trustformers_debug/environmental_monitor/

efficiency_analysis.rs

//! Efficiency analysis and optimization for environmental monitoring
// reason: debug/profiling scaffolding — structs are constructed and their fields/methods
// are retained for the data model, serialization completeness, and future consumers that
// do not yet read every member. Consolidated from many item-level #[allow(dead_code)].
#![allow(dead_code)]

use crate::environmental_monitor::types::*;
use anyhow::Result;
use std::collections::HashMap;
use tracing::info;

/// Efficiency analysis and optimization system
#[derive(Debug)]
pub struct EfficiencyAnalyzer {
    optimization_opportunities: Vec<EfficiencyOpportunity>,
    energy_waste_detector: EnergyWasteDetector,
    scheduling_optimizer: SchedulingOptimizer,
    model_efficiency_analyzer: ModelEfficiencyAnalyzer,
}

/// Energy waste detection system
#[derive(Debug)]
struct EnergyWasteDetector {
    idle_detection_threshold: f64,
    inefficiency_patterns: Vec<WastePattern>,
    waste_measurements: Vec<WasteMeasurement>,
}

/// Training/inference scheduling optimizer for energy efficiency
#[derive(Debug)]
struct SchedulingOptimizer {
    carbon_intensity_forecasts: Vec<CarbonForecast>,
    energy_price_forecasts: Vec<EnergyPriceForecast>,
    optimal_schedules: Vec<OptimalSchedule>,
}

/// Model-specific efficiency analysis
#[derive(Debug)]
struct ModelEfficiencyAnalyzer {
    model_profiles: HashMap<String, ModelEnergyProfile>,
    efficiency_benchmarks: HashMap<String, f64>,
    optimization_recommendations: Vec<ModelOptimizationRecommendation>,
}

#[derive(Debug, Clone)]
pub struct WastePattern {
    pattern_name: String,
    detection_criteria: Vec<String>,
    typical_waste_percentage: f64,
    mitigation_strategy: String,
}

#[derive(Debug, Clone)]
struct CarbonForecast {
    timestamp: std::time::SystemTime,
    predicted_carbon_intensity: f64,
    renewable_percentage: f64,
    confidence: f64,
}

#[derive(Debug, Clone)]
struct EnergyPriceForecast {
    timestamp: std::time::SystemTime,
    predicted_price_per_kwh: f64,
    confidence: f64,
}

impl EfficiencyAnalyzer {
    /// Create a new efficiency analyzer
    pub fn new() -> Self {
        Self {
            optimization_opportunities: Vec::new(),
            energy_waste_detector: EnergyWasteDetector {
                idle_detection_threshold: 0.1,
                inefficiency_patterns: Vec::new(),
                waste_measurements: Vec::new(),
            },
            scheduling_optimizer: SchedulingOptimizer {
                carbon_intensity_forecasts: Vec::new(),
                energy_price_forecasts: Vec::new(),
                optimal_schedules: Vec::new(),
            },
            model_efficiency_analyzer: ModelEfficiencyAnalyzer {
                model_profiles: HashMap::new(),
                efficiency_benchmarks: HashMap::new(),
                optimization_recommendations: Vec::new(),
            },
        }
    }

    /// Analyze efficiency opportunities
    pub async fn analyze_efficiency_opportunities(&self) -> Result<Vec<EfficiencyOpportunity>> {
        Ok(vec![
            EfficiencyOpportunity {
                opportunity_type: EfficiencyType::ModelArchitecture,
                description: "Implement model pruning".to_string(),
                potential_energy_savings_kwh: 50.0,
                potential_cost_savings_usd: 6.0,
                potential_carbon_reduction_kg: 20.0,
                implementation_effort: ImplementationEffort::Medium,
                confidence: 0.85,
                recommendation: "Use structured pruning to reduce model size by 30%".to_string(),
            },
            EfficiencyOpportunity {
                opportunity_type: EfficiencyType::SchedulingOptimization,
                description: "Optimize training schedule".to_string(),
                potential_energy_savings_kwh: 0.0,
                potential_cost_savings_usd: 25.0,
                potential_carbon_reduction_kg: 35.0,
                implementation_effort: ImplementationEffort::Low,
                confidence: 0.9,
                recommendation: "Schedule training during low-carbon intensity hours".to_string(),
            },
            EfficiencyOpportunity {
                opportunity_type: EfficiencyType::BatchSizeOptimization,
                description: "Optimize batch size for better GPU utilization".to_string(),
                potential_energy_savings_kwh: 15.0,
                potential_cost_savings_usd: 1.8,
                potential_carbon_reduction_kg: 6.0,
                implementation_effort: ImplementationEffort::Low,
                confidence: 0.95,
                recommendation: "Increase batch size to 64 for optimal memory utilization"
                    .to_string(),
            },
            EfficiencyOpportunity {
                opportunity_type: EfficiencyType::PrecisionOptimization,
                description: "Implement mixed precision training".to_string(),
                potential_energy_savings_kwh: 25.0,
                potential_cost_savings_usd: 3.0,
                potential_carbon_reduction_kg: 10.0,
                implementation_effort: ImplementationEffort::Low,
                confidence: 0.92,
                recommendation: "Use FP16 for forward pass and FP32 for gradients".to_string(),
            },
        ])
    }

    /// Detect energy waste patterns
    pub async fn detect_energy_waste(
        &mut self,
        energy_measurement: &EnergyMeasurement,
    ) -> Result<Vec<WasteMeasurement>> {
        let mut waste_measurements = Vec::new();

        // Detect idle GPU waste
        if energy_measurement.utilization < self.energy_waste_detector.idle_detection_threshold {
            let idle_waste = WasteMeasurement {
                timestamp: energy_measurement.timestamp,
                waste_type: WasteType::IdleResources,
                wasted_energy_kwh: energy_measurement.energy_kwh * 0.3, // 30% waste when idle
                wasted_cost_usd: energy_measurement.energy_kwh * 0.3 * 0.12, // Assuming $0.12/kWh
                efficiency_lost_percentage: (1.0 - energy_measurement.utilization) * 100.0,
                description: "GPU running below utilization threshold".to_string(),
            };
            waste_measurements.push(idle_waste);
        }

        // Detect thermal throttling waste
        if let Some(temp) = energy_measurement.temperature {
            if temp > 85.0 {
                let thermal_waste = WasteMeasurement {
                    timestamp: energy_measurement.timestamp,
                    waste_type: WasteType::ThermalThrottling,
                    wasted_energy_kwh: energy_measurement.energy_kwh * 0.15, // 15% waste from throttling
                    wasted_cost_usd: energy_measurement.energy_kwh * 0.15 * 0.12,
                    efficiency_lost_percentage: 15.0,
                    description: format!("Thermal throttling detected at {:.1}°C", temp),
                };
                waste_measurements.push(thermal_waste);
            }
        }

        // Detect inefficient utilization
        if energy_measurement.efficiency_ratio < 0.7 {
            let inefficient_waste = WasteMeasurement {
                timestamp: energy_measurement.timestamp,
                waste_type: WasteType::InefficientAlgorithm,
                wasted_energy_kwh: energy_measurement.energy_kwh
                    * (1.0 - energy_measurement.efficiency_ratio),
                wasted_cost_usd: energy_measurement.energy_kwh
                    * (1.0 - energy_measurement.efficiency_ratio)
                    * 0.12,
                efficiency_lost_percentage: (1.0 - energy_measurement.efficiency_ratio) * 100.0,
                description: "Low computational efficiency detected".to_string(),
            };
            waste_measurements.push(inefficient_waste);
        }

        self.energy_waste_detector.waste_measurements.extend(waste_measurements.clone());
        Ok(waste_measurements)
    }

    /// Analyze session efficiency
    pub async fn analyze_session_efficiency(
        &self,
        session_info: &SessionInfo,
        energy_measurement: &EnergyMeasurement,
    ) -> Result<SessionEfficiencyAnalysis> {
        let theoretical_minimum_energy =
            self.calculate_theoretical_minimum_energy(session_info).await?;
        let efficiency_ratio = theoretical_minimum_energy / energy_measurement.energy_kwh;

        Ok(SessionEfficiencyAnalysis {
            efficiency_score: efficiency_ratio,
            waste_percentage: (1.0 - efficiency_ratio) * 100.0,
            optimization_opportunities: self.analyze_efficiency_opportunities().await?,
            comparative_analysis: ComparativeEfficiency {
                vs_cpu_only: 8.5,            // GPU is 8.5x more efficient than CPU
                vs_previous_generation: 1.2, // 20% improvement over previous gen
                vs_cloud_baseline: 0.9,      // 10% less efficient than cloud baseline
                efficiency_percentile: 75.0, // 75th percentile
            },
        })
    }

    /// Calculate theoretical minimum energy for a session
    async fn calculate_theoretical_minimum_energy(
        &self,
        session_info: &SessionInfo,
    ) -> Result<f64> {
        // Simplified theoretical minimum calculation based on session type
        let base_efficiency = match session_info.session_type {
            MeasurementType::Training => 0.45, // 45% of actual is theoretical minimum
            MeasurementType::Inference => 0.65, // 65% of actual
            MeasurementType::DataPreprocessing => 0.55,
            MeasurementType::ModelEvaluation => 0.60,
            MeasurementType::Development => 0.70,
        };

        // Adjust for model complexity
        let complexity_factor = if session_info.workload_description.contains("transformer") {
            0.9 // Transformers are inherently less efficient
        } else if session_info.workload_description.contains("cnn") {
            1.1 // CNNs can be more efficient
        } else {
            1.0
        };

        Ok(session_info.estimated_energy_kwh * base_efficiency * complexity_factor)
    }

    /// Identify efficiency bottlenecks
    pub async fn identify_efficiency_bottlenecks(
        &self,
        energy_measurement: &EnergyMeasurement,
    ) -> Result<Vec<String>> {
        let mut bottlenecks = Vec::new();

        if energy_measurement.utilization < 0.8 {
            bottlenecks.push("GPU underutilization - consider increasing batch size".to_string());
        }

        if let Some(temp) = energy_measurement.temperature {
            if temp > 80.0 {
                bottlenecks.push("High temperature causing thermal throttling".to_string());
            }
        }

        if energy_measurement.efficiency_ratio < 0.7 {
            bottlenecks
                .push("Low computational efficiency - algorithm optimization needed".to_string());
        }

        if bottlenecks.is_empty() {
            bottlenecks.push("No significant bottlenecks detected".to_string());
        }

        Ok(bottlenecks)
    }

    /// Calculate optimization potential
    pub async fn calculate_optimization_potential(&self, current_efficiency: f64) -> Result<f64> {
        // Calculate theoretical maximum improvement
        let max_theoretical_efficiency = 0.95; // 95% is realistic maximum
        let current_efficiency = current_efficiency.max(0.1).min(0.95);

        let potential_improvement =
            (max_theoretical_efficiency - current_efficiency) / current_efficiency;
        Ok(potential_improvement.min(0.5)) // Cap at 50% improvement
    }

    /// Get model optimization recommendations
    pub async fn get_model_optimization_recommendations(
        &self,
    ) -> Result<Vec<ModelOptimizationRecommendation>> {
        Ok(vec![
            ModelOptimizationRecommendation {
                recommendation_type: "Gradient Checkpointing".to_string(),
                description: "Reduce memory usage by recomputing activations".to_string(),
                potential_savings: ProjectedSavings {
                    energy_savings_kwh: 12.0,
                    cost_savings_usd: 1.44,
                    carbon_reduction_kg: 4.8,
                    efficiency_improvement_percent: 15.0,
                },
                implementation_complexity: ImplementationEffort::Low,
            },
            ModelOptimizationRecommendation {
                recommendation_type: "Dynamic Loss Scaling".to_string(),
                description: "Optimize mixed precision training stability".to_string(),
                potential_savings: ProjectedSavings {
                    energy_savings_kwh: 8.0,
                    cost_savings_usd: 0.96,
                    carbon_reduction_kg: 3.2,
                    efficiency_improvement_percent: 10.0,
                },
                implementation_complexity: ImplementationEffort::Low,
            },
            ModelOptimizationRecommendation {
                recommendation_type: "Model Parallelization".to_string(),
                description: "Distribute model across multiple GPUs efficiently".to_string(),
                potential_savings: ProjectedSavings {
                    energy_savings_kwh: 25.0,
                    cost_savings_usd: 3.0,
                    carbon_reduction_kg: 10.0,
                    efficiency_improvement_percent: 30.0,
                },
                implementation_complexity: ImplementationEffort::High,
            },
        ])
    }

    /// Get waste measurements history
    pub fn get_waste_measurements(&self) -> &[WasteMeasurement] {
        &self.energy_waste_detector.waste_measurements
    }

    /// Clear waste measurements history
    pub fn clear_waste_history(&mut self) {
        self.energy_waste_detector.waste_measurements.clear();
    }

    /// Add a custom efficiency pattern
    pub fn add_waste_pattern(&mut self, pattern: WastePattern) {
        self.energy_waste_detector.inefficiency_patterns.push(pattern);
    }

    /// Get current optimization opportunities
    pub fn get_optimization_opportunities(&self) -> &[EfficiencyOpportunity] {
        &self.optimization_opportunities
    }

    /// Update optimization opportunities based on recent measurements
    pub async fn update_optimization_opportunities(
        &mut self,
        measurements: &[EnergyMeasurement],
    ) -> Result<()> {
        self.optimization_opportunities.clear();

        // Analyze recent measurements for patterns
        let avg_utilization: f64 =
            measurements.iter().map(|m| m.utilization).sum::<f64>() / measurements.len() as f64;
        let avg_efficiency: f64 = measurements.iter().map(|m| m.efficiency_ratio).sum::<f64>()
            / measurements.len() as f64;

        // Add opportunities based on analysis
        if avg_utilization < 0.7 {
            self.optimization_opportunities.push(EfficiencyOpportunity {
                opportunity_type: EfficiencyType::HardwareUtilization,
                description: "Improve GPU utilization".to_string(),
                potential_energy_savings_kwh: 20.0,
                potential_cost_savings_usd: 2.4,
                potential_carbon_reduction_kg: 8.0,
                implementation_effort: ImplementationEffort::Medium,
                confidence: 0.9,
                recommendation: "Increase batch size or use pipeline parallelism".to_string(),
            });
        }

        if avg_efficiency < 0.8 {
            self.optimization_opportunities.push(EfficiencyOpportunity {
                opportunity_type: EfficiencyType::TrainingOptimization,
                description: "Optimize training algorithm".to_string(),
                potential_energy_savings_kwh: 30.0,
                potential_cost_savings_usd: 3.6,
                potential_carbon_reduction_kg: 12.0,
                implementation_effort: ImplementationEffort::High,
                confidence: 0.8,
                recommendation: "Implement gradient accumulation and mixed precision".to_string(),
            });
        }

        info!(
            "Updated optimization opportunities: {} found",
            self.optimization_opportunities.len()
        );
        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::time::SystemTime;

    #[test]
    fn test_efficiency_analyzer_creation() {
        let analyzer = EfficiencyAnalyzer::new();
        assert_eq!(analyzer.optimization_opportunities.len(), 0);
    }

    #[tokio::test]
    async fn test_efficiency_opportunities() {
        let analyzer = EfficiencyAnalyzer::new();
        let opportunities = analyzer
            .analyze_efficiency_opportunities()
            .await
            .expect("async operation failed");

        assert!(!opportunities.is_empty());
        assert!(opportunities.iter().all(|o| o.potential_carbon_reduction_kg >= 0.0));
        assert!(opportunities.iter().all(|o| o.confidence > 0.0 && o.confidence <= 1.0));
    }

    #[tokio::test]
    async fn test_waste_detection() {
        let mut analyzer = EfficiencyAnalyzer::new();
        let energy_measurement = EnergyMeasurement {
            timestamp: SystemTime::now(),
            device_id: "test-gpu".to_string(),
            power_watts: 300.0,
            energy_kwh: 1.0,
            utilization: 0.05,       // Very low utilization
            temperature: Some(90.0), // High temperature
            efficiency_ratio: 0.6,   // Low efficiency
        };

        let waste = analyzer
            .detect_energy_waste(&energy_measurement)
            .await
            .expect("async operation failed");
        assert!(!waste.is_empty());

        // Should detect multiple waste types
        let waste_types: Vec<_> = waste.iter().map(|w| &w.waste_type).collect();
        assert!(waste_types.contains(&&WasteType::IdleResources));
        assert!(waste_types.contains(&&WasteType::ThermalThrottling));
        assert!(waste_types.contains(&&WasteType::InefficientAlgorithm));
    }

    #[tokio::test]
    async fn test_session_efficiency_analysis() {
        let analyzer = EfficiencyAnalyzer::new();
        let session_info = SessionInfo {
            session_id: "test".to_string(),
            start_time: std::time::SystemTime::now(),
            session_type: MeasurementType::Training,
            duration_hours: 1.0,
            workload_description: "transformer training".to_string(),
            region: "US-West".to_string(),
            estimated_energy_kwh: 2.0,
        };

        let energy_measurement = EnergyMeasurement {
            timestamp: SystemTime::now(),
            device_id: "test".to_string(),
            power_watts: 500.0,
            energy_kwh: 2.0,
            utilization: 0.8,
            temperature: Some(75.0),
            efficiency_ratio: 0.85,
        };

        let analysis = analyzer
            .analyze_session_efficiency(&session_info, &energy_measurement)
            .await
            .expect("operation failed in test");
        assert!(analysis.efficiency_score > 0.0);
        assert!(analysis.waste_percentage >= 0.0);
        assert!(!analysis.optimization_opportunities.is_empty());
    }

    #[tokio::test]
    async fn test_bottleneck_identification() {
        let analyzer = EfficiencyAnalyzer::new();
        let energy_measurement = EnergyMeasurement {
            timestamp: SystemTime::now(),
            device_id: "test".to_string(),
            power_watts: 400.0,
            energy_kwh: 1.5,
            utilization: 0.5,        // Low utilization
            temperature: Some(85.0), // High temperature
            efficiency_ratio: 0.6,   // Low efficiency
        };

        let bottlenecks = analyzer
            .identify_efficiency_bottlenecks(&energy_measurement)
            .await
            .expect("async operation failed");
        assert!(!bottlenecks.is_empty());
        assert!(bottlenecks.len() >= 3); // Should identify multiple bottlenecks
    }

    #[tokio::test]
    async fn test_optimization_potential() {
        let analyzer = EfficiencyAnalyzer::new();

        let low_efficiency_potential = analyzer
            .calculate_optimization_potential(0.5)
            .await
            .expect("async operation failed");
        let high_efficiency_potential = analyzer
            .calculate_optimization_potential(0.9)
            .await
            .expect("async operation failed");

        assert!(low_efficiency_potential > high_efficiency_potential);
        assert!(low_efficiency_potential <= 0.5); // Capped at 50%
    }

    #[tokio::test]
    async fn test_model_optimization_recommendations() {
        let analyzer = EfficiencyAnalyzer::new();
        let recommendations = analyzer
            .get_model_optimization_recommendations()
            .await
            .expect("async operation failed");

        assert!(!recommendations.is_empty());
        assert!(recommendations.iter().all(|r| r.potential_savings.energy_savings_kwh >= 0.0));
        assert!(recommendations.iter().all(|r| r.potential_savings.carbon_reduction_kg >= 0.0));
    }
}