scirs2_core/advanced_ecosystem_integration/

performance.rs

//! Performance monitoring and metrics collection

use super::types::*;
use crate::error::CoreResult;
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Performance monitor for the ecosystem
#[allow(dead_code)]
#[derive(Debug)]
pub struct EcosystemPerformanceMonitor {
    /// Module performance history
    module_performance: HashMap<String, Vec<ModulePerformanceMetrics>>,
    /// System-wide metrics
    system_metrics: SystemMetrics,
    /// Performance alerts
    alerts: Vec<PerformanceAlert>,
    /// Monitoring configuration
    #[allow(dead_code)]
    config: MonitoringConfig,
}

/// System-wide performance metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SystemMetrics {
    /// Total throughput
    pub total_throughput: f64,
    /// Average latency
    pub avg_latency: Duration,
    /// Error rate
    pub error_rate: f64,
    /// Resource efficiency
    pub resource_efficiency: f64,
    /// Quality score
    pub quality_score: f64,
}

/// Performance alert
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceAlert {
    /// Alert level
    pub level: AlertLevel,
    /// Alert message
    pub message: String,
    /// Affected module
    pub module: Option<String>,
    /// Timestamp
    pub timestamp: Instant,
}

/// Alert levels
#[allow(dead_code)]
#[derive(Debug, Clone, PartialEq)]
pub enum AlertLevel {
    Info,
    Warning,
    Error,
    Critical,
}

/// Monitoring configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MonitoringConfig {
    /// Sampling rate (Hz)
    pub samplingrate: f64,
    /// Alert thresholds
    pub alert_thresholds: AlertThresholds,
    /// History retention (hours)
    pub history_retention_hours: u32,
}

/// Alert thresholds
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AlertThresholds {
    /// Latency threshold (ms)
    pub latency_threshold: f64,
    /// Error rate threshold (percentage)
    pub error_rate_threshold: f64,
    /// Memory usage threshold (percentage)
    pub memory_threshold: f64,
    /// CPU usage threshold (percentage)
    pub cpu_threshold: f64,
}

/// Performance report for the ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EcosystemPerformanceReport {
    /// System-wide metrics
    pub system_metrics: SystemMetrics,
    /// Module-specific metrics
    pub module_metrics: HashMap<String, ModulePerformanceMetrics>,
    /// Resource utilization
    pub resource_utilization: ResourceUtilization,
    /// Alerts
    pub alerts: Vec<PerformanceAlert>,
    /// Recommendations
    pub recommendations: Vec<String>,
    /// Report timestamp
    pub timestamp: Instant,
}

impl Default for EcosystemPerformanceMonitor {
    fn default() -> Self {
        Self::new()
    }
}

impl EcosystemPerformanceMonitor {
    pub fn new() -> Self {
        Self {
            module_performance: HashMap::new(),
            system_metrics: SystemMetrics {
                total_throughput: 0.0,
                avg_latency: Duration::default(),
                error_rate: 0.0,
                resource_efficiency: 0.0,
                quality_score: 0.0,
            },
            alerts: Vec::new(),
            config: MonitoringConfig {
                samplingrate: 1.0,
                alert_thresholds: AlertThresholds {
                    latency_threshold: 1000.0,
                    error_rate_threshold: 0.05,
                    memory_threshold: 0.8,
                    cpu_threshold: 0.8,
                },
                history_retention_hours: 24,
            },
        }
    }

    /// Collect system and module metrics
    pub fn collect_metrics(&mut self) -> CoreResult<()> {
        // Update system metrics
        self.system_metrics.total_throughput = self.calculate_total_throughput();
        self.system_metrics.avg_latency = self.calculate_average_latency();
        self.system_metrics.error_rate = self.calculate_error_rate();
        self.system_metrics.resource_efficiency = self.calculate_resource_efficiency();
        self.system_metrics.quality_score = self.calculate_quality_score();

        // Check for alerts
        self.check_performance_alerts()?;

        // Clean up old metrics
        self.cleanup_old_metrics();

        Ok(())
    }

    /// Record operation duration for a module
    pub fn record_operation_duration(&mut self, module_name: &str, duration: Duration) {
        if !self.module_performance.contains_key(module_name) {
            self.module_performance
                .insert(module_name.to_string(), Vec::new());
        }

        // For simplicity, we'll create a basic metric record
        // In a real implementation, this would be more sophisticated
        let metrics = ModulePerformanceMetrics {
            avg_processing_time: duration,
            ops_per_second: 1.0 / duration.as_secs_f64(),
            success_rate: 1.0,
            quality_score: 0.8,
            efficiency_score: 0.75,
        };

        if let Some(history) = self.module_performance.get_mut(module_name) {
            history.push(metrics);

            // Keep only recent metrics (e.g., last 1000 operations)
            if history.len() > 1000 {
                history.drain(0..history.len() - 1000);
            }
        }
    }

    /// Generate comprehensive performance report
    pub fn generate_report(&self) -> EcosystemPerformanceReport {
        let mut module_metrics = HashMap::new();

        // Aggregate module metrics
        for (module_name, history) in &self.module_performance {
            if let Some(latest_metrics) = history.last() {
                module_metrics.insert(module_name.clone(), latest_metrics.clone());
            }
        }

        EcosystemPerformanceReport {
            system_metrics: self.system_metrics.clone(),
            module_metrics,
            resource_utilization: ResourceUtilization {
                cpu_usage: 0.5,
                memory_usage: 0.3,
                gpu_usage: Some(0.2),
                network_usage: 0.1,
            },
            alerts: self.alerts.clone(),
            recommendations: self.generate_recommendations(),
            timestamp: Instant::now(),
        }
    }

    /// Create optimized pipeline based on performance analysis
    pub fn create_optimized_pipeline(
        &self,
        _input: &AdvancedInput,
        _config: &CrossModuleOptimizationConfig,
    ) -> CoreResult<OptimizedPipeline> {
        // Create optimized processing pipeline based on input characteristics
        let stages = vec![
            PipelineStage {
                name: "preprocessing".to_string(),
                module: "data_transform".to_string(),
                config: HashMap::new(),
                dependencies: vec![],
            },
            PipelineStage {
                name: "computation".to_string(),
                module: "neural_compute".to_string(),
                config: HashMap::new(),
                dependencies: vec!["preprocessing".to_string()],
            },
            PipelineStage {
                name: "postprocessing".to_string(),
                module: "output_format".to_string(),
                config: HashMap::new(),
                dependencies: vec!["computation".to_string()],
            },
        ];

        Ok(OptimizedPipeline {
            stages,
            optimization_level: OptimizationLevel::Advanced,
            estimated_performance: PerformanceMetrics {
                throughput: 1000.0,
                latency: Duration::from_millis(50),
                cpu_usage: 50.0,
                memory_usage: 1024,
                gpu_usage: 30.0,
            },
        })
    }

    /// Apply pre-stage optimization
    pub fn apply_pre_stage_optimization(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
        _context: &OptimizationContext,
    ) -> CoreResult<AdvancedInput> {
        // Pre-stage optimization logic
        println!("    ⚡ Applying pre-stage optimizations for {}", stage.name);

        // Add any pre-processing optimizations here
        Ok(data)
    }

    /// Execute pipeline stage
    pub fn execute_pipeline_stage(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
    ) -> CoreResult<AdvancedInput> {
        // Execute the pipeline stage
        println!("    🔧 Executing stage: {}", stage.name);

        // In a real implementation, this would delegate to the appropriate module
        // For now, just pass through the data
        Ok(data)
    }

    /// Apply post-stage optimization
    pub fn apply_post_stage_optimization(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
        context: &mut OptimizationContext,
    ) -> CoreResult<AdvancedInput> {
        // Post-stage optimization logic
        println!(
            "    📈 Applying post-stage optimizations for {}",
            stage.name
        );

        // Update optimization context with stage results
        context.stages_completed += 1;
        context.total_memory_used += 1024; // Example value
        context.total_cpu_cycles += 1000000; // Example value

        Ok(data)
    }

    /// Add performance alert
    pub fn add_alert(&mut self, level: AlertLevel, message: String, module: Option<String>) {
        let alert = PerformanceAlert {
            level,
            message,
            module,
            timestamp: Instant::now(),
        };
        self.alerts.push(alert);

        // Keep only recent alerts
        if self.alerts.len() > 100 {
            self.alerts.drain(0..self.alerts.len() - 100);
        }
    }

    /// Calculate total system throughput
    fn calculate_total_throughput(&self) -> f64 {
        self.module_performance
            .values()
            .flat_map(|history| history.iter())
            .map(|metrics| metrics.ops_per_second)
            .sum()
    }

    /// Calculate average system latency
    fn calculate_average_latency(&self) -> Duration {
        let latencies: Vec<Duration> = self
            .module_performance
            .values()
            .flat_map(|history| history.iter())
            .map(|metrics| metrics.avg_processing_time)
            .collect();

        if latencies.is_empty() {
            return Duration::from_secs(0);
        }

        let total_nanos: u64 = latencies.iter().map(|d| d.as_nanos() as u64).sum();
        Duration::from_nanos(total_nanos / latencies.len() as u64)
    }

    /// Calculate system error rate
    fn calculate_error_rate(&self) -> f64 {
        let success_rates: Vec<f64> = self
            .module_performance
            .values()
            .flat_map(|history| history.iter())
            .map(|metrics| metrics.success_rate)
            .collect();

        if success_rates.is_empty() {
            return 0.0;
        }

        let avg_success_rate = success_rates.iter().sum::<f64>() / success_rates.len() as f64;
        1.0 - avg_success_rate
    }

    /// Calculate resource efficiency
    fn calculate_resource_efficiency(&self) -> f64 {
        let efficiency_scores: Vec<f64> = self
            .module_performance
            .values()
            .flat_map(|history| history.iter())
            .map(|metrics| metrics.efficiency_score)
            .collect();

        if efficiency_scores.is_empty() {
            return 0.0;
        }

        efficiency_scores.iter().sum::<f64>() / efficiency_scores.len() as f64
    }

    /// Calculate overall quality score
    fn calculate_quality_score(&self) -> f64 {
        let quality_scores: Vec<f64> = self
            .module_performance
            .values()
            .flat_map(|history| history.iter())
            .map(|metrics| metrics.quality_score)
            .collect();

        if quality_scores.is_empty() {
            return 0.0;
        }

        quality_scores.iter().sum::<f64>() / quality_scores.len() as f64
    }

    /// Check for performance alerts
    fn check_performance_alerts(&mut self) -> CoreResult<()> {
        // Check latency threshold
        if self.system_metrics.avg_latency.as_millis() as f64
            > self.config.alert_thresholds.latency_threshold
        {
            self.add_alert(
                AlertLevel::Warning,
                format!(
                    "System latency ({:.2}ms) exceeds threshold",
                    self.system_metrics.avg_latency.as_millis()
                ),
                None,
            );
        }

        // Check error rate threshold
        if self.system_metrics.error_rate > self.config.alert_thresholds.error_rate_threshold {
            self.add_alert(
                AlertLevel::Error,
                format!(
                    "Error rate ({:.2}%) exceeds threshold",
                    self.system_metrics.error_rate * 100.0
                ),
                None,
            );
        }

        // Check resource efficiency
        if self.system_metrics.resource_efficiency < 0.5 {
            self.add_alert(
                AlertLevel::Info,
                "Resource efficiency is below optimal levels".to_string(),
                None,
            );
        }

        Ok(())
    }

    /// Generate performance recommendations
    fn generate_recommendations(&self) -> Vec<String> {
        let mut recommendations = Vec::new();

        if self.system_metrics.resource_efficiency < 0.7 {
            recommendations.push("Consider enabling cross-module optimization".to_string());
        }

        if self.system_metrics.avg_latency.as_millis() > 500 {
            recommendations.push("Enable adaptive load balancing to reduce latency".to_string());
        }

        if self.system_metrics.error_rate > 0.01 {
            recommendations.push("Review error handling and fault tolerance settings".to_string());
        }

        if recommendations.is_empty() {
            recommendations.push("System performance is optimal".to_string());
        }

        recommendations
    }

    /// Clean up old performance metrics
    fn cleanup_old_metrics(&mut self) {
        let retention_limit = self.config.history_retention_hours as usize * 3600; // Convert to seconds

        for history in self.module_performance.values_mut() {
            if history.len() > retention_limit {
                history.drain(0..history.len() - retention_limit);
            }
        }

        // Clean up old alerts (keep last 50)
        if self.alerts.len() > 50 {
            self.alerts.drain(0..self.alerts.len() - 50);
        }
    }
}