oxirs-cluster 0.2.4

//! Quantum Network Optimization

use anyhow::Result;
use scirs2_core::quantum_optimization::{QuantumOptimizer, QuantumStrategy};
use scirs2_core::ndarray_ext::Array1;
use std::collections::HashMap;
use std::time::Instant;
use super::config::NetworkOptimizationConfig;
use super::types::*;
pub struct QuantumNetworkOptimizer {
    config: NetworkOptimizationConfig,
    quantum_optimizer: QuantumOptimizer,
    routing_optimizer: RoutingOptimizer,
    congestion_predictor: CongestionPredictor,
}

impl QuantumNetworkOptimizer {
    async fn new(config: NetworkOptimizationConfig) -> Result<Self> {
        let quantum_strategy = QuantumStrategy::new(64, 20);
        let quantum_optimizer = QuantumOptimizer::new(quantum_strategy)?;

        Ok(Self {
            config,
            quantum_optimizer,
            routing_optimizer: RoutingOptimizer::new(),
            congestion_predictor: CongestionPredictor::new(),
        })
    }

    async fn optimize_network_topology(&self, cluster_state: &ClusterState) -> Result<NetworkOptimizationResult> {
        let mut routing_optimizations = Vec::new();
        let mut performance_improvement = 1.0;

        // Quantum network optimization
        if self.config.enable_quantum_network_optimization {
            let quantum_optimization = self.apply_quantum_network_optimization(cluster_state).await?;
            routing_optimizations.extend(quantum_optimization.optimizations);
            performance_improvement *= quantum_optimization.improvement;
        }

        // Adaptive routing optimization
        if self.config.enable_adaptive_routing {
            let routing_optimization = self.routing_optimizer.optimize_routing(cluster_state).await?;
            routing_optimizations.extend(routing_optimization.optimizations);
            performance_improvement *= routing_optimization.improvement;
        }

        // Congestion prediction and mitigation
        if self.config.enable_congestion_prediction {
            let congestion_predictions = self.congestion_predictor.predict_congestion(cluster_state).await?;
            let mitigation_actions = self.create_congestion_mitigation_actions(&congestion_predictions);
            routing_optimizations.extend(mitigation_actions);
            performance_improvement *= 1.05;
        }

        Ok(NetworkOptimizationResult {
            routing_optimizations,
            performance_improvement,
            optimal_topology: self.calculate_optimal_topology(cluster_state),
            network_efficiency_score: self.calculate_network_efficiency(cluster_state),
        })
    }

    async fn apply_quantum_network_optimization(&self, cluster_state: &ClusterState) -> Result<QuantumNetworkOptimization> {
        // Convert network topology to quantum representation
        let quantum_topology = self.create_quantum_topology_representation(cluster_state)?;

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

        // Extract routing optimizations
        let optimizations = self.extract_routing_optimizations(&optimized_topology);

        Ok(QuantumNetworkOptimization {
            optimizations,
            improvement: 1.3,
        })
    }

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

        // Add latency matrix features
        for ((_, _), latency) in &cluster_state.network_topology.latency_matrix {
            features.push(latency.as_millis() as f64);
        }

        // Add bandwidth matrix features
        for ((_, _), bandwidth) in &cluster_state.network_topology.bandwidth_matrix {
            features.push(*bandwidth);
        }

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

        Ok(Array1::from_vec(features))
    }

    fn extract_routing_optimizations(&self, _optimized_topology: &Array1<f64>) -> Vec<String> {
        vec![
            "Quantum-optimized routing paths".to_string(),
            "Quantum entanglement-based load balancing".to_string(),
            "Quantum coherence network optimization".to_string(),
        ]
    }

    fn create_congestion_mitigation_actions(&self, predictions: &[CongestionPrediction]) -> Vec<String> {
        let mut actions = Vec::new();

        for prediction in predictions {
            if prediction.congestion_probability > 0.7 {
                actions.push(format!(
                    "Preemptive rerouting for link between nodes {} and {} (congestion probability: {:.2})",
                    prediction.source_node, prediction.target_node, prediction.congestion_probability
                ));
            }
        }

        actions
    }

    fn calculate_optimal_topology(&self, cluster_state: &ClusterState) -> NetworkTopology {
        // Return current topology as placeholder (would be optimized in real implementation)
        cluster_state.network_topology.clone()
    }

    fn calculate_network_efficiency(&self, cluster_state: &ClusterState) -> f64 {
        // Calculate network efficiency based on utilization and latency
        let avg_utilization = cluster_state.performance_metrics.network_utilization;
        let avg_latency_ms = cluster_state.performance_metrics.consensus_latency_ms as f64;

        // Efficiency is high when utilization is good (not too low, not too high) and latency is low
        let utilization_efficiency = if avg_utilization > 0.3 && avg_utilization < 0.8 {
            1.0
        } else {
            0.5
        };

        let latency_efficiency = if avg_latency_ms < 100.0 {
            1.0
        } else {
            100.0 / avg_latency_ms
        };

        (utilization_efficiency * latency_efficiency).min(1.0)
    }
}

/// Routing optimizer
#[derive(Debug)]
pub struct RoutingOptimizer {
    optimization_model: MLPipeline,
}

impl RoutingOptimizer {
    fn new() -> Self {
        Self {
            optimization_model: MLPipeline::new(),
        }
    }

    async fn optimize_routing(&self, _cluster_state: &ClusterState) -> Result<RoutingOptimization> {
        let optimizations = vec![
            "Optimized shortest-path routing".to_string(),
            "Load-aware routing adjustments".to_string(),
        ];

        Ok(RoutingOptimization {
            optimizations,
            improvement: 1.15,
        })
    }
}

/// Congestion predictor
#[derive(Debug)]
pub struct CongestionPredictor {
    prediction_model: MLPipeline,
    congestion_history: VecDeque<CongestionEvent>,
}

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

    async fn predict_congestion(&self, cluster_state: &ClusterState) -> Result<Vec<CongestionPrediction>> {
        let mut predictions = Vec::new();

        // Predict congestion for each network link
        for ((source, target), bandwidth) in &cluster_state.network_topology.bandwidth_matrix {
            let utilization = self.calculate_link_utilization(*source, *target, cluster_state);
            let congestion_probability = if utilization > 0.8 { 0.9 } else { utilization * 0.5 };

            predictions.push(CongestionPrediction {
                source_node: *source,
                target_node: *target,
                congestion_probability,
                predicted_congestion_time: SystemTime::now() + Duration::from_secs(300), // 5 minutes
                current_utilization: utilization,
                available_bandwidth: *bandwidth,
            });
        }

        Ok(predictions)
    }

    fn calculate_link_utilization(&self, _source: OxirsNodeId, _target: OxirsNodeId, _cluster_state: &ClusterState) -> f64 {
        // Calculate current utilization of network link (simplified)
        0.5 // Placeholder value
    }
}