scirs2-vision 0.5.1

//! Predictive scaling system for computer vision workloads
//!
//! This module implements time series analysis and prediction for automatically
//! scaling processing resources based on predicted workload patterns.

use std::collections::VecDeque;
use std::time::{Duration, Instant};

/// Predictive scaling system using time series analysis
#[derive(Debug)]
pub struct PredictiveScaler {
    /// Historical workload data
    workload_history: VecDeque<WorkloadMeasurement>,
    /// Prediction model parameters
    model_params: PredictionModel,
    /// Scaling predictions
    scaling_predictions: VecDeque<ScalingPrediction>,
    /// Current scaling state
    current_scaling: ScalingState,
}

/// Workload measurement
#[derive(Debug, Clone)]
pub struct WorkloadMeasurement {
    /// Timestamp
    pub timestamp: Instant,
    /// Processing load (0-1)
    pub processing_load: f64,
    /// Input complexity
    pub input_complexity: f64,
    /// Required resources
    pub required_resources: ResourceRequirement,
}

/// Resource requirements
#[derive(Debug, Clone)]
pub struct ResourceRequirement {
    /// CPU cores needed
    pub cpu_cores: f64,
    /// Memory requirement (MB)
    pub memory_mb: f64,
    /// GPU utilization needed
    pub gpu_utilization: f64,
}

/// Time series prediction model
#[derive(Debug, Clone)]
pub struct PredictionModel {
    /// Model type
    pub model_type: ModelType,
    /// Model parameters
    pub parameters: Vec<f64>,
    /// Prediction window (seconds)
    pub _predictionwindow: f64,
    /// Model accuracy
    pub accuracy: f64,
}

/// Types of prediction models
#[derive(Debug, Clone)]
pub enum ModelType {
    /// Linear regression
    LinearRegression,
    /// ARIMA model
    ARIMA {
        /// Autoregressive order
        p: usize,
        /// Degree of differencing
        d: usize,
        /// Moving average order
        q: usize,
    },
    /// Neural network
    NeuralNetwork {
        /// Sizes of hidden layers
        hidden_layers: Vec<usize>,
    },
    /// Ensemble method
    Ensemble {
        /// Component models in the ensemble
        models: Vec<ModelType>,
    },
}

/// Scaling prediction
#[derive(Debug, Clone)]
pub struct ScalingPrediction {
    /// Time horizon for prediction
    pub horizon: Duration,
    /// Predicted resource needs
    pub predicted_resources: ResourceRequirement,
    /// Confidence level
    pub confidence: f64,
    /// Prediction timestamp
    pub timestamp: Instant,
}

/// Current scaling state
#[derive(Debug, Clone)]
pub struct ScalingState {
    /// Active CPU cores
    pub active_cores: usize,
    /// Allocated memory (MB)
    pub allocated_memory: f64,
    /// GPU utilization
    pub gpu_utilization: f64,
    /// Last scaling action_
    pub last_scaling: Instant,
}

impl PredictiveScaler {
    /// Create a new predictive scaler
    pub fn new(_predictionwindow: f64) -> Self {
        Self {
            workload_history: VecDeque::with_capacity(10000),
            model_params: PredictionModel {
                model_type: ModelType::LinearRegression,
                parameters: vec![0.0, 1.0], // Simple linear model
                _predictionwindow,
                accuracy: 0.7,
            },
            scaling_predictions: VecDeque::with_capacity(100),
            current_scaling: ScalingState {
                active_cores: 1,
                allocated_memory: 512.0,
                gpu_utilization: 0.0,
                last_scaling: Instant::now(),
            },
        }
    }

    /// Record workload measurement
    pub fn record_workload(&mut self, measurement: WorkloadMeasurement) {
        self.workload_history.push_back(measurement);

        // Keep bounded history
        if self.workload_history.len() > 10000 {
            self.workload_history.pop_front();
        }

        // Update model if enough data
        if self.workload_history.len() > 100 {
            self.update_prediction_model();
        }
    }

    /// Update prediction model parameters
    fn update_prediction_model(&mut self) {
        match &self.model_params.model_type {
            ModelType::LinearRegression => {
                self.update_linear_regression();
            }
            ModelType::ARIMA { .. } => {
                // Would implement ARIMA parameter estimation
                self.update_arima_model();
            }
            _ => {
                // Other model types would be implemented here
            }
        }
    }

    /// Update linear regression model
    fn update_linear_regression(&mut self) {
        if self.workload_history.len() < 10 {
            return;
        }

        // Simple linear regression on recent data
        let recent_data: Vec<_> = self.workload_history.iter().rev().take(100).collect();

        let n = recent_data.len() as f64;
        let mut sum_x = 0.0;
        let mut sum_y = 0.0;
        let mut sum_xy = 0.0;
        let mut sum_x2 = 0.0;

        for (i, measurement) in recent_data.iter().enumerate() {
            let x = i as f64;
            let y = measurement.processing_load;

            sum_x += x;
            sum_y += y;
            sum_xy += x * y;
            sum_x2 += x * x;
        }

        // Calculate regression coefficients
        let slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);
        let intercept = (sum_y - slope * sum_x) / n;

        self.model_params.parameters = vec![intercept, slope];
    }

    /// Update ARIMA model (simplified)
    fn update_arima_model(&mut self) {
        // In a real implementation, this would fit ARIMA parameters
        // using maximum likelihood estimation or similar methods
    }

    /// Generate scaling predictions
    pub fn generate_predictions(&mut self, horizons: Vec<Duration>) -> Vec<ScalingPrediction> {
        let mut predictions = Vec::new();
        let current_time = Instant::now();

        for horizon in horizons {
            let predictedload = self.predict_load(horizon);
            let predicted_resources = self.load_to_resources(predictedload);
            let confidence = self.calculate_confidence(horizon);

            predictions.push(ScalingPrediction {
                horizon,
                predicted_resources,
                confidence,
                timestamp: current_time,
            });
        }

        // Store predictions
        for prediction in &predictions {
            self.scaling_predictions.push_back(prediction.clone());
        }

        // Keep bounded prediction history
        if self.scaling_predictions.len() > 100 {
            self.scaling_predictions.pop_front();
        }

        predictions
    }

    /// Predict load for a given time horizon
    fn predict_load(&self, horizon: Duration) -> f64 {
        let horizon_secs = horizon.as_secs_f64();

        match &self.model_params.model_type {
            ModelType::LinearRegression => {
                let intercept = self.model_params.parameters[0];
                let slope = self.model_params.parameters[1];

                // Project current trend forward
                let current_index = self.workload_history.len() as f64;
                let future_index = current_index + horizon_secs / 60.0; // Assume 1 minute intervals

                (intercept + slope * future_index).clamp(0.0, 1.0)
            }
            _ => {
                // Default to current load if model not implemented
                self.workload_history
                    .back()
                    .map(|m| m.processing_load)
                    .unwrap_or(0.5)
            }
        }
    }

    /// Convert load prediction to resource requirements
    fn load_to_resources(&self, predictedload: f64) -> ResourceRequirement {
        ResourceRequirement {
            cpu_cores: (predictedload * 8.0).ceil(), // Scale up to 8 cores max
            memory_mb: 512.0 + predictedload * 1536.0, // 512MB to 2GB
            gpu_utilization: (predictedload * 0.8).min(1.0), // Up to 80% GPU
        }
    }

    /// Calculate prediction confidence
    fn calculate_confidence(&self, horizon: Duration) -> f64 {
        let base_confidence = self.model_params.accuracy;
        let horizon_penalty = (horizon.as_secs_f64() / 3600.0) * 0.1; // Decrease 10% per hour

        (base_confidence - horizon_penalty).max(0.1)
    }

    /// Get scaling recommendations
    pub fn get_scaling_recommendations(&self) -> Vec<ScalingRecommendation> {
        let mut recommendations = Vec::new();

        if let Some(latest_prediction) = self.scaling_predictions.back() {
            let current_resources = &self.current_scaling;
            let predicted_resources = &latest_prediction.predicted_resources;

            // CPU scaling recommendation
            if predicted_resources.cpu_cores > current_resources.active_cores as f64 + 1.0 {
                recommendations.push(ScalingRecommendation {
                    resource_type: ResourceType::CPU,
                    action_: ScalingAction::ScaleUp,
                    magnitude: (predicted_resources.cpu_cores
                        - current_resources.active_cores as f64)
                        as usize,
                    confidence: latest_prediction.confidence,
                    reason: "Predicted CPU demand increase".to_string(),
                });
            } else if predicted_resources.cpu_cores < current_resources.active_cores as f64 - 1.0 {
                recommendations.push(ScalingRecommendation {
                    resource_type: ResourceType::CPU,
                    action_: ScalingAction::ScaleDown,
                    magnitude: (current_resources.active_cores as f64
                        - predicted_resources.cpu_cores) as usize,
                    confidence: latest_prediction.confidence,
                    reason: "Predicted CPU demand decrease".to_string(),
                });
            }

            // Memory scaling recommendation
            if predicted_resources.memory_mb > current_resources.allocated_memory * 1.2 {
                recommendations.push(ScalingRecommendation {
                    resource_type: ResourceType::Memory,
                    action_: ScalingAction::ScaleUp,
                    magnitude: (predicted_resources.memory_mb - current_resources.allocated_memory)
                        as usize,
                    confidence: latest_prediction.confidence,
                    reason: "Predicted memory demand increase".to_string(),
                });
            }
        }

        recommendations
    }
}

/// Scaling recommendation
#[derive(Debug, Clone)]
pub struct ScalingRecommendation {
    /// Type of resource to scale
    pub resource_type: ResourceType,
    /// Scaling action_
    pub action_: ScalingAction,
    /// Magnitude of scaling
    pub magnitude: usize,
    /// Confidence in recommendation
    pub confidence: f64,
    /// Reason for recommendation
    pub reason: String,
}

/// Resource types for scaling
#[derive(Debug, Clone)]
pub enum ResourceType {
    /// CPU resources
    CPU,
    /// Memory resources
    Memory,
    /// GPU resources
    GPU,
    /// Network resources
    Network,
}

/// Scaling actions
#[derive(Debug, Clone)]
pub enum ScalingAction {
    /// Scale up resources
    ScaleUp,
    /// Scale down resources
    ScaleDown,
    /// Maintain current resource levels
    Maintain,
}