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1 //! Predictive Replication Module
2 //!
3 //! Machine learning-based node failure prediction and proactive data migration
4
5 use serde::{Deserialize, Serialize};
6 use std::collections::HashMap;
7 use tokio::time::{Duration, Instant};
8
9 #[derive(Debug, Clone, Serialize, Deserialize)]
10 pub struct NodeMetrics {
11 pub node_id: String,
12 pub uptime_percentage: f32,
13 pub response_latency: Duration,
14 pub storage_usage: f32,
15 pub bandwidth_utilization: f32,
16 pub error_rate: f32,
17 pub last_failure: Option<Instant>,
18 pub hardware_health: HardwareHealth,
19 pub geographic_risk: GeographicRisk,
20 pub network_stability: NetworkStability,
21 }
22
23 #[derive(Debug, Clone, Serialize, Deserialize)]
24 pub struct HardwareHealth {
25 pub cpu_temperature: f32,
26 pub disk_health_score: f32,
27 pub memory_errors: u32,
28 pub power_stability: f32,
29 }
30
31 #[derive(Debug, Clone, Serialize, Deserialize)]
32 pub struct GeographicRisk {
33 pub natural_disaster_risk: f32,
34 pub political_stability: f32,
35 pub infrastructure_quality: f32,
36 pub connectivity_redundancy: f32,
37 }
38
39 #[derive(Debug, Clone, Serialize, Deserialize)]
40 pub struct NetworkStability {
41 pub connection_drops: u32,
42 pub peer_count: u32,
43 pub routing_efficiency: f32,
44 pub congestion_level: f32,
45 }
46
47 #[derive(Debug, Clone, Serialize, Deserialize)]
48 pub struct FailurePrediction {
49 pub node_id: String,
50 pub failure_probability: f32,
51 pub predicted_failure_time: Option<Instant>,
52 pub confidence_score: f32,
53 pub risk_factors: Vec<RiskFactor>,
54 pub recommended_actions: Vec<RecommendedAction>,
55 }
56
57 #[derive(Debug, Clone, Serialize, Deserialize)]
58 pub enum RiskFactor {
59 HighLatency,
60 FrequentDisconnections,
61 StorageExhaustion,
62 HardwareDeterioration,
63 NetworkCongestion,
64 GeographicInstability,
65 PerformanceDegradation,
66 }
67
68 #[derive(Debug, Clone, Serialize, Deserialize)]
69 pub enum RecommendedAction {
70 MigrateChunksImmediately,
71 IncreaseRedundancy,
72 ScheduleMaintenance,
73 ReduceLoad,
74 MonitorClosely,
75 PrepareFailover,
76 }
77
78 pub struct MLPredictor {
79 node_history: HashMap<String, Vec<NodeMetrics>>,
80 prediction_models: HashMap<String, PredictionModel>,
81 feature_weights: FeatureWeights,
82 training_data: Vec<TrainingExample>,
83 }
84
85 #[derive(Debug, Clone)]
86 struct PredictionModel {
87 weights: Vec<f32>,
88 bias: f32,
89 accuracy: f32,
90 last_updated: Instant,
91 }
92
93 #[derive(Debug, Clone)]
94 struct FeatureWeights {
95 uptime: f32,
96 latency: f32,
97 storage: f32,
98 bandwidth: f32,
99 error_rate: f32,
100 hardware_health: f32,
101 geographic_risk: f32,
102 network_stability: f32,
103 }
104
105 #[derive(Debug, Clone)]
106 struct TrainingExample {
107 features: Vec<f32>,
108 outcome: bool, // true if node failed
109 timestamp: Instant,
110 }
111
112 impl MLPredictor {
113 pub fn new() -> Self {
114 Self {
115 node_history: HashMap::new(),
116 prediction_models: HashMap::new(),
117 feature_weights: FeatureWeights::default(),
118 training_data: Vec::new(),
119 }
120 }
121
122 pub async fn update_node_metrics(&mut self, metrics: NodeMetrics) {
123 let node_id = metrics.node_id.clone();
124 let history = self.node_history.entry(node_id.clone()).or_insert_with(Vec::new);
125
126 history.push(metrics.clone());
127
128 // Keep only last 1000 data points per node
129 if history.len() > 1000 {
130 history.drain(0..history.len() - 1000);
131 }
132
133 // Update training data based on actual failures
134 if let Some(last_metrics) = history.get(history.len().saturating_sub(2)) {
135 if self.detect_failure_transition(last_metrics, &metrics) {
136 let features = self.extract_features(last_metrics);
137 self.training_data.push(TrainingExample {
138 features,
139 outcome: true,
140 timestamp: Instant::now(),
141 });
142 }
143 }
144
145 // Retrain model periodically
146 if history.len() % 100 == 0 {
147 self.retrain_model(&node_id).await;
148 }
149 }
150
151 pub async fn predict_node_failure(&self, node_id: &str) -> Option<FailurePrediction> {
152 let history = self.node_history.get(node_id)?;
153 let latest_metrics = history.last()?;
154 let model = self.prediction_models.get(node_id)?;
155
156 let features = self.extract_features(latest_metrics);
157 let failure_probability = self.calculate_failure_probability(&features, model);
158
159 if failure_probability < 0.1 {
160 return None; // Low risk, no prediction needed
161 }
162
163 let confidence_score = self.calculate_confidence(&features, model);
164 let risk_factors = self.identify_risk_factors(latest_metrics);
165 let recommended_actions = self.generate_recommendations(failure_probability, &risk_factors);
166
167 let predicted_failure_time = if failure_probability > 0.7 {
168 Some(Instant::now() + Duration::from_secs(3600)) // 1 hour
169 } else if failure_probability > 0.5 {
170 Some(Instant::now() + Duration::from_secs(7200)) // 2 hours
171 } else {
172 Some(Instant::now() + Duration::from_secs(14400)) // 4 hours
173 };
174
175 Some(FailurePrediction {
176 node_id: node_id.to_string(),
177 failure_probability,
178 predicted_failure_time,
179 confidence_score,
180 risk_factors,
181 recommended_actions,
182 })
183 }
184
185 pub async fn get_high_risk_nodes(&self) -> Vec<FailurePrediction> {
186 let mut high_risk = Vec::new();
187
188 for node_id in self.node_history.keys() {
189 if let Some(prediction) = self.predict_node_failure(node_id).await {
190 if prediction.failure_probability > 0.3 {
191 high_risk.push(prediction);
192 }
193 }
194 }
195
196 // Sort by failure probability (highest first)
197 high_risk.sort_by(|a, b| b.failure_probability.partial_cmp(&a.failure_probability).unwrap());
198 high_risk
199 }
200
201 fn extract_features(&self, metrics: &NodeMetrics) -> Vec<f32> {
202 vec![
203 metrics.uptime_percentage,
204 metrics.response_latency.as_millis() as f32,
205 metrics.storage_usage,
206 metrics.bandwidth_utilization,
207 metrics.error_rate,
208 self.calculate_hardware_score(&metrics.hardware_health),
209 self.calculate_geographic_risk_score(&metrics.geographic_risk),
210 self.calculate_network_stability_score(&metrics.network_stability),
211 ]
212 }
213
214 fn calculate_failure_probability(&self, features: &[f32], model: &PredictionModel) -> f32 {
215 let mut score = model.bias;
216 for (feature, weight) in features.iter().zip(model.weights.iter()) {
217 score += feature * weight;
218 }
219
220 // Sigmoid activation
221 1.0 / (1.0 + (-score).exp())
222 }
223
224 fn calculate_confidence(&self, features: &[f32], model: &PredictionModel) -> f32 {
225 // Confidence based on feature consistency and model accuracy
226 let feature_variance = self.calculate_feature_variance(features);
227 let base_confidence = model.accuracy;
228
229 // Higher variance reduces confidence
230 base_confidence * (1.0 - feature_variance.min(0.5))
231 }
232
233 fn calculate_feature_variance(&self, features: &[f32]) -> f32 {
234 if features.is_empty() {
235 return 0.0;
236 }
237
238 let mean: f32 = features.iter().sum::<f32>() / features.len() as f32;
239 let variance: f32 = features.iter()
240 .map(|x| (x - mean).powi(2))
241 .sum::<f32>() / features.len() as f32;
242
243 variance.sqrt()
244 }
245
246 fn identify_risk_factors(&self, metrics: &NodeMetrics) -> Vec<RiskFactor> {
247 let mut factors = Vec::new();
248
249 if metrics.response_latency > Duration::from_millis(1000) {
250 factors.push(RiskFactor::HighLatency);
251 }
252
253 if metrics.error_rate > 0.05 {
254 factors.push(RiskFactor::FrequentDisconnections);
255 }
256
257 if metrics.storage_usage > 0.9 {
258 factors.push(RiskFactor::StorageExhaustion);
259 }
260
261 if metrics.hardware_health.disk_health_score < 0.7
262 || metrics.hardware_health.cpu_temperature > 80.0 {
263 factors.push(RiskFactor::HardwareDeterioration);
264 }
265
266 if metrics.network_stability.congestion_level > 0.8 {
267 factors.push(RiskFactor::NetworkCongestion);
268 }
269
270 if metrics.geographic_risk.natural_disaster_risk > 0.6
271 || metrics.geographic_risk.political_stability < 0.4 {
272 factors.push(RiskFactor::GeographicInstability);
273 }
274
275 if metrics.uptime_percentage < 0.95 && metrics.bandwidth_utilization < 0.3 {
276 factors.push(RiskFactor::PerformanceDegradation);
277 }
278
279 factors
280 }
281
282 fn generate_recommendations(
283 &self,
284 failure_probability: f32,
285 risk_factors: &[RiskFactor]
286 ) -> Vec<RecommendedAction> {
287 let mut actions = Vec::new();
288
289 if failure_probability > 0.8 {
290 actions.push(RecommendedAction::MigrateChunksImmediately);
291 actions.push(RecommendedAction::PrepareFailover);
292 } else if failure_probability > 0.6 {
293 actions.push(RecommendedAction::IncreaseRedundancy);
294 actions.push(RecommendedAction::MonitorClosely);
295 } else if failure_probability > 0.4 {
296 actions.push(RecommendedAction::ScheduleMaintenance);
297 }
298
299 for factor in risk_factors {
300 match factor {
301 RiskFactor::StorageExhaustion => {
302 actions.push(RecommendedAction::ReduceLoad);
303 }
304 RiskFactor::HardwareDeterioration => {
305 actions.push(RecommendedAction::ScheduleMaintenance);
306 }
307 RiskFactor::NetworkCongestion => {
308 actions.push(RecommendedAction::ReduceLoad);
309 }
310 _ => {}
311 }
312 }
313
314 actions.sort();
315 actions.dedup();
316 actions
317 }
318
319 async fn retrain_model(&mut self, node_id: &str) {
320 if let Some(history) = self.node_history.get(node_id) {
321 if history.len() < 50 {
322 return; // Need more data
323 }
324
325 let mut model = PredictionModel {
326 weights: vec![0.1; 8], // Initialize with small weights
327 bias: 0.0,
328 accuracy: 0.5,
329 last_updated: Instant::now(),
330 };
331
332 // Simple gradient descent training
333 let learning_rate = 0.01;
334 let epochs = 100;
335
336 for _ in 0..epochs {
337 for example in &self.training_data {
338 if example.features.len() == 8 {
339 let prediction = self.calculate_failure_probability(&example.features, &model);
340 let target = if example.outcome { 1.0 } else { 0.0 };
341 let error = prediction - target;
342
343 // Update weights
344 for (i, feature) in example.features.iter().enumerate() {
345 model.weights[i] -= learning_rate * error * feature;
346 }
347 model.bias -= learning_rate * error;
348 }
349 }
350 }
351
352 // Calculate accuracy on validation set
353 let mut correct = 0;
354 let mut total = 0;
355 for example in &self.training_data {
356 if example.features.len() == 8 {
357 let prediction = self.calculate_failure_probability(&example.features, &model);
358 let predicted_outcome = prediction > 0.5;
359 if predicted_outcome == example.outcome {
360 correct += 1;
361 }
362 total += 1;
363 }
364 }
365
366 if total > 0 {
367 model.accuracy = correct as f32 / total as f32;
368 }
369
370 self.prediction_models.insert(node_id.to_string(), model);
371 }
372 }
373
374 fn detect_failure_transition(&self, previous: &NodeMetrics, current: &NodeMetrics) -> bool {
375 // Detect if node has failed based on metrics change
376 let uptime_drop = previous.uptime_percentage - current.uptime_percentage;
377 let latency_spike = current.response_latency.as_millis() as f32 /
378 previous.response_latency.as_millis() as f32;
379 let error_increase = current.error_rate / previous.error_rate.max(0.001);
380
381 uptime_drop > 0.2 || latency_spike > 2.0 || error_increase > 3.0
382 }
383
384 fn calculate_hardware_score(&self, health: &HardwareHealth) -> f32 {
385 let temp_score = if health.cpu_temperature > 90.0 { 0.0 }
386 else if health.cpu_temperature > 80.0 { 0.3 }
387 else if health.cpu_temperature > 70.0 { 0.7 }
388 else { 1.0 };
389
390 let disk_score = health.disk_health_score;
391 let memory_score = if health.memory_errors > 10 { 0.2 }
392 else if health.memory_errors > 5 { 0.6 }
393 else { 1.0 };
394 let power_score = health.power_stability;
395
396 (temp_score + disk_score + memory_score + power_score) / 4.0
397 }
398
399 fn calculate_geographic_risk_score(&self, risk: &GeographicRisk) -> f32 {
400 let disaster_score = 1.0 - risk.natural_disaster_risk;
401 let political_score = risk.political_stability;
402 let infrastructure_score = risk.infrastructure_quality;
403 let connectivity_score = risk.connectivity_redundancy;
404
405 (disaster_score + political_score + infrastructure_score + connectivity_score) / 4.0
406 }
407
408 fn calculate_network_stability_score(&self, stability: &NetworkStability) -> f32 {
409 let connection_score = if stability.connection_drops > 10 { 0.2 }
410 else if stability.connection_drops > 5 { 0.6 }
411 else { 1.0 };
412
413 let peer_score = if stability.peer_count < 3 { 0.3 }
414 else if stability.peer_count < 8 { 0.7 }
415 else { 1.0 };
416
417 let routing_score = stability.routing_efficiency;
418 let congestion_score = 1.0 - stability.congestion_level;
419
420 (connection_score + peer_score + routing_score + congestion_score) / 4.0
421 }
422 }
423
424 impl Default for FeatureWeights {
425 fn default() -> Self {
426 Self {
427 uptime: 0.25,
428 latency: 0.20,
429 storage: 0.15,
430 bandwidth: 0.10,
431 error_rate: 0.15,
432 hardware_health: 0.05,
433 geographic_risk: 0.05,
434 network_stability: 0.05,
435 }
436 }
437 }
438
439 pub struct ProactiveReplicationManager {
440 predictor: MLPredictor,
441 migration_scheduler: MigrationScheduler,
442 chunk_priority_queue: Vec<ChunkMigrationTask>,
443 }
444
445 #[derive(Debug, Clone)]
446 struct ChunkMigrationTask {
447 chunk_id: String,
448 source_nodes: Vec<String>,
449 target_nodes: Vec<String>,
450 priority: u8, // 1-10, higher is more urgent
451 deadline: Instant,
452 estimated_transfer_time: Duration,
453 }
454
455 struct MigrationScheduler {
456 active_migrations: HashMap<String, MigrationProgress>,
457 bandwidth_budget: BandwidthBudget,
458 node_capacities: HashMap<String, NodeCapacity>,
459 }
460
461 #[derive(Debug, Clone)]
462 struct MigrationProgress {
463 task: ChunkMigrationTask,
464 bytes_transferred: u64,
465 total_bytes: u64,
466 start_time: Instant,
467 estimated_completion: Instant,
468 }
469
470 #[derive(Debug, Clone)]
471 struct BandwidthBudget {
472 total_available: u64, // bytes per second
473 reserved_for_users: u64,
474 available_for_migration: u64,
475 current_usage: u64,
476 }
477
478 #[derive(Debug, Clone)]
479 struct NodeCapacity {
480 storage_available: u64,
481 bandwidth_capacity: u64,
482 current_load: f32,
483 reliability_score: f32,
484 }
485
486 impl ProactiveReplicationManager {
487 pub fn new() -> Self {
488 Self {
489 predictor: MLPredictor::new(),
490 migration_scheduler: MigrationScheduler::new(),
491 chunk_priority_queue: Vec::new(),
492 }
493 }
494
495 pub async fn analyze_and_migrate(&mut self) -> Result<(), Box<dyn std::error::Error>> {
496 // Get high-risk nodes
497 let high_risk_nodes = self.predictor.get_high_risk_nodes().await;
498
499 for prediction in high_risk_nodes {
500 if prediction.failure_probability > 0.5 {
501 self.schedule_emergency_migration(&prediction.node_id, prediction.failure_probability).await?;
502 } else if prediction.failure_probability > 0.3 {
503 self.schedule_preemptive_migration(&prediction.node_id, prediction.failure_probability).await?;
504 }
505 }
506
507 // Execute scheduled migrations
508 self.execute_migration_queue().await?;
509
510 Ok(())
511 }
512
513 async fn schedule_emergency_migration(&mut self, node_id: &str, risk: f32) -> Result<(), Box<dyn std::error::Error>> {
514 let chunks = self.get_chunks_on_node(node_id).await?;
515
516 for chunk_id in chunks {
517 let task = ChunkMigrationTask {
518 chunk_id,
519 source_nodes: vec![node_id.to_string()],
520 target_nodes: self.select_migration_targets(2, Some(node_id)).await?,
521 priority: 10, // Highest priority
522 deadline: Instant::now() + Duration::from_secs(1800), // 30 minutes
523 estimated_transfer_time: Duration::from_secs(300), // 5 minutes estimate
524 };
525
526 self.chunk_priority_queue.push(task);
527 }
528
529 // Sort by priority and deadline
530 self.chunk_priority_queue.sort_by(|a, b| {
531 b.priority.cmp(&a.priority)
532 .then_with(|| a.deadline.cmp(&b.deadline))
533 });
534
535 Ok(())
536 }
537
538 async fn schedule_preemptive_migration(&mut self, node_id: &str, risk: f32) -> Result<(), Box<dyn std::error::Error>> {
539 let chunks = self.get_chunks_on_node(node_id).await?;
540 let priority = ((risk - 0.3) * 20.0) as u8 + 3; // Priority 3-7
541
542 for chunk_id in chunks {
543 let task = ChunkMigrationTask {
544 chunk_id,
545 source_nodes: vec![node_id.to_string()],
546 target_nodes: self.select_migration_targets(1, Some(node_id)).await?,
547 priority,
548 deadline: Instant::now() + Duration::from_secs(7200), // 2 hours
549 estimated_transfer_time: Duration::from_secs(600), // 10 minutes estimate
550 };
551
552 self.chunk_priority_queue.push(task);
553 }
554
555 self.chunk_priority_queue.sort_by(|a, b| {
556 b.priority.cmp(&a.priority)
557 .then_with(|| a.deadline.cmp(&b.deadline))
558 });
559
560 Ok(())
561 }
562
563 async fn execute_migration_queue(&mut self) -> Result<(), Box<dyn std::error::Error>> {
564 let available_bandwidth = self.migration_scheduler.bandwidth_budget.available_for_migration;
565 let mut current_bandwidth_usage = 0u64;
566
567 while let Some(task) = self.chunk_priority_queue.pop() {
568 if current_bandwidth_usage + self.estimate_bandwidth_usage(&task) > available_bandwidth {
569 // Put task back and wait for next cycle
570 self.chunk_priority_queue.push(task);
571 break;
572 }
573
574 if self.can_start_migration(&task).await {
575 self.start_migration(task).await?;
576 current_bandwidth_usage += self.estimate_bandwidth_usage(&self.chunk_priority_queue.last().unwrap());
577 }
578 }
579
580 Ok(())
581 }
582
583 async fn get_chunks_on_node(&self, _node_id: &str) -> Result<Vec<String>, Box<dyn std::error::Error>> {
584 // Placeholder: In reality, this would query the chunk index
585 Ok(vec!["chunk_1".to_string(), "chunk_2".to_string()])
586 }
587
588 async fn select_migration_targets(&self, count: usize, avoid_node: Option<&str>) -> Result<Vec<String>, Box<dyn std::error::Error>> {
589 let mut candidates: Vec<_> = self.migration_scheduler.node_capacities.iter()
590 .filter(|(node_id, capacity)| {
591 if let Some(avoid) = avoid_node {
592 *node_id != avoid && capacity.current_load < 0.8 && capacity.reliability_score > 0.7
593 } else {
594 capacity.current_load < 0.8 && capacity.reliability_score > 0.7
595 }
596 })
597 .collect();
598
599 candidates.sort_by(|a, b| b.1.reliability_score.partial_cmp(&a.1.reliability_score).unwrap());
600
601 Ok(candidates.into_iter()
602 .take(count)
603 .map(|(node_id, _)| node_id.clone())
604 .collect())
605 }
606
607 async fn can_start_migration(&self, task: &ChunkMigrationTask) -> bool {
608 // Check if target nodes have capacity
609 for target_node in &task.target_nodes {
610 if let Some(capacity) = self.migration_scheduler.node_capacities.get(target_node) {
611 if capacity.current_load > 0.85 {
612 return false;
613 }
614 }
615 }
616
617 // Check if we're not already migrating this chunk
618 !self.migration_scheduler.active_migrations.contains_key(&task.chunk_id)
619 }
620
621 async fn start_migration(&mut self, task: ChunkMigrationTask) -> Result<(), Box<dyn std::error::Error>> {
622 let progress = MigrationProgress {
623 task: task.clone(),
624 bytes_transferred: 0,
625 total_bytes: 1024 * 1024, // 1MB estimate
626 start_time: Instant::now(),
627 estimated_completion: Instant::now() + task.estimated_transfer_time,
628 };
629
630 self.migration_scheduler.active_migrations.insert(task.chunk_id, progress);
631
632 // Placeholder: In reality, this would initiate the actual transfer
633 println!("Starting migration of chunk {} from {:?} to {:?}",
634 task.chunk_id, task.source_nodes, task.target_nodes);
635
636 Ok(())
637 }
638
639 fn estimate_bandwidth_usage(&self, task: &ChunkMigrationTask) -> u64 {
640 // Estimate based on chunk size and transfer time
641 let chunk_size = 1024 * 1024; // 1MB estimate
642 let transfer_duration = task.estimated_transfer_time.as_secs().max(1);
643 chunk_size / transfer_duration
644 }
645 }
646
647 impl MigrationScheduler {
648 fn new() -> Self {
649 Self {
650 active_migrations: HashMap::new(),
651 bandwidth_budget: BandwidthBudget {
652 total_available: 100 * 1024 * 1024, // 100 MB/s
653 reserved_for_users: 70 * 1024 * 1024, // 70 MB/s for users
654 available_for_migration: 30 * 1024 * 1024, // 30 MB/s for migration
655 current_usage: 0,
656 },
657 node_capacities: HashMap::new(),
658 }
659 }
660 }