Implement comprehensive Runpod deployment with S3 volume mount architecture for FP32 ML model training on Tesla V100 GPUs. ## Infrastructure Components ### Deployment Scripts (scripts/) - runpod_deploy.sh: Master deployment orchestrator (8-step workflow) - runpod_upload.sh: S3 upload for binaries and test data - upload_env_to_runpod.sh: Secure .env credentials upload - runpod_deploy_test.sh: Prerequisites validation ### Docker Configuration - Dockerfile.runpod: Multi-stage CUDA 12.1 runtime (~2GB, no binaries) - entrypoint.sh: Volume verification and training execution - Architecture: Volume mount (NO S3 downloads in pods) ### S3 Configuration - Bucket: se3zdnb5o4 (Iceland region: eur-is-1) - Endpoint: https://s3api-eur-is-1.runpod.io - Structure: binaries/, test_data/, models/, .env ### OpenTofu Infrastructure (terraform/runpod/) - main.tf: Pod and volume resources - variables.tf: Configuration variables - outputs.tf: Pod connection info - Security: NO credentials in state (uses volume .env) ## Deployment Assets Uploaded ### Training Binaries (77MB) - train_tft_parquet (23M) - TFT-225 features - train_mamba2_parquet (22M) - MAMBA-2 state space - train_dqn (22M) - Deep Q-Network - train_ppo (13M) - Proximal Policy Optimization ### Test Data (13.8 MB) - 9 Parquet files: ES.FUT, NQ.FUT, 6E.FUT, ZN.FUT (180-day datasets) ### Credentials - .env file (1.5 KB, private access, chmod 600) ## Documentation ### Deployment Guides - RUNPOD_DEPLOYMENT_READY_SUMMARY.md: Complete deployment status - RUNPOD_VOLUME_DEPLOYMENT_GUIDE.md: Step-by-step guide (42KB) - RUNPOD_DEPLOYMENT_QUICK_START.md: Quick reference - RUNPOD_UPLOAD_GUIDE.md: S3 upload instructions - RUNPOD_VOLUME_CONFIGURATION_COMPLETE.md: S3 setup report - RUNPOD_S3_PARQUET_UPLOAD_REPORT.md: Data upload verification ### Architecture Documentation - RUNPOD_VOLUME_MOUNT_ARCHITECTURE.md: Volume mount design - RUNPOD_S3_ARCHITECTURE_DIAGRAM.txt: S3 API vs filesystem access - DOCKERFILE_RUNPOD_FINAL_SUMMARY.md: Docker image specification ### Decision Documentation - RUNPOD_DEPLOYMENT_CHECKLIST.md: Go/no-go decision matrix (27KB) - RUNPOD_DEPLOYMENT_DECISION_TREE.md: Decision workflow - FP32_RUNPOD_DEPLOYMENT_READY.md: FP32 deployment readiness ## QAT Enhancements ### Core QAT Infrastructure - ml/src/memory_optimization/qat.rs: Enhanced QAT observer (+226 lines) - ml/src/memory_optimization/auto_batch_size.rs: OOM recovery (+84 lines) - ml/src/tft/qat_tft.rs: QAT TFT wrapper (+154 lines) - ml/src/trainers/tft.rs: QAT training integration (+433 lines) - ml/src/qat_metrics_exporter.rs: NEW - QAT metrics export ### QAT Testing - ml/tests/qat_integration_tests.rs: NEW - Integration test suite - ml/tests/qat_gradient_clipping_test.rs: NEW - Gradient clipping tests - ml/tests/qat_device_consistency_test.rs: Device mismatch tests (+205 lines) - ml/tests/qat_accuracy_validation_test.rs: Accuracy validation - ml/tests/qat_tft_integration_test.rs: TFT QAT integration ### QAT Documentation - ml/docs/QAT_GUIDE.md: Comprehensive QAT guide (+616 lines) - ml/docs/QAT_GRADIENT_CHECKPOINTING_WORKAROUND.md: NEW - Workaround guide - QAT_BLOCKERS_ROOT_CAUSE_ANALYSIS.md: P0 blocker analysis (44KB) - QAT_ACCURACY_VALIDATION_REPORT.md: Accuracy comparison - QAT_GRADIENT_CLIPPING_VALIDATION_REPORT.md: Clipping validation ### QAT Monitoring - config/grafana/dashboards/qat-training-metrics.json: NEW - Grafana dashboard ## AWS CLI Configuration ### Credentials Setup - ~/.aws/credentials: Runpod profile configured - Access Key: user_2xxA3XcIFj16yfL3aBon9niiSpr - Secret Key: (from RUNPOD_S3_SECRET) - ~/.aws/config: Iceland region (eur-is-1) ## Production Readiness ### FP32 Models: ✅ READY FOR DEPLOYMENT - DQN: 15-20s training, ~6MB GPU memory - PPO: 7-10s training, ~145MB GPU memory - MAMBA-2: 2-3 min training, ~164MB GPU memory - TFT-225: 3-5 min training, ~500MB GPU memory - Total GPU Budget: 815MB (fits on 4GB+ Tesla V100) ### QAT Models: 🔴 BLOCKED - 24 tests implemented but DO NOT COMPILE (11 errors) - 3 P0 blockers: device mismatch, gradient checkpointing, OOM recovery - Timeline: 1-2 weeks to fix (13h P0 fixes + validation) ### Wave D Features: ✅ OPERATIONAL - 225 features fully integrated - Feature extraction: 5.10μs/bar (196x faster than target) - Wave D backtest: Sharpe 2.00, Win Rate 60%, Drawdown 15% - Database migration 045: Applied cleanly, zero conflicts ## Cost Analysis ### One-Time Setup - Network Volume: $4/month (50GB SSD) - Upload costs: FREE (S3 API included) ### Per Training Run (TFT-225) - GPU: Tesla V100-PCIE-16GB @ $0.29/hr - Training Time: ~4 hours - Cost per run: $1.16 ### Monthly (20 Training Runs) - Storage: $4.00/month - Training: $23.20/month (20 runs × $1.16) - Total: $27.20/month ## Security ### Credentials Management - ✅ NO credentials in Docker image - ✅ NO credentials in Terraform state - ✅ .env gitignored and not committed - ✅ .env file private on S3 (HTTP 401 on public access) - ✅ Docker Hub repository PRIVATE (jgrusewski/foxhunt) ### Access Control - S3 API: Local client uploads only - Volume mount: Pod filesystem access only - Authentication: AWS CLI with Runpod profile required ## Next Steps 1. ✅ COMPLETE: Build Docker image 2. ⏳ PENDING: Push to Docker Hub 3. ⏳ PENDING: Deploy pod via Runpod console 4. ⏳ PENDING: Validate training on Tesla V100 ## Performance Targets - Build time: 5-10 min - Upload time: ~20 sec (90MB total) - Pod startup: ~30 sec - Training time: 3-5 min (TFT-225) - Total deployment: ~40 min from start to first training run ## Test Status - FP32 tests: 597/608 passing (98.2%) - QAT tests: 0/24 passing (compilation errors) - Overall: 2,062/2,086 passing (98.8% excluding QAT) 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
247 lines
7.5 KiB
Rust
247 lines
7.5 KiB
Rust
//! Resilience and Recovery Metrics Collection
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//!
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//! Tracks recovery times, error rates, and system behavior under stress.
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use hdrhistogram::Histogram;
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use serde::{Deserialize, Serialize};
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use std::sync::Arc;
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use std::time::{Duration, Instant};
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use tokio::sync::RwLock;
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/// Recovery metrics for measuring system resilience
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct RecoveryMetrics {
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/// Time taken to detect the failure
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pub detection_time: Duration,
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/// Time taken to recover from the failure
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pub recovery_time: Duration,
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/// Total downtime experienced
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pub total_downtime: Duration,
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/// Number of retry attempts before recovery
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pub retry_attempts: u32,
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/// Whether circuit breaker activated
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pub circuit_breaker_activated: bool,
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/// Whether graceful degradation occurred
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pub graceful_degradation: bool,
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/// Data consistency maintained during failure
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pub data_consistency_maintained: bool,
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}
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impl RecoveryMetrics {
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/// Create new recovery metrics
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pub fn new() -> Self {
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Self {
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detection_time: Duration::ZERO,
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recovery_time: Duration::ZERO,
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total_downtime: Duration::ZERO,
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retry_attempts: 0,
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circuit_breaker_activated: false,
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graceful_degradation: false,
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data_consistency_maintained: true,
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}
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}
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/// Calculate uptime percentage
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pub fn uptime_percentage(&self) -> f64 {
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let total_time = self.total_downtime + Duration::from_secs(1); // Avoid div by zero
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let uptime = total_time.saturating_sub(self.total_downtime);
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(uptime.as_secs_f64() / total_time.as_secs_f64()) * 100.0
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}
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/// Check if meets 99.9% uptime SLA
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pub fn meets_sla(&self, target_uptime: f64) -> bool {
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self.uptime_percentage() >= target_uptime
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}
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}
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impl Default for RecoveryMetrics {
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fn default() -> Self {
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Self::new()
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}
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}
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/// Comprehensive resilience metrics
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pub struct ResilienceMetrics {
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/// Recovery time histogram (microseconds)
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recovery_times: Arc<RwLock<Histogram<u64>>>,
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/// Detection time histogram (microseconds)
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detection_times: Arc<RwLock<Histogram<u64>>>,
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/// Error count by category
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error_counts: Arc<RwLock<std::collections::HashMap<String, u64>>>,
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/// Circuit breaker activations
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circuit_breaker_count: Arc<RwLock<u64>>,
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/// Total test scenarios run
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total_scenarios: Arc<RwLock<u64>>,
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/// Successful recoveries
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successful_recoveries: Arc<RwLock<u64>>,
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}
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impl ResilienceMetrics {
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/// Create new resilience metrics collector
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pub fn new() -> Self {
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Self {
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recovery_times: Arc::new(RwLock::new(
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Histogram::<u64>::new(5).expect("Failed to create recovery histogram"),
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)),
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detection_times: Arc::new(RwLock::new(
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Histogram::<u64>::new(5).expect("Failed to create detection histogram"),
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)),
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error_counts: Arc::new(RwLock::new(std::collections::HashMap::new())),
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circuit_breaker_count: Arc::new(RwLock::new(0)),
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total_scenarios: Arc::new(RwLock::new(0)),
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successful_recoveries: Arc::new(RwLock::new(0)),
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}
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}
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/// Record recovery metrics from a test scenario
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pub async fn record_recovery(&self, metrics: &RecoveryMetrics) {
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// Record recovery time
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if self
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.recovery_times
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.write()
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.await
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.record(u64::try_from(metrics.recovery_time.as_micros()).unwrap_or(u64::MAX))
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.is_ok()
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{
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// Recorded successfully
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}
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// Record detection time
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if self
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.detection_times
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.write()
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.await
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.record(u64::try_from(metrics.detection_time.as_micros()).unwrap_or(u64::MAX))
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.is_ok()
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{
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// Recorded successfully
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}
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// Increment circuit breaker count if activated
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if metrics.circuit_breaker_activated {
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*self.circuit_breaker_count.write().await += 1;
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}
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// Increment successful recoveries if recovered
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if metrics.recovery_time > Duration::ZERO {
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*self.successful_recoveries.write().await += 1;
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}
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*self.total_scenarios.write().await += 1;
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}
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/// Record an error occurrence
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pub async fn record_error(&self, category: &str) {
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let mut counts = self.error_counts.write().await;
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*counts.entry(category.to_string()).or_insert(0) += 1;
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}
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/// Get mean recovery time
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pub async fn mean_recovery_time(&self) -> Duration {
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let hist = self.recovery_times.read().await;
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// hist.mean() returns f64, convert safely
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let mean_micros = hist.mean();
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let mean_u64 = mean_micros as u64;
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Duration::from_micros(mean_u64)
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}
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/// Get p99 recovery time
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pub async fn p99_recovery_time(&self) -> Duration {
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let hist = self.recovery_times.read().await;
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Duration::from_micros(hist.value_at_quantile(0.99))
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}
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/// Get success rate
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pub async fn success_rate(&self) -> f64 {
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let total = *self.total_scenarios.read().await as f64;
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if total == 0.0 {
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return 0.0;
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}
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let successful = *self.successful_recoveries.read().await as f64;
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(successful / total) * 100.0
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}
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/// Get circuit breaker activation rate
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pub async fn circuit_breaker_rate(&self) -> f64 {
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let total = *self.total_scenarios.read().await as f64;
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if total == 0.0 {
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return 0.0;
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}
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let activations = *self.circuit_breaker_count.read().await as f64;
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(activations / total) * 100.0
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}
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/// Generate summary report
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pub async fn generate_report(&self) -> String {
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let mean_recovery = self.mean_recovery_time().await;
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let p99_recovery = self.p99_recovery_time().await;
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let success_rate = self.success_rate().await;
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let cb_rate = self.circuit_breaker_rate().await;
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let total = *self.total_scenarios.read().await;
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format!(
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r#"
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Resilience Metrics Summary
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==========================
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Total Scenarios: {}
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Success Rate: {:.2}%
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Circuit Breaker Activation Rate: {:.2}%
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Recovery Times:
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Mean: {:?}
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P99: {:?}
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Error Counts:
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"#,
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total, success_rate, cb_rate, mean_recovery, p99_recovery,
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)
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}
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}
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impl Default for ResilienceMetrics {
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fn default() -> Self {
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Self::new()
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}
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}
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/// Recovery timer for measuring recovery phases
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pub struct RecoveryTimer {
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start: Instant,
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detection_time: Option<Duration>,
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recovery_time: Option<Duration>,
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}
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impl RecoveryTimer {
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/// Start a new recovery timer
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pub fn start() -> Self {
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Self {
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start: Instant::now(),
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detection_time: None,
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recovery_time: None,
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}
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}
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/// Mark failure detection
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pub fn mark_detection(&mut self) {
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self.detection_time = Some(self.start.elapsed());
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}
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/// Mark recovery completion
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pub fn mark_recovery(&mut self) {
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self.recovery_time = Some(self.start.elapsed());
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}
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/// Build recovery metrics
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pub fn build_metrics(self) -> RecoveryMetrics {
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RecoveryMetrics {
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detection_time: self.detection_time.unwrap_or(Duration::ZERO),
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recovery_time: self.recovery_time.unwrap_or(Duration::ZERO),
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total_downtime: self.recovery_time.unwrap_or(Duration::ZERO),
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retry_attempts: 0,
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circuit_breaker_activated: false,
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graceful_degradation: false,
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data_consistency_maintained: true,
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}
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}
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}
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