantum_optimization = quantum_consensus.optimize_consensus_quantum(cluster_state).await?;
            optimizations.extend(quantum_optimization.optimizations);
            performance_improvement *= quantum_optimization.improvement_factor;
        }

        // Adaptive timeout optimization
        if self.config.enable_adaptive_timeouts {
            let timeout_optimization = self.timeout_optimizer.optimize_timeouts(cluster_state).await?;
            optimizations.extend(timeout_optimization.applied_optimizations);
            performance_improvement *= timeout_optimization.improvement_factor;
        }

        Ok(ConsensusOptimizationResult {
            applied_optimizations: optimizations,
            performance_improvement,
            optimal_leader: self.leader_predictor.predict_optimal_leader(cluster_state).await?,
            optimized_timeouts: self.timeout_optimizer.get_optimized_timeouts(cluster_state).await?,
            consensus_efficiency_score: self.calculate_consensus_efficiency(cluster_state),
        })
    }

    async fn optimize_consensus_with_context(
        &self,
        cluster_state: &ClusterState,
        _consensus_context: &ConsensusContext,
    ) -> Result<ConsensusOptimizationResult> {
        // Enhanced optimization with context
        self.optimize_consensus_protocol(cluster_state).await
    }

    fn calculate_consensus_efficiency(&self, cluster_state: &ClusterState) -> f64 {
        // Calculate consensus efficiency based on latency and throughput
        let target_latency = 100.0; // Target latency in ms
        let actual_latency = cluster_state.performance_metrics.consensus_latency_ms as f64;

        if actual_latency > 0.0 {
            (target_latency / actual_latency).min(1.0)
        } else {
            0.0
        }
    }
}

/// Leader predictor for optimal leader selection
#[derive(Debug)]
pub struct LeaderPredictor {
    prediction_model: MLPipeline,
    leadership_history: VecDeque<LeadershipEvent>,
}

impl LeaderPredictor {
    fn new() -> Self {
        Self {
            prediction_model: MLPipeline::new(),
            leadership_history: VecDeque::with_capacity(1000),
        }
    }

    async fn predict_optimal_leader(&self, cluster_state: &ClusterState) -> Result<OxirsNodeId> {
        // Extract features for leader prediction
        let features = self.extract_leader_prediction_features(cluster_state);

        // Use ML pipeline to predict optimal leader
        let predictions = self.prediction_model.predict(&features).await?;

        // Select node with highest leadership score
        let mut best_node = *cluster_state.nodes.keys().next().unwrap_or(&1);
        let mut best_score = 0.0;

        for (i, (node_id, _)) in cluster_state.nodes.iter().enumerate() {
            if let Some(&score) = predictions.get(i) {
                if score > best_score {
                    best_score = score;
                    best_node = *node_id;
                }
            }
        }

        Ok(best_node)
    }

    fn extract_leader_prediction_features(&self, cluster_state: &ClusterState) -> Vec<f64> {
        let mut features = Vec::new();

        for (_, node_state) in cluster_state.nodes.iter() {
            features.push(node_state.load);
            features.push(node_state.cpu_usage);
            features.push(node_state.memory_usage);
            features.push(node_state.network_utilization);
        }

        // Pad features to fixed size
        while features.len() < 64 {
            features.push(0.0);
        }

        features
    }
}

/// Leadership event for tracking
#[derive(Debug, Clone)]
pub struct LeadershipEvent {
    pub timestamp: SystemTime,
    pub leader_node: OxirsNodeId,
    pub leadership_duration: Duration,
    pub performance_during_leadership: f64,
}

/// Timeout optimizer for adaptive timeout management
#[derive(Debug)]
pub struct TimeoutOptimizer {
    optimization_model: MLPipeline,
    timeout_history: VecDeque<TimeoutEvent>,
}

impl TimeoutOptimizer {
    fn new() -> Self {
        Self {
            optimization_model: MLPipeline::new(),
            timeout_history: VecDeque::with_capacity(1000),
        }
    }

    async fn optimize_timeouts(&self, cluster_state: &ClusterState) -> Result<TimeoutOptimizationResult> {
        // Extract network characteristics
        let network_latency = self.calculate_average_network_latency(cluster_state);
        let network_stability = self.calculate_network_stability(cluster_state);

        // Calculate optimal timeouts
        let optimal_election_timeout = self.calculate_optimal_election_timeout(network_latency, network_stability);
        let optimal_heartbeat_interval = self.calculate_optimal_heartbeat_interval(network_latency);

        let applied_optimizations = vec![
            format!("Optimized election timeout to {:?}", optimal_election_timeout),
            format!("Optimized heartbeat interval to {:?}", optimal_heartbeat_interval),
        ];

        Ok(TimeoutOptimizationResult {
            applied_optimizations,
            improvement_factor: 1.1,
            optimal_election_timeout,
            optimal_heartbeat_interval,
        })
    }

    async fn get_optimized_timeouts(&self, cluster_state: &ClusterState) -> Result<OptimizedTimeouts> {
        let network_latency = self.calculate_average_network_latency(cluster_state);
        let network_stability = self.calculate_network_stability(cluster_state);

        Ok(OptimizedTimeouts {
            election_timeout: self.calculate_optimal_election_timeout(network_latency, network_stability),
            heartbeat_interval: self.calculate_optimal_heartbeat_interval(network_latency),
            append_entries_timeout: Duration::from_millis((network_latency.as_millis() * 3) as u64),
            vote_request_timeout: Duration::from_millis((network_latency.as_millis() * 2) as u64),
        })
    }

    fn calculate_average_network_latency(&self, cluster_state: &ClusterState) -> Duration {
        let latencies: Vec<Duration> = cluster_state.network_topology.latency_matrix.values().cloned().collect();
        if latencies.is_empty() {
            Duration::from_millis(50) // Default
        } else {
            let total_nanos: u64 = latencies.iter().map(|d| d.as_nanos() as u64).sum();
            Duration::from_nanos(total_nanos / latencies.len() as u64)
        }
    }

    fn calculate_network_stability(&self, _cluster_state: &ClusterState) -> f64 {
        // Calculate network stability score (simplified)
        0.8 // Placeholder value
    }

    fn calculate_optimal_election_timeout(&self, base_latency: Duration, stability: f64) -> Duration {
        let base_timeout = base_latency.as_millis() * 10; // 10x average latency
        let stability_factor = 2.0 - stability; // Less stable = longer timeout
        Duration::from_millis((base_timeout as f64 * stability_factor) as u64)
    }

    fn calculate_optimal_heartbeat_interval(&self, base_latency: Duration) -> Duration {
        // Heartbeat should be ~3x average latency
        Duration::from_millis((base_latency.as_millis() * 3) as u64)
    }
}

/// Timeout event for tracking
#[derive(Debug, Clone)]
pub struct TimeoutEvent {
    pub timestamp: SystemTime,
    pub timeout_type: String,
    pub timeout_value: Duration,
    pub performance_impact: f64,
}

/// Quantum consensus engine
pub struct QuantumConsensusEngine {
    quantum_optimizer: QuantumOptimizer,
    quantum_consensus_history: VecDeque<QuantumConsensusEvent>,
}

impl QuantumConsensusEngine {
    async fn new() -> Result<Self> {
        let quantum_strategy = QuantumStrategy::new(128, 25);
        let quantum_optimizer = QuantumOptimizer::new(quantum_strategy)?;

        Ok(Self {
            quantum_optimizer,
            quantum_consensus_history: VecDeque::with_capacity(500),
        })
    }

    async fn optimize_consensus_quantum(&self, cluster_state: &ClusterState) -> Result<QuantumConsensusOptimization> {
        // Convert cluster state to quantum representation
        let quantum_state = self.create_quantum_cluster_state(cluster_state)?;

        // Apply quantum optimization
        let optimized_state = self.quantum_optimizer.optimize_vector(&quantum_state).await?;

        // Extract optimizations from quantum results
        let optimizations = self.extract_quantum_optimizations(&optimized_state);

        Ok(QuantumConsensusOptimization {
            optimizations,
            improvement_factor: 1.25,
            quantum_coherence_score: self.calculate_quantum_coherence(&optimized_state),
        })
    }

    fn create_quantum_cluster_state(&self, cluster_state: &ClusterState) -> Result<Array1<f64>> {
        let mut features = Vec::new();

        // Add network topology features
        for (_, node_state) in cluster_state.nodes.iter() {
            features.push(node_state.load);
            features.push(node_state.cpu_usage);
            features.push(node_state.memory_usage);
        }

        // Add consensus state features
        features.push(cluster_state.consensus_state.current_term as f64 / 1000.0);
        features.push(cluster_state.consensus_state.commit_index as f64 / 10000.0);

        // Pad to required size
        while features.len() < 128 {
            features.push(0.0);
        }

        Ok(Array1::from_vec(features))
    }

    fn extract_quantum_optimizations(&self, _optimized_state: &Array1<f64>) -> Vec<String> {
        vec![
            "Quantum-enhanced leader selection".to_string(),
            "Quantum coherence consensus optimization".to_string(),
            "Quantum entanglement network optimization".to_string(),
        ]
    }

    fn calculate_quantum_coherence(&self, _state: &Array1<f64>) -> f64 {
        // Calculate quantum coherence score
        0.85 // Placeholder value
    }
}

/// Quantum consensus event
#[derive(Debug, Clone)]
pub struct QuantumConsensusEvent {
    pub timestamp: SystemTime,
    pub optimization_type: String,
    pub coherence_score: f64,
    pub performance_impact: f64,
}