use crate::cross_module_performance_profiler::{
calculate_percentage_change, ModulePerformanceMonitor, PredictivePerformanceEngine,
ResourceAllocator,
};
use crate::cross_module_performance_types::{
AnomalyEvent, AnomalyType, CacheStats, CoordinatorConfig, ModuleMetrics,
OptimizationRecommendation, OptimizationResults, OptimizationType, PerformanceImpact, Priority,
SeverityLevel,
};
use anyhow::{anyhow, Result};
use chrono::Utc;
use std::collections::HashMap;
use std::sync::atomic::{AtomicU64, AtomicUsize, Ordering};
use std::sync::{Arc, RwLock};
use std::time::Duration;
use tokio::time;
use tracing::{info, warn};
#[derive(Debug)]
pub struct GlobalPerformanceMetrics {
total_optimizations: AtomicU64,
avg_performance_gain: Arc<RwLock<f64>>,
success_rate: Arc<RwLock<f64>>,
last_optimization: Arc<RwLock<Option<chrono::DateTime<chrono::Utc>>>>,
}
impl GlobalPerformanceMetrics {
pub fn new() -> Self {
Self {
total_optimizations: AtomicU64::new(0),
avg_performance_gain: Arc::new(RwLock::new(0.0)),
success_rate: Arc::new(RwLock::new(0.0)),
last_optimization: Arc::new(RwLock::new(None)),
}
}
pub fn update(&mut self, results: &OptimizationResults) {
self.total_optimizations.fetch_add(1, Ordering::SeqCst);
{
let mut gain = self.avg_performance_gain.write().expect("lock poisoned");
*gain = (*gain + results.total_performance_gain) / 2.0;
}
{
let mut rate = self.success_rate.write().expect("lock poisoned");
let success = results.optimizations_applied as f64
/ (results.optimizations_applied + results.optimization_failures).max(1) as f64;
*rate = (*rate + success) / 2.0;
}
{
let mut last = self.last_optimization.write().expect("lock poisoned");
*last = Some(Utc::now());
}
}
}
impl Default for GlobalPerformanceMetrics {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug)]
pub struct OptimizationCache {
pub cache: HashMap<String, crate::cross_module_performance_types::CachedOptimization>,
pub stats: CacheStats,
}
impl OptimizationCache {
pub fn new() -> Self {
Self {
cache: HashMap::new(),
stats: CacheStats {
hits: AtomicU64::new(0),
misses: AtomicU64::new(0),
size: AtomicUsize::new(0),
},
}
}
}
impl Default for OptimizationCache {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug)]
pub struct CrossModulePerformanceCoordinator {
config: CoordinatorConfig,
module_monitors: Arc<RwLock<HashMap<String, ModulePerformanceMonitor>>>,
resource_allocator: ResourceAllocator,
predictive_engine: PredictivePerformanceEngine,
optimization_cache: Arc<RwLock<OptimizationCache>>,
global_metrics: Arc<RwLock<GlobalPerformanceMetrics>>,
}
impl CrossModulePerformanceCoordinator {
pub fn new(config: CoordinatorConfig) -> Self {
Self {
config,
module_monitors: Arc::new(RwLock::new(HashMap::new())),
resource_allocator: ResourceAllocator::new(),
predictive_engine: PredictivePerformanceEngine::new(),
optimization_cache: Arc::new(RwLock::new(OptimizationCache::new())),
global_metrics: Arc::new(RwLock::new(GlobalPerformanceMetrics::new())),
}
}
pub async fn register_module(&self, module_name: String) -> Result<()> {
let monitor = ModulePerformanceMonitor::new(module_name.clone());
{
let mut monitors = self.module_monitors.write().expect("lock poisoned");
monitors.insert(module_name.clone(), monitor);
}
info!(
"Registered module '{}' for performance monitoring",
module_name
);
Ok(())
}
pub async fn update_module_metrics(
&self,
module_name: &str,
metrics: ModuleMetrics,
) -> Result<()> {
let monitor = {
let monitors = self.module_monitors.read().expect("lock poisoned");
monitors.get(module_name).cloned()
};
if let Some(monitor) = monitor {
monitor.update_metrics(metrics).await?;
} else {
return Err(anyhow!("Module '{}' not registered", module_name));
}
Ok(())
}
pub async fn optimize_performance(&self) -> Result<OptimizationResults> {
info!("Starting cross-module performance optimization");
let mut results = OptimizationResults::new();
let performance_data = self.collect_performance_data().await?;
let anomalies = self
.predictive_engine
.detect_anomalies(&performance_data)
.await?;
results.anomalies_detected = anomalies.len();
let recommendations = self
.generate_optimization_recommendations(&performance_data, &anomalies)
.await?;
results.recommendations = recommendations.clone();
for recommendation in recommendations {
match self.apply_optimization(recommendation).await {
Ok(impact) => {
results.optimizations_applied += 1;
results.total_performance_gain += impact.overall_score;
}
Err(e) => {
warn!("Failed to apply optimization: {}", e);
results.optimization_failures += 1;
}
}
}
self.update_global_metrics(&results).await?;
info!("Performance optimization completed: {:?}", results);
Ok(results)
}
async fn collect_performance_data(&self) -> Result<HashMap<String, ModuleMetrics>> {
let monitor_list = {
let monitors = self.module_monitors.read().expect("lock poisoned");
monitors
.iter()
.map(|(name, monitor)| (name.clone(), monitor.clone()))
.collect::<Vec<_>>()
};
let mut data = HashMap::new();
for (module_name, monitor) in monitor_list {
let metrics = monitor.get_current_metrics().await?;
data.insert(module_name, metrics);
}
Ok(data)
}
async fn generate_optimization_recommendations(
&self,
performance_data: &HashMap<String, ModuleMetrics>,
anomalies: &[AnomalyEvent],
) -> Result<Vec<OptimizationRecommendation>> {
let mut recommendations = Vec::new();
for (module_name, metrics) in performance_data {
if metrics.cpu_usage > 80.0 {
recommendations.push(OptimizationRecommendation {
module_name: module_name.clone(),
optimization_type: OptimizationType::ResourceReallocation,
priority: Priority::High,
description: "High CPU usage detected - recommend resource reallocation"
.to_string(),
estimated_impact: PerformanceImpact {
latency_change_pct: -15.0,
throughput_change_pct: 20.0,
efficiency_change_pct: 10.0,
overall_score: 75.0,
},
implementation_steps: vec![
"Increase CPU allocation".to_string(),
"Enable parallel processing".to_string(),
"Optimize critical paths".to_string(),
],
});
}
if metrics.memory_usage > 8_000_000_000 {
recommendations.push(OptimizationRecommendation {
module_name: module_name.clone(),
optimization_type: OptimizationType::MemoryOptimization,
priority: Priority::Medium,
description: "High memory usage - recommend memory optimization".to_string(),
estimated_impact: PerformanceImpact {
latency_change_pct: -10.0,
throughput_change_pct: 15.0,
efficiency_change_pct: 25.0,
overall_score: 70.0,
},
implementation_steps: vec![
"Enable memory pooling".to_string(),
"Optimize data structures".to_string(),
"Implement garbage collection tuning".to_string(),
],
});
}
if metrics.cache_hit_rate < 80.0 {
recommendations.push(OptimizationRecommendation {
module_name: module_name.clone(),
optimization_type: OptimizationType::CacheOptimization,
priority: Priority::Medium,
description: "Low cache hit rate - recommend cache optimization".to_string(),
estimated_impact: PerformanceImpact {
latency_change_pct: -20.0,
throughput_change_pct: 25.0,
efficiency_change_pct: 15.0,
overall_score: 80.0,
},
implementation_steps: vec![
"Increase cache size".to_string(),
"Implement intelligent prefetching".to_string(),
"Optimize cache eviction policy".to_string(),
],
});
}
}
for anomaly in anomalies {
recommendations.extend(self.generate_anomaly_recommendations(anomaly).await?);
}
recommendations.sort_by(|a, b| {
b.priority.cmp(&a.priority).then_with(|| {
b.estimated_impact
.overall_score
.partial_cmp(&a.estimated_impact.overall_score)
.unwrap_or(std::cmp::Ordering::Equal)
})
});
Ok(recommendations)
}
async fn generate_anomaly_recommendations(
&self,
anomaly: &AnomalyEvent,
) -> Result<Vec<OptimizationRecommendation>> {
let mut recommendations = Vec::new();
match anomaly.anomaly_type {
AnomalyType::PerformanceDegradation => {
recommendations.push(OptimizationRecommendation {
module_name: anomaly.module_name.clone(),
optimization_type: OptimizationType::PerformanceTuning,
priority: Priority::High,
description: "Performance degradation detected - immediate optimization needed"
.to_string(),
estimated_impact: PerformanceImpact {
latency_change_pct: -30.0,
throughput_change_pct: 40.0,
efficiency_change_pct: 20.0,
overall_score: 85.0,
},
implementation_steps: anomaly.recommended_actions.clone(),
});
}
AnomalyType::MemoryLeak => {
recommendations.push(OptimizationRecommendation {
module_name: anomaly.module_name.clone(),
optimization_type: OptimizationType::MemoryOptimization,
priority: Priority::Critical,
description: "Memory leak detected - immediate action required".to_string(),
estimated_impact: PerformanceImpact {
latency_change_pct: -50.0,
throughput_change_pct: 60.0,
efficiency_change_pct: 80.0,
overall_score: 95.0,
},
implementation_steps: vec![
"Identify memory leak source".to_string(),
"Implement automatic memory cleanup".to_string(),
"Add memory monitoring alerts".to_string(),
],
});
}
_ => {
recommendations.push(OptimizationRecommendation {
module_name: anomaly.module_name.clone(),
optimization_type: OptimizationType::GeneralOptimization,
priority: match anomaly.severity {
SeverityLevel::Critical => Priority::Critical,
SeverityLevel::High => Priority::High,
SeverityLevel::Medium => Priority::Medium,
SeverityLevel::Low => Priority::Low,
},
description: format!("Anomaly detected: {:?}", anomaly.anomaly_type),
estimated_impact: PerformanceImpact {
latency_change_pct: -10.0,
throughput_change_pct: 15.0,
efficiency_change_pct: 10.0,
overall_score: 60.0,
},
implementation_steps: anomaly.recommended_actions.clone(),
});
}
}
Ok(recommendations)
}
async fn apply_optimization(
&self,
recommendation: OptimizationRecommendation,
) -> Result<PerformanceImpact> {
info!("Applying optimization: {}", recommendation.description);
match recommendation.optimization_type {
OptimizationType::ResourceReallocation => {
self.resource_allocator
.reallocate_resources(&recommendation.module_name, &recommendation)
.await?;
}
OptimizationType::MemoryOptimization => {
self.apply_memory_optimization(&recommendation.module_name, &recommendation)
.await?;
}
OptimizationType::CacheOptimization => {
self.apply_cache_optimization(&recommendation.module_name, &recommendation)
.await?;
}
OptimizationType::PerformanceTuning => {
self.apply_performance_tuning(&recommendation.module_name, &recommendation)
.await?;
}
OptimizationType::GeneralOptimization => {
self.apply_general_optimization(&recommendation.module_name, &recommendation)
.await?;
}
}
time::sleep(Duration::from_secs(5)).await;
let actual_impact = self
.measure_optimization_impact(&recommendation.module_name)
.await?;
self.predictive_engine
.update_models(&recommendation, &actual_impact)
.await?;
Ok(actual_impact)
}
async fn apply_memory_optimization(
&self,
module_name: &str,
recommendation: &OptimizationRecommendation,
) -> Result<()> {
use tracing::debug;
debug!("Applying memory optimization for module: {}", module_name);
for step in &recommendation.implementation_steps {
if step.contains("memory pooling") {
self.enable_memory_pooling(module_name).await?;
} else if step.contains("garbage collection") {
self.optimize_garbage_collection(module_name).await?;
} else if step.contains("data structures") {
self.optimize_data_structures(module_name).await?;
}
}
Ok(())
}
async fn apply_cache_optimization(
&self,
module_name: &str,
recommendation: &OptimizationRecommendation,
) -> Result<()> {
use tracing::debug;
debug!("Applying cache optimization for module: {}", module_name);
for step in &recommendation.implementation_steps {
if step.contains("cache size") {
self.increase_cache_size(module_name).await?;
} else if step.contains("prefetching") {
self.enable_intelligent_prefetching(module_name).await?;
} else if step.contains("eviction policy") {
self.optimize_cache_eviction(module_name).await?;
}
}
Ok(())
}
async fn apply_performance_tuning(
&self,
module_name: &str,
recommendation: &OptimizationRecommendation,
) -> Result<()> {
use tracing::debug;
debug!("Applying performance tuning for module: {}", module_name);
for step in &recommendation.implementation_steps {
if step.contains("parallel processing") {
self.enable_parallel_processing(module_name).await?;
} else if step.contains("critical paths") {
self.optimize_critical_paths(module_name).await?;
} else if step.contains("algorithms") {
self.optimize_algorithms(module_name).await?;
}
}
Ok(())
}
async fn apply_general_optimization(
&self,
module_name: &str,
_recommendation: &OptimizationRecommendation,
) -> Result<()> {
use tracing::debug;
debug!("Applying general optimization for module: {}", module_name);
self.tune_module_parameters(module_name).await?;
self.optimize_resource_usage(module_name).await?;
Ok(())
}
async fn measure_optimization_impact(&self, module_name: &str) -> Result<PerformanceImpact> {
let baseline = self.get_baseline_metrics(module_name).await?;
let current = self.get_current_module_metrics(module_name).await?;
let latency_change = calculate_percentage_change(
baseline.avg_response_time.as_millis() as f64,
current.avg_response_time.as_millis() as f64,
);
let throughput_change =
calculate_percentage_change(baseline.request_rate, current.request_rate);
let efficiency_change = calculate_percentage_change(baseline.cpu_usage, current.cpu_usage);
let overall_score =
(latency_change.abs() + throughput_change + efficiency_change.abs()) / 3.0;
Ok(PerformanceImpact {
latency_change_pct: latency_change,
throughput_change_pct: throughput_change,
efficiency_change_pct: efficiency_change,
overall_score,
})
}
async fn update_global_metrics(&self, results: &OptimizationResults) -> Result<()> {
let mut global_metrics = self.global_metrics.write().expect("lock poisoned");
global_metrics.update(results);
Ok(())
}
async fn enable_memory_pooling(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Enabling memory pooling");
Ok(())
}
async fn optimize_garbage_collection(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Optimizing garbage collection");
Ok(())
}
async fn optimize_data_structures(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Optimizing data structures");
Ok(())
}
async fn increase_cache_size(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Increasing cache size");
Ok(())
}
async fn enable_intelligent_prefetching(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Enabling intelligent prefetching");
Ok(())
}
async fn optimize_cache_eviction(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Optimizing cache eviction policy");
Ok(())
}
async fn enable_parallel_processing(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Enabling parallel processing");
Ok(())
}
async fn optimize_critical_paths(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Optimizing critical paths");
Ok(())
}
async fn optimize_algorithms(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Optimizing algorithms");
Ok(())
}
async fn tune_module_parameters(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Tuning module parameters");
Ok(())
}
async fn optimize_resource_usage(&self, _module_name: &str) -> Result<()> {
use tracing::debug;
debug!("Optimizing resource usage");
Ok(())
}
async fn get_baseline_metrics(&self, _module_name: &str) -> Result<ModuleMetrics> {
Ok(ModuleMetrics {
cpu_usage: 50.0,
memory_usage: 4_000_000_000,
gpu_memory_usage: Some(2_000_000_000),
network_io_bps: 1_000_000,
disk_io_bps: 500_000,
request_rate: 100.0,
avg_response_time: Duration::from_millis(100),
error_rate: 1.0,
cache_hit_rate: 85.0,
active_connections: 50,
queue_depth: 10,
})
}
async fn get_current_module_metrics(&self, module_name: &str) -> Result<ModuleMetrics> {
let monitor = {
let monitors = self.module_monitors.read().expect("lock poisoned");
monitors.get(module_name).cloned()
};
if let Some(monitor) = monitor {
monitor.get_current_metrics().await
} else {
Err(anyhow!("Module '{}' not found", module_name))
}
}
}