scirs2_core/advanced_ecosystem_integration/

resources.rs

//! Resource management and allocation

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

/// Resource manager for the ecosystem
#[allow(dead_code)]
#[derive(Debug)]
pub struct EcosystemResourceManager {
    /// Available resources
    available_resources: ResourcePool,
    /// Resource allocations
    allocations: HashMap<String, ResourceAllocation>,
    /// Load balancer
    #[allow(dead_code)]
    load_balancer: LoadBalancer,
    /// Resource monitoring
    #[allow(dead_code)]
    resource_monitor: ResourceMonitor,
}

/// Pool of available resources
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourcePool {
    /// CPU cores available
    pub cpu_cores: usize,
    /// Memory available (MB)
    pub memory_mb: usize,
    /// GPU devices available
    pub gpu_devices: usize,
    /// Network bandwidth (MB/s)
    pub network_bandwidth: f64,
}

/// Resource allocation for a module
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ResourceAllocation {
    /// Allocated CPU cores
    pub cpu_cores: f64,
    /// Allocated memory (MB)
    pub memory_mb: usize,
    /// Allocated GPU fraction
    pub gpu_fraction: Option<f64>,
    /// Allocated bandwidth (MB/s)
    pub bandwidth: f64,
    /// Priority level
    pub priority: Priority,
}

/// Load balancer for distributing work
#[allow(dead_code)]
#[derive(Debug)]
pub struct LoadBalancer {
    /// Current load distribution
    #[allow(dead_code)]
    load_distribution: HashMap<String, f64>,
    /// Balancing strategy
    #[allow(dead_code)]
    strategy: LoadBalancingStrategy,
    /// Performance history
    #[allow(dead_code)]
    performance_history: Vec<LoadBalancingMetrics>,
}

/// Load balancing strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    RoundRobin,
    WeightedRoundRobin,
    LeastConnections,
    PerformanceBased,
    ResourceBased,
    AIOptimized,
}

/// Load balancing metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LoadBalancingMetrics {
    /// Distribution efficiency
    pub distribution_efficiency: f64,
    /// Response time variance
    pub response_time_variance: f64,
    /// Resource utilization balance
    pub utilization_balance: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Resource monitor
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourceMonitor {
    /// Current resource usage
    #[allow(dead_code)]
    current_usage: ResourceUtilization,
    /// Usage history
    #[allow(dead_code)]
    usage_history: Vec<ResourceSnapshot>,
    /// Prediction model
    #[allow(dead_code)]
    prediction_model: Option<ResourcePredictionModel>,
}

/// Snapshot of resource usage at a point in time
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ResourceSnapshot {
    /// Resource utilization
    pub utilization: ResourceUtilization,
    /// Timestamp
    pub timestamp: Instant,
    /// Associated workload
    pub workload_info: Option<String>,
}

/// Model for predicting resource usage
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourcePredictionModel {
    /// Model parameters
    #[allow(dead_code)]
    parameters: Vec<f64>,
    /// Prediction accuracy
    #[allow(dead_code)]
    accuracy: f64,
    /// Last training timestamp
    #[allow(dead_code)]
    last_trained: Instant,
}

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

impl EcosystemResourceManager {
    /// Create a new resource manager
    pub fn new() -> Self {
        Self {
            available_resources: ResourcePool {
                cpu_cores: 8,
                memory_mb: 16384,
                gpu_devices: 1,
                network_bandwidth: 1000.0,
            },
            allocations: HashMap::new(),
            load_balancer: LoadBalancer {
                load_distribution: HashMap::new(),
                strategy: LoadBalancingStrategy::PerformanceBased,
                performance_history: Vec::new(),
            },
            resource_monitor: ResourceMonitor {
                current_usage: ResourceUtilization {
                    cpu_usage: 0.0,
                    memory_usage: 0.0,
                    gpu_usage: None,
                    network_usage: 0.0,
                },
                usage_history: Vec::new(),
                prediction_model: None,
            },
        }
    }

    /// Allocate resources for a module
    pub fn allocate_resources(&mut self, module_name: &str) -> CoreResult<()> {
        let allocation = ResourceAllocation {
            cpu_cores: 1.0,
            memory_mb: 512,
            gpu_fraction: Some(0.1),
            bandwidth: 10.0,
            priority: Priority::Normal,
        };

        self.allocations.insert(module_name.to_string(), allocation);
        println!("    📊 Allocated resources for module: {}", module_name);
        Ok(())
    }

    /// Deallocate resources for a module
    pub fn deallocate_resources(&mut self, module_name: &str) -> CoreResult<()> {
        if self.allocations.remove(module_name).is_some() {
            println!("    🔄 Deallocated resources for module: {}", module_name);
        }
        Ok(())
    }

    /// Rebalance resources based on current usage patterns
    pub fn rebalance_resources(&mut self) -> CoreResult<()> {
        println!("    ⚖️  Rebalancing resource allocations...");

        // Calculate total resource demands
        let mut total_cpu_demand = 0.0;
        let mut total_memory_demand = 0;

        for allocation in self.allocations.values() {
            total_cpu_demand += allocation.cpu_cores;
            total_memory_demand += allocation.memory_mb;
        }

        // Redistribute if over-allocated
        if total_cpu_demand > self.available_resources.cpu_cores as f64 {
            let scale_factor = self.available_resources.cpu_cores as f64 / total_cpu_demand;
            for allocation in self.allocations.values_mut() {
                allocation.cpu_cores *= scale_factor;
            }
            println!("    📉 Scaled down CPU allocations by factor: {scale_factor:.2}");
        }

        if total_memory_demand > self.available_resources.memory_mb {
            let scale_factor =
                self.available_resources.memory_mb as f64 / total_memory_demand as f64;
            for allocation in self.allocations.values_mut() {
                allocation.memory_mb = (allocation.memory_mb as f64 * scale_factor) as usize;
            }
            println!("    📉 Scaled down memory allocations by factor: {scale_factor:.2}");
        }

        Ok(())
    }

    /// Apply predictive scaling based on historical patterns
    pub fn apply_predictive_scaling(&mut self) -> CoreResult<()> {
        println!("    🔮 Applying predictive scaling...");

        // Simple predictive scaling - in real implementation would use ML models
        for (module_name, allocation) in &mut self.allocations {
            // Simulate prediction of increased demand
            if module_name.contains("neural") || module_name.contains("ml") {
                allocation.cpu_cores *= 1.2; // 20% increase for ML workloads
                allocation.memory_mb = (allocation.memory_mb as f64 * 1.3) as usize; // 30% increase
                println!("    📈 Predictively scaled up resources for ML module: {module_name}");
            }
        }

        Ok(())
    }

    /// Get current resource utilization
    pub fn get_resource_utilization(&self) -> ResourceUtilization {
        // Calculate current utilization based on allocations
        let mut cpu_usage = 0.0;
        let mut memory_usage = 0.0;
        let mut gpu_usage = 0.0;
        let mut network_usage = 0.0;

        for allocation in self.allocations.values() {
            cpu_usage += allocation.cpu_cores;
            memory_usage += allocation.memory_mb as f64;
            if let Some(gpu_frac) = allocation.gpu_fraction {
                gpu_usage += gpu_frac;
            }
            network_usage += allocation.bandwidth;
        }

        ResourceUtilization {
            cpu_usage: cpu_usage / self.available_resources.cpu_cores as f64,
            memory_usage: memory_usage / self.available_resources.memory_mb as f64,
            gpu_usage: if gpu_usage > 0.0 {
                Some(gpu_usage)
            } else {
                None
            },
            network_usage: network_usage / self.available_resources.network_bandwidth,
        }
    }

    /// Get resource allocation for a specific module
    pub fn get_allocation(&self, module_name: &str) -> Option<&ResourceAllocation> {
        self.allocations.get(module_name)
    }

    /// Update resource allocation for a module
    pub fn update_allocation(
        &mut self,
        module_name: &str,
        allocation: ResourceAllocation,
    ) -> CoreResult<()> {
        self.allocations.insert(module_name.to_string(), allocation);
        println!(
            "    🔄 Updated resource allocation for module: {}",
            module_name
        );
        Ok(())
    }

    /// Get available resources
    pub fn get_available_resources(&self) -> &ResourcePool {
        &self.available_resources
    }

    /// Set available resources
    pub fn set_available_resources(&mut self, resources: ResourcePool) {
        self.available_resources = resources;
        println!("    📊 Updated available resource pool");
    }

    /// Optimize resource allocation based on performance metrics
    pub fn optimize_allocation(
        &mut self,
        performance_data: &HashMap<String, ModulePerformanceMetrics>,
    ) -> CoreResult<()> {
        println!("    🎯 Optimizing resource allocation based on performance...");

        for (module_name, metrics) in performance_data {
            if let Some(allocation) = self.allocations.get_mut(module_name) {
                // Increase resources for high-performing modules
                if metrics.efficiency_score > 0.8 && metrics.success_rate > 0.95 {
                    allocation.cpu_cores *= 1.1;
                    allocation.memory_mb = (allocation.memory_mb as f64 * 1.1) as usize;
                    println!(
                        "    📈 Increased resources for high-performing module: {}",
                        module_name
                    );
                }
                // Decrease resources for underperforming modules
                else if metrics.efficiency_score < 0.5 || metrics.success_rate < 0.8 {
                    allocation.cpu_cores *= 0.9;
                    allocation.memory_mb = (allocation.memory_mb as f64 * 0.9) as usize;
                    println!(
                        "    📉 Decreased resources for underperforming module: {}",
                        module_name
                    );
                }
            }
        }

        // Rebalance to ensure we don't exceed available resources
        self.rebalance_resources()?;

        Ok(())
    }

    /// Get resource efficiency metrics
    pub fn get_efficiency_metrics(&self) -> HashMap<String, f64> {
        let mut efficiency_metrics = HashMap::new();
        let utilization = self.get_resource_utilization();

        efficiency_metrics.insert("cpu_efficiency".to_string(), utilization.cpu_usage);
        efficiency_metrics.insert("memory_efficiency".to_string(), utilization.memory_usage);
        efficiency_metrics.insert("network_efficiency".to_string(), utilization.network_usage);

        if let Some(gpu_usage) = utilization.gpu_usage {
            efficiency_metrics.insert("gpu_efficiency".to_string(), gpu_usage);
        }

        efficiency_metrics
    }

    /// Predict future resource needs
    pub fn predict_resource_needs(&self, time_horizon: Duration) -> CoreResult<ResourcePool> {
        // Simple prediction based on current trends
        // In a real implementation, this would use sophisticated ML models

        let current_utilization = self.get_resource_utilization();

        // Assume linear growth over time horizon
        let growth_factor = 1.0 + (time_horizon.as_secs_f64() / 3600.0) * 0.1; // 10% growth per hour

        let predicted_resources = ResourcePool {
            cpu_cores: (self.available_resources.cpu_cores as f64
                * current_utilization.cpu_usage
                * growth_factor) as usize,
            memory_mb: (self.available_resources.memory_mb as f64
                * current_utilization.memory_usage
                * growth_factor) as usize,
            gpu_devices: if current_utilization.gpu_usage.is_some() {
                (self.available_resources.gpu_devices as f64 * growth_factor) as usize
            } else {
                0
            },
            network_bandwidth: self.available_resources.network_bandwidth
                * current_utilization.network_usage
                * growth_factor,
        };

        Ok(predicted_resources)
    }

    /// Handle resource shortage scenarios
    pub fn handle_resource_shortage(&mut self) -> CoreResult<()> {
        println!("    ⚠️  Handling resource shortage...");

        // Reduce allocations for low-priority modules
        for allocation in self.allocations.values_mut() {
            match allocation.priority {
                Priority::Low => {
                    allocation.cpu_cores *= 0.5;
                    allocation.memory_mb = (allocation.memory_mb as f64 * 0.5) as usize;
                }
                Priority::Normal => {
                    allocation.cpu_cores *= 0.8;
                    allocation.memory_mb = (allocation.memory_mb as f64 * 0.8) as usize;
                }
                _ => {} // Keep high/critical/realtime unchanged
            }
        }

        println!("    📉 Reduced allocations for lower priority modules");
        Ok(())
    }
}