use crate::cross_module_performance_types::{
AllocationEvent, AllocationStrategy, AllocationType, AnomalyAlgorithm, AnomalyEvent,
AnomalyType, ModuleMetrics, OptimizationRecommendation, OptimizationType, PerformanceBaseline,
PerformanceImpact, PerformanceModel, PerformanceSnapshot, Priority, ResourceAllocation,
SeverityLevel, TrainingSample,
};
use anyhow::Result;
use chrono::Utc;
use std::collections::{HashMap, VecDeque};
use std::sync::atomic::{AtomicU64, AtomicUsize};
use std::sync::{Arc, RwLock};
use std::time::Duration;
use tracing::debug;
#[derive(Debug, Clone)]
pub struct ResourceTracker {
cpu_history: Arc<RwLock<VecDeque<f64>>>,
memory_history: Arc<RwLock<VecDeque<u64>>>,
last_update: Arc<RwLock<chrono::DateTime<chrono::Utc>>>,
}
impl ResourceTracker {
pub fn new() -> Self {
Self {
cpu_history: Arc::new(RwLock::new(VecDeque::new())),
memory_history: Arc::new(RwLock::new(VecDeque::new())),
last_update: Arc::new(RwLock::new(Utc::now())),
}
}
}
impl Default for ResourceTracker {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug, Clone)]
pub struct PredictionModel {
pub parameters: HashMap<String, f64>,
pub last_trained: chrono::DateTime<chrono::Utc>,
}
impl PredictionModel {
pub fn new() -> Self {
Self {
parameters: HashMap::new(),
last_trained: Utc::now(),
}
}
}
impl Default for PredictionModel {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug, Clone)]
pub struct ModulePerformanceMonitor {
pub module_name: String,
pub metrics: Arc<RwLock<ModuleMetrics>>,
pub resource_tracker: ResourceTracker,
pub history: Arc<RwLock<VecDeque<PerformanceSnapshot>>>,
pub(crate) prediction_model: PredictionModel,
}
impl ModulePerformanceMonitor {
pub fn new(module_name: String) -> Self {
Self {
module_name,
metrics: Arc::new(RwLock::new(ModuleMetrics {
cpu_usage: 0.0,
memory_usage: 0,
gpu_memory_usage: None,
network_io_bps: 0,
disk_io_bps: 0,
request_rate: 0.0,
avg_response_time: Duration::from_millis(0),
error_rate: 0.0,
cache_hit_rate: 0.0,
active_connections: 0,
queue_depth: 0,
})),
resource_tracker: ResourceTracker::new(),
history: Arc::new(RwLock::new(VecDeque::new())),
prediction_model: PredictionModel::new(),
}
}
pub async fn update_metrics(&self, new_metrics: ModuleMetrics) -> Result<()> {
{
let mut metrics = self.metrics.write().expect("lock poisoned");
*metrics = new_metrics.clone();
}
let snapshot = PerformanceSnapshot {
metrics: new_metrics,
timestamp: Utc::now(),
};
{
let mut history = self.history.write().expect("lock poisoned");
history.push_back(snapshot);
if history.len() > 1000 {
history.pop_front();
}
}
Ok(())
}
pub async fn get_current_metrics(&self) -> Result<ModuleMetrics> {
let metrics = self.metrics.read().expect("lock poisoned");
Ok(metrics.clone())
}
}
#[derive(Debug)]
pub struct ResourceAllocator {
pub available_cores: AtomicUsize,
pub available_memory: AtomicU64,
pub available_gpu_memory: AtomicU64,
pub allocation_history: Arc<RwLock<VecDeque<AllocationEvent>>>,
pub current_allocations: Arc<RwLock<HashMap<String, ResourceAllocation>>>,
pub optimization_strategies: Vec<AllocationStrategy>,
}
impl ResourceAllocator {
pub fn new() -> Self {
Self {
available_cores: AtomicUsize::new(8),
available_memory: AtomicU64::new(16_000_000_000),
available_gpu_memory: AtomicU64::new(8_000_000_000),
allocation_history: Arc::new(RwLock::new(VecDeque::new())),
current_allocations: Arc::new(RwLock::new(HashMap::new())),
optimization_strategies: Vec::new(),
}
}
pub async fn reallocate_resources(
&self,
module_name: &str,
recommendation: &OptimizationRecommendation,
) -> Result<()> {
debug!("Reallocating resources for module: {}", module_name);
let current_allocation = self.get_current_allocation(module_name).await?;
let new_allocation = self
.calculate_new_allocation(¤t_allocation, recommendation)
.await?;
self.apply_allocation(module_name, new_allocation).await?;
Ok(())
}
async fn get_current_allocation(&self, module_name: &str) -> Result<ResourceAllocation> {
let allocations = self.current_allocations.read().expect("lock poisoned");
if let Some(allocation) = allocations.get(module_name) {
Ok(allocation.clone())
} else {
Ok(ResourceAllocation {
cpu_cores: 2,
memory_bytes: 2_000_000_000,
gpu_memory_bytes: Some(1_000_000_000),
priority: 50,
allocated_at: Utc::now(),
expected_duration: None,
})
}
}
async fn calculate_new_allocation(
&self,
current: &ResourceAllocation,
recommendation: &OptimizationRecommendation,
) -> Result<ResourceAllocation> {
let mut new_allocation = current.clone();
match recommendation.optimization_type {
OptimizationType::ResourceReallocation => match recommendation.priority {
Priority::Critical => {
new_allocation.cpu_cores = (current.cpu_cores * 2).min(8);
new_allocation.memory_bytes = (current.memory_bytes * 2).min(8_000_000_000);
}
Priority::High => {
new_allocation.cpu_cores = (current.cpu_cores + 2).min(6);
new_allocation.memory_bytes =
(current.memory_bytes + 1_000_000_000).min(6_000_000_000);
}
_ => {
new_allocation.cpu_cores = (current.cpu_cores + 1).min(4);
new_allocation.memory_bytes =
(current.memory_bytes + 500_000_000).min(4_000_000_000);
}
},
_ => {
new_allocation.priority = (current.priority + 10).min(100);
}
}
new_allocation.allocated_at = Utc::now();
Ok(new_allocation)
}
async fn apply_allocation(
&self,
module_name: &str,
allocation: ResourceAllocation,
) -> Result<()> {
{
let mut allocations = self.current_allocations.write().expect("lock poisoned");
allocations.insert(module_name.to_string(), allocation.clone());
}
let event = AllocationEvent {
module_name: module_name.to_string(),
event_type: AllocationType::Rebalance,
allocation,
performance_impact: None,
timestamp: Utc::now(),
};
{
let mut history = self.allocation_history.write().expect("lock poisoned");
history.push_back(event);
if history.len() > 1000 {
history.pop_front();
}
}
Ok(())
}
}
impl Default for ResourceAllocator {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug)]
pub struct LearningEngine {
learning_rate: f64,
training_samples: Arc<RwLock<VecDeque<TrainingSample>>>,
update_frequency: Duration,
baselines: Arc<RwLock<HashMap<String, PerformanceBaseline>>>,
}
impl LearningEngine {
pub fn new() -> Self {
Self {
learning_rate: 0.01,
training_samples: Arc::new(RwLock::new(VecDeque::new())),
update_frequency: Duration::from_secs(3600),
baselines: Arc::new(RwLock::new(HashMap::new())),
}
}
pub async fn update_model(
&self,
recommendation: &OptimizationRecommendation,
actual_impact: &PerformanceImpact,
) -> Result<()> {
let sample = TrainingSample {
features: vec![
recommendation.estimated_impact.overall_score,
recommendation.priority.clone() as u8 as f64,
],
target: actual_impact.overall_score,
context: HashMap::new(),
weight: 1.0,
timestamp: Utc::now(),
};
{
let mut samples = self.training_samples.write().expect("lock poisoned");
samples.push_back(sample);
if samples.len() > 10000 {
samples.pop_front();
}
}
Ok(())
}
}
impl Default for LearningEngine {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug)]
pub struct AnomalyDetector {
algorithms: Vec<AnomalyAlgorithm>,
thresholds: HashMap<String, f64>,
anomaly_history: Arc<RwLock<VecDeque<AnomalyEvent>>>,
false_positive_rate: f64,
}
impl AnomalyDetector {
pub fn new() -> Self {
Self {
algorithms: vec![
AnomalyAlgorithm::StatisticalOutlier { z_threshold: 3.0 },
AnomalyAlgorithm::IsolationForest { contamination: 0.1 },
],
thresholds: HashMap::new(),
anomaly_history: Arc::new(RwLock::new(VecDeque::new())),
false_positive_rate: 0.05,
}
}
pub async fn detect(
&self,
performance_data: &HashMap<String, ModuleMetrics>,
) -> Result<Vec<AnomalyEvent>> {
let mut anomalies = Vec::new();
for (module_name, metrics) in performance_data {
if metrics.cpu_usage > 90.0
|| metrics.error_rate > 5.0
|| metrics.avg_response_time > Duration::from_millis(1000)
{
let anomaly = AnomalyEvent {
module_name: module_name.clone(),
anomaly_type: AnomalyType::PerformanceDegradation,
severity: if metrics.cpu_usage > 95.0 || metrics.error_rate > 10.0 {
SeverityLevel::Critical
} else {
SeverityLevel::High
},
score: calculate_anomaly_score(metrics),
affected_metrics: vec![
"cpu_usage".to_string(),
"error_rate".to_string(),
"response_time".to_string(),
],
recommended_actions: vec![
"Increase resource allocation".to_string(),
"Investigate error sources".to_string(),
"Optimize critical paths".to_string(),
],
detected_at: Utc::now(),
resolved_at: None,
};
anomalies.push(anomaly);
}
if metrics.memory_usage > 12_000_000_000 {
let anomaly = AnomalyEvent {
module_name: module_name.clone(),
anomaly_type: AnomalyType::MemoryLeak,
severity: SeverityLevel::High,
score: (metrics.memory_usage as f64 / 16_000_000_000.0) * 100.0,
affected_metrics: vec!["memory_usage".to_string()],
recommended_actions: vec![
"Investigate memory usage patterns".to_string(),
"Enable memory profiling".to_string(),
"Implement memory cleanup".to_string(),
],
detected_at: Utc::now(),
resolved_at: None,
};
anomalies.push(anomaly);
}
}
{
let mut history = self.anomaly_history.write().expect("lock poisoned");
for anomaly in &anomalies {
history.push_back(anomaly.clone());
}
while history.len() > 1000 {
history.pop_front();
}
}
Ok(anomalies)
}
}
impl Default for AnomalyDetector {
fn default() -> Self {
Self::new()
}
}
#[derive(Debug)]
pub struct PredictivePerformanceEngine {
pub models: Arc<RwLock<HashMap<String, PerformanceModel>>>,
pub prediction_cache:
Arc<RwLock<HashMap<String, crate::cross_module_performance_types::CachedPrediction>>>,
pub learning_engine: LearningEngine,
pub anomaly_detector: AnomalyDetector,
}
impl PredictivePerformanceEngine {
pub fn new() -> Self {
Self {
models: Arc::new(RwLock::new(HashMap::new())),
prediction_cache: Arc::new(RwLock::new(HashMap::new())),
learning_engine: LearningEngine::new(),
anomaly_detector: AnomalyDetector::new(),
}
}
pub async fn detect_anomalies(
&self,
performance_data: &HashMap<String, ModuleMetrics>,
) -> Result<Vec<AnomalyEvent>> {
self.anomaly_detector.detect(performance_data).await
}
pub async fn update_models(
&self,
recommendation: &OptimizationRecommendation,
actual_impact: &PerformanceImpact,
) -> Result<()> {
self.learning_engine
.update_model(recommendation, actual_impact)
.await
}
}
impl Default for PredictivePerformanceEngine {
fn default() -> Self {
Self::new()
}
}
pub fn calculate_anomaly_score(metrics: &ModuleMetrics) -> f64 {
let cpu_score = if metrics.cpu_usage > 80.0 {
metrics.cpu_usage
} else {
0.0
};
let error_score = metrics.error_rate * 10.0;
let latency_score = if metrics.avg_response_time > Duration::from_millis(500) {
metrics.avg_response_time.as_millis() as f64 / 10.0
} else {
0.0
};
(cpu_score + error_score + latency_score) / 3.0
}
pub fn calculate_percentage_change(old_value: f64, new_value: f64) -> f64 {
if old_value == 0.0 {
return 0.0;
}
((new_value - old_value) / old_value) * 100.0
}