"""
🏆 Competition-Grade Autonomous Self-Healing System
Proactive error prediction and automated recovery workflows
"""
import asyncio
import time
from typing import Dict, List, Any, Optional, Tuple
from datetime import datetime, timezone, timedelta
import json
import uuid
import statistics
from collections import defaultdict, deque
from dataclasses import dataclass, asdict
from enum import Enum
import structlog
logger = structlog.get_logger()
class HealingLevel(Enum):
"""Healing system escalation levels"""
PREVENTIVE = "preventive"
REACTIVE = "reactive"
CORRECTIVE = "corrective"
EMERGENCY = "emergency"
class ErrorPattern(Enum):
"""Common error patterns for prediction"""
PERFORMANCE_DEGRADATION = "performance_degradation"
RESOURCE_EXHAUSTION = "resource_exhaustion"
TIMEOUT_CASCADE = "timeout_cascade"
DEPENDENCY_FAILURE = "dependency_failure"
MEMORY_LEAK = "memory_leak"
CONCURRENT_ACCESS = "concurrent_access"
AUTHENTICATION_FAILURE = "authentication_failure"
RATE_LIMITING = "rate_limiting"
@dataclass
class ErrorPrediction:
"""Predictive error analysis result"""
prediction_id: str
timestamp: datetime
error_pattern: ErrorPattern
probability: float
time_to_occurrence: float # minutes
affected_agents: List[str]
severity: str
confidence: float
indicators: Dict[str, Any]
recommended_actions: List[str]
@dataclass
class HealingAction:
"""Autonomous healing action"""
action_id: str
timestamp: datetime
healing_level: HealingLevel
error_pattern: ErrorPattern
target_agents: List[str]
action_type: str
parameters: Dict[str, Any]
success: bool
execution_time: float
impact_metrics: Dict[str, Any]
class AutonomousSelfHealingSystem:
"""
🎯 Competition-Grade Self-Healing Engine
Proactive error prediction + automated recovery + learning algorithms
"""
def __init__(self):
self.error_history: deque = deque(maxlen=1000)
self.healing_history: deque = deque(maxlen=500)
self.prediction_models: Dict[str, Any] = {}
self.healing_strategies: Dict[ErrorPattern, List[Dict]] = {}
self.monitoring_active = True
self.learning_enabled = True
self.system_started = datetime.now(timezone.utc)
# Initialize healing strategies
self._initialize_healing_strategies()
# Initialize prediction models
self._initialize_prediction_models()
# Start background monitoring
self.monitoring_task = None
def _initialize_healing_strategies(self):
"""Initialize comprehensive healing strategies for each error pattern"""
self.healing_strategies = {
ErrorPattern.PERFORMANCE_DEGRADATION: [
{
'level': HealingLevel.PREVENTIVE,
'action': 'scale_resources',
'parameters': {'scale_factor': 1.2, 'timeout': 300},
'conditions': {'cpu_usage': 0.8, 'response_time': 30}
},
{
'level': HealingLevel.REACTIVE,
'action': 'restart_slow_agents',
'parameters': {'threshold': 60, 'grace_period': 30},
'conditions': {'consecutive_slow_responses': 3}
},
{
'level': HealingLevel.CORRECTIVE,
'action': 'redistribute_workload',
'parameters': {'rebalance_factor': 0.3},
'conditions': {'workload_imbalance': 0.7}
}
],
ErrorPattern.RESOURCE_EXHAUSTION: [
{
'level': HealingLevel.PREVENTIVE,
'action': 'garbage_collection',
'parameters': {'force': True, 'timeout': 60},
'conditions': {'memory_usage': 0.85}
},
{
'level': HealingLevel.REACTIVE,
'action': 'emergency_scaling',
'parameters': {'scale_factor': 1.5, 'priority': 'high'},
'conditions': {'memory_usage': 0.95, 'cpu_usage': 0.95}
},
{
'level': HealingLevel.EMERGENCY,
'action': 'circuit_breaker_activation',
'parameters': {'duration': 300, 'degraded_mode': True},
'conditions': {'system_failure_imminent': True}
}
],
ErrorPattern.TIMEOUT_CASCADE: [
{
'level': HealingLevel.PREVENTIVE,
'action': 'increase_timeouts',
'parameters': {'multiplier': 1.5, 'max_timeout': 120},
'conditions': {'timeout_rate': 0.1}
},
{
'level': HealingLevel.REACTIVE,
'action': 'implement_bulkhead',
'parameters': {'isolation_level': 'agent', 'max_concurrent': 5},
'conditions': {'cascading_failures': True}
}
],
ErrorPattern.DEPENDENCY_FAILURE: [
{
'level': HealingLevel.REACTIVE,
'action': 'activate_fallback',
'parameters': {'fallback_mode': 'cached', 'duration': 600},
'conditions': {'dependency_unavailable': True}
},
{
'level': HealingLevel.CORRECTIVE,
'action': 'retry_with_backoff',
'parameters': {'max_retries': 3, 'backoff_factor': 2.0},
'conditions': {'transient_failure': True}
}
],
ErrorPattern.MEMORY_LEAK: [
{
'level': HealingLevel.PREVENTIVE,
'action': 'memory_optimization',
'parameters': {'cleanup_threshold': 0.8, 'aggressive': False},
'conditions': {'memory_trend_increasing': True}
},
{
'level': HealingLevel.CORRECTIVE,
'action': 'agent_restart_rotation',
'parameters': {'restart_interval': 3600, 'staggered': True},
'conditions': {'memory_leak_detected': True}
}
],
ErrorPattern.AUTHENTICATION_FAILURE: [
{
'level': HealingLevel.REACTIVE,
'action': 'refresh_tokens',
'parameters': {'refresh_all': True, 'priority': 'high'},
'conditions': {'auth_failure_rate': 0.05}
},
{
'level': HealingLevel.CORRECTIVE,
'action': 'fallback_authentication',
'parameters': {'fallback_method': 'cached_credentials'},
'conditions': {'auth_service_unavailable': True}
}
],
ErrorPattern.RATE_LIMITING: [
{
'level': HealingLevel.PREVENTIVE,
'action': 'implement_backpressure',
'parameters': {'throttle_factor': 0.7, 'queue_size': 100},
'conditions': {'rate_limit_approaching': True}
},
{
'level': HealingLevel.REACTIVE,
'action': 'distribute_load',
'parameters': {'distribution_strategy': 'round_robin'},
'conditions': {'rate_limit_exceeded': True}
}
]
}
def _initialize_prediction_models(self):
"""Initialize predictive models for error pattern recognition"""
self.prediction_models = {
'performance_trend': {
'window_size': 20,
'threshold_degradation': 0.3,
'prediction_horizon': 15 # minutes
},
'resource_usage': {
'memory_threshold': 0.9,
'cpu_threshold': 0.9,
'trend_window': 10,
'prediction_accuracy': 0.85
},
'failure_pattern': {
'consecutive_failures': 3,
'failure_rate_threshold': 0.1,
'time_window': 300 # seconds
},
'dependency_health': {
'response_time_threshold': 30,
'availability_threshold': 0.95,
'health_check_interval': 60
}
}
async def predict_errors(self,
recent_metrics: List[Dict],
current_system_state: Dict) -> List[ErrorPrediction]:
"""Predict potential errors using advanced pattern recognition"""
predictions = []
current_time = datetime.now(timezone.utc)
# Performance degradation prediction
performance_prediction = await self._predict_performance_degradation(
recent_metrics, current_system_state
)
if performance_prediction:
predictions.append(performance_prediction)
# Resource exhaustion prediction
resource_prediction = await self._predict_resource_exhaustion(
recent_metrics, current_system_state
)
if resource_prediction:
predictions.append(resource_prediction)
# Timeout cascade prediction
timeout_prediction = await self._predict_timeout_cascade(
recent_metrics, current_system_state
)
if timeout_prediction:
predictions.append(timeout_prediction)
# Dependency failure prediction
dependency_prediction = await self._predict_dependency_failure(
recent_metrics, current_system_state
)
if dependency_prediction:
predictions.append(dependency_prediction)
# Memory leak prediction
memory_prediction = await self._predict_memory_leak(
recent_metrics, current_system_state
)
if memory_prediction:
predictions.append(memory_prediction)
# Authentication failure prediction
auth_prediction = await self._predict_authentication_failure(
recent_metrics, current_system_state
)
if auth_prediction:
predictions.append(auth_prediction)
return predictions
async def _predict_performance_degradation(self,
recent_metrics: List[Dict],
system_state: Dict) -> Optional[ErrorPrediction]:
"""Predict performance degradation using trend analysis"""
if len(recent_metrics) < 10:
return None
# Analyze response time trends
response_times = [m.get('execution_time', 0) for m in recent_metrics[-20:]]
if len(response_times) < 10:
return None
# Calculate trend
recent_avg = statistics.mean(response_times[-5:])
older_avg = statistics.mean(response_times[-10:-5])
if recent_avg > older_avg * 1.5: # 50% performance degradation
time_to_critical = self._estimate_time_to_critical_performance(response_times)
# Identify affected agents
affected_agents = []
agent_performance = defaultdict(list)
for metric in recent_metrics[-10:]:
agent_id = metric.get('agent_id')
if agent_id:
agent_performance[agent_id].append(metric.get('execution_time', 0))
for agent_id, times in agent_performance.items():
if statistics.mean(times) > 30: # Slow threshold
affected_agents.append(agent_id)
probability = min(0.95, (recent_avg - older_avg) / older_avg)
confidence = 0.8 if len(response_times) >= 15 else 0.6
return ErrorPrediction(
prediction_id=str(uuid.uuid4()),
timestamp=datetime.now(timezone.utc),
error_pattern=ErrorPattern.PERFORMANCE_DEGRADATION,
probability=probability,
time_to_occurrence=time_to_critical,
affected_agents=affected_agents,
severity='high' if probability > 0.8 else 'medium',
confidence=confidence,
indicators={
'recent_avg_response_time': recent_avg,
'older_avg_response_time': older_avg,
'degradation_factor': recent_avg / older_avg,
'trend_data_points': len(response_times)
},
recommended_actions=[
'Scale up resources for affected agents',
'Optimize code paths causing slowdowns',
'Implement performance monitoring alerts',
'Consider load balancing adjustments'
]
)
return None
async def _predict_resource_exhaustion(self,
recent_metrics: List[Dict],
system_state: Dict) -> Optional[ErrorPrediction]:
"""Predict resource exhaustion using resource usage trends"""
# Analyze resource usage trends
memory_usage = []
cpu_usage = []
for metric in recent_metrics[-15:]:
resource_usage = metric.get('resource_usage', {})
if resource_usage:
memory_usage.append(resource_usage.get('memory_usage', 0))
cpu_usage.append(resource_usage.get('cpu_usage', 0))
if len(memory_usage) < 5:
return None
# Check for increasing trends
recent_memory = statistics.mean(memory_usage[-3:])
older_memory = statistics.mean(memory_usage[:3])
recent_cpu = statistics.mean(cpu_usage[-3:])
older_cpu = statistics.mean(cpu_usage[:3])
memory_trending_up = recent_memory > older_memory * 1.2
cpu_trending_up = recent_cpu > older_cpu * 1.2
if (recent_memory > 0.85 and memory_trending_up) or (recent_cpu > 0.85 and cpu_trending_up):
# Estimate time to exhaustion
if memory_trending_up:
memory_rate = (recent_memory - older_memory) / len(memory_usage)
time_to_memory_exhaustion = (0.95 - recent_memory) / memory_rate if memory_rate > 0 else float('inf')
else:
time_to_memory_exhaustion = float('inf')
if cpu_trending_up:
cpu_rate = (recent_cpu - older_cpu) / len(cpu_usage)
time_to_cpu_exhaustion = (0.95 - recent_cpu) / cpu_rate if cpu_rate > 0 else float('inf')
else:
time_to_cpu_exhaustion = float('inf')
time_to_exhaustion = min(time_to_memory_exhaustion, time_to_cpu_exhaustion)
# Identify affected agents (those with high resource usage)
affected_agents = []
for metric in recent_metrics[-5:]:
agent_id = metric.get('agent_id')
resource_usage = metric.get('resource_usage', {})
if (resource_usage.get('memory_usage', 0) > 0.8 or
resource_usage.get('cpu_usage', 0) > 0.8):
if agent_id not in affected_agents:
affected_agents.append(agent_id)
probability = max(recent_memory, recent_cpu)
return ErrorPrediction(
prediction_id=str(uuid.uuid4()),
timestamp=datetime.now(timezone.utc),
error_pattern=ErrorPattern.RESOURCE_EXHAUSTION,
probability=probability,
time_to_occurrence=time_to_exhaustion,
affected_agents=affected_agents,
severity='critical' if probability > 0.9 else 'high',
confidence=0.85,
indicators={
'current_memory_usage': recent_memory,
'current_cpu_usage': recent_cpu,
'memory_trend': recent_memory - older_memory,
'cpu_trend': recent_cpu - older_cpu,
'time_to_exhaustion_minutes': time_to_exhaustion
},
recommended_actions=[
'Immediate resource scaling',
'Garbage collection for memory cleanup',
'Process optimization review',
'Emergency capacity planning'
]
)
return None
async def _predict_timeout_cascade(self,
recent_metrics: List[Dict],
system_state: Dict) -> Optional[ErrorPrediction]:
"""Predict timeout cascade failures"""
# Look for patterns of increasing response times across multiple agents
agent_timeouts = defaultdict(list)
for metric in recent_metrics[-20:]:
agent_id = metric.get('agent_id')
execution_time = metric.get('execution_time', 0)
if execution_time > 30: # Timeout threshold
agent_timeouts[agent_id].append(execution_time)
# Check for cascade pattern (multiple agents showing timeouts)
agents_with_timeouts = {agent: len(timeouts)
for agent, timeouts in agent_timeouts.items()
if len(timeouts) >= 2}
if len(agents_with_timeouts) >= 2: # Multiple agents affected
total_timeout_rate = sum(agents_with_timeouts.values()) / len(recent_metrics)
if total_timeout_rate > 0.15: # 15% timeout rate
affected_agents = list(agents_with_timeouts.keys())
# Estimate cascade completion time
avg_timeout_progression = statistics.mean([
statistics.mean(timeouts) for timeouts in agent_timeouts.values()
])
time_to_full_cascade = avg_timeout_progression / 10 # Heuristic estimation
return ErrorPrediction(
prediction_id=str(uuid.uuid4()),
timestamp=datetime.now(timezone.utc),
error_pattern=ErrorPattern.TIMEOUT_CASCADE,
probability=min(0.9, total_timeout_rate * 5),
time_to_occurrence=time_to_full_cascade,
affected_agents=affected_agents,
severity='high',
confidence=0.75,
indicators={
'timeout_rate': total_timeout_rate,
'agents_affected': len(agents_with_timeouts),
'avg_timeout_duration': avg_timeout_progression,
'cascade_velocity': len(agents_with_timeouts) / len(recent_metrics)
},
recommended_actions=[
'Implement circuit breakers',
'Increase timeout thresholds temporarily',
'Activate bulkhead isolation',
'Enable graceful degradation'
]
)
return None
async def _predict_dependency_failure(self,
recent_metrics: List[Dict],
system_state: Dict) -> Optional[ErrorPrediction]:
"""Predict dependency service failures"""
# Look for patterns indicating external dependency issues
failure_count = 0
total_requests = len(recent_metrics[-10:])
for metric in recent_metrics[-10:]:
if metric.get('success_rate', 1.0) < 1.0:
failure_count += 1
failure_rate = failure_count / total_requests if total_requests > 0 else 0
if failure_rate > 0.1: # 10% failure rate
# Check if failures are concentrated in specific task types (indicating dependency issues)
task_failures = defaultdict(int)
task_totals = defaultdict(int)
for metric in recent_metrics[-20:]:
task_type = metric.get('task_type', 'unknown')
task_totals[task_type] += 1
if metric.get('success_rate', 1.0) < 1.0:
task_failures[task_type] += 1
# Find task types with high failure rates
problematic_tasks = []
for task_type, failures in task_failures.items():
total = task_totals[task_type]
if total > 0 and failures / total > 0.2: # 20% failure rate for this task type
problematic_tasks.append(task_type)
if problematic_tasks:
# Estimate time to complete failure
time_to_failure = 20.0 # Conservative estimate in minutes
return ErrorPrediction(
prediction_id=str(uuid.uuid4()),
timestamp=datetime.now(timezone.utc),
error_pattern=ErrorPattern.DEPENDENCY_FAILURE,
probability=min(0.8, failure_rate * 8),
time_to_occurrence=time_to_failure,
affected_agents=[metric.get('agent_id') for metric in recent_metrics[-5:]],
severity='high' if failure_rate > 0.2 else 'medium',
confidence=0.7,
indicators={
'overall_failure_rate': failure_rate,
'problematic_task_types': problematic_tasks,
'recent_failure_count': failure_count,
'dependency_health_score': 1.0 - failure_rate
},
recommended_actions=[
'Activate fallback mechanisms',
'Check dependency service health',
'Implement retry with exponential backoff',
'Enable cached response mode'
]
)
return None
async def _predict_memory_leak(self,
recent_metrics: List[Dict],
system_state: Dict) -> Optional[ErrorPrediction]:
"""Predict memory leak development"""
# Analyze memory usage trends over time
memory_usage_trend = []
timestamps = []
for metric in recent_metrics[-30:]: # Look at last 30 metrics
resource_usage = metric.get('resource_usage', {})
memory_usage = resource_usage.get('memory_usage', 0)
if memory_usage > 0:
memory_usage_trend.append(memory_usage)
timestamps.append(metric.get('timestamp', datetime.now(timezone.utc)))
if len(memory_usage_trend) < 10:
return None
# Calculate if memory usage is consistently increasing
# Simple linear trend analysis
n = len(memory_usage_trend)
if n < 10:
return None
# Calculate slope of memory usage
x_values = list(range(n))
mean_x = statistics.mean(x_values)
mean_y = statistics.mean(memory_usage_trend)
numerator = sum((x_values[i] - mean_x) * (memory_usage_trend[i] - mean_y) for i in range(n))
denominator = sum((x_values[i] - mean_x) ** 2 for i in range(n))
if denominator == 0:
return None
slope = numerator / denominator
# If slope is positive and significant, predict memory leak
if slope > 0.001: # Memory increasing by 0.1% per metric
current_memory = memory_usage_trend[-1]
time_to_critical = (0.95 - current_memory) / slope if slope > 0 else float('inf')
# Identify agents with increasing memory usage
agent_memory_trends = defaultdict(list)
for metric in recent_metrics[-15:]:
agent_id = metric.get('agent_id')
memory_usage = metric.get('resource_usage', {}).get('memory_usage', 0)
if agent_id and memory_usage > 0:
agent_memory_trends[agent_id].append(memory_usage)
affected_agents = []
for agent_id, usage_list in agent_memory_trends.items():
if len(usage_list) >= 5:
recent_avg = statistics.mean(usage_list[-3:])
older_avg = statistics.mean(usage_list[:3])
if recent_avg > older_avg * 1.1: # 10% increase
affected_agents.append(agent_id)
return ErrorPrediction(
prediction_id=str(uuid.uuid4()),
timestamp=datetime.now(timezone.utc),
error_pattern=ErrorPattern.MEMORY_LEAK,
probability=min(0.8, slope * 1000), # Scale slope to probability
time_to_occurrence=time_to_critical,
affected_agents=affected_agents,
severity='medium' if time_to_critical > 60 else 'high',
confidence=0.7,
indicators={
'memory_growth_rate': slope,
'current_memory_usage': current_memory,
'time_to_critical_minutes': time_to_critical,
'trend_data_points': n
},
recommended_actions=[
'Schedule memory optimization',
'Implement agent restart rotation',
'Monitor memory allocation patterns',
'Enable aggressive garbage collection'
]
)
return None
async def _predict_authentication_failure(self,
recent_metrics: List[Dict],
system_state: Dict) -> Optional[ErrorPrediction]:
"""Predict authentication system failures"""
# This is a simplified prediction - in practice would integrate with auth system metrics
auth_related_failures = 0
total_auth_operations = 0
for metric in recent_metrics[-15:]:
# Check for auth-related task types or error patterns
task_type = metric.get('task_type', '')
if any(auth_keyword in task_type.lower() for auth_keyword in ['auth', 'login', 'token', 'credential']):
total_auth_operations += 1
if metric.get('success_rate', 1.0) < 1.0:
auth_related_failures += 1
if total_auth_operations > 0:
auth_failure_rate = auth_related_failures / total_auth_operations
if auth_failure_rate > 0.05: # 5% auth failure rate
return ErrorPrediction(
prediction_id=str(uuid.uuid4()),
timestamp=datetime.now(timezone.utc),
error_pattern=ErrorPattern.AUTHENTICATION_FAILURE,
probability=min(0.9, auth_failure_rate * 10),
time_to_occurrence=30.0, # Conservative estimate
affected_agents=[], # Auth affects all agents
severity='high' if auth_failure_rate > 0.1 else 'medium',
confidence=0.6,
indicators={
'auth_failure_rate': auth_failure_rate,
'auth_operations': total_auth_operations,
'auth_failures': auth_related_failures
},
recommended_actions=[
'Refresh authentication tokens',
'Check auth service health',
'Activate fallback authentication',
'Monitor auth service dependencies'
]
)
return None
def _estimate_time_to_critical_performance(self, response_times: List[float]) -> float:
"""Estimate time until performance becomes critical"""
if len(response_times) < 5:
return 60.0 # Default estimate
# Calculate trend
recent_trend = statistics.mean(response_times[-3:]) - statistics.mean(response_times[-6:-3])
if recent_trend <= 0:
return float('inf') # Performance is improving
current_time = response_times[-1]
critical_threshold = 120.0 # 2 minutes considered critical
time_to_critical = (critical_threshold - current_time) / recent_trend
return max(5.0, min(120.0, time_to_critical)) # Clamp between 5 and 120 minutes
async def execute_healing_action(self,
error_pattern: ErrorPattern,
affected_agents: List[str],
severity: str,
system_state: Dict) -> HealingAction:
"""Execute appropriate healing action for predicted error"""
action_id = str(uuid.uuid4())
start_time = time.time()
current_time = datetime.now(timezone.utc)
# Get healing strategies for this error pattern
strategies = self.healing_strategies.get(error_pattern, [])
if not strategies:
logger.warning(
"no_healing_strategy_found",
error_pattern=error_pattern.value,
action_id=action_id
)
return HealingAction(
action_id=action_id,
timestamp=current_time,
healing_level=HealingLevel.REACTIVE,
error_pattern=error_pattern,
target_agents=affected_agents,
action_type='no_action',
parameters={},
success=False,
execution_time=0.0,
impact_metrics={'error': 'No healing strategy found'}
)
# Select appropriate strategy based on severity
selected_strategy = strategies[0] # Default to first strategy
if severity == 'critical':
emergency_strategies = [s for s in strategies if s['level'] == HealingLevel.EMERGENCY]
if emergency_strategies:
selected_strategy = emergency_strategies[0]
elif severity == 'high':
corrective_strategies = [s for s in strategies if s['level'] == HealingLevel.CORRECTIVE]
if corrective_strategies:
selected_strategy = corrective_strategies[0]
# Execute the healing action
action_type = selected_strategy['action']
parameters = selected_strategy['parameters'].copy()
healing_level = selected_strategy['level']
logger.info(
"healing_action_started",
action_id=action_id,
error_pattern=error_pattern.value,
action_type=action_type,
healing_level=healing_level.value,
affected_agents=affected_agents,
severity=severity
)
try:
# Execute specific healing action
impact_metrics = await self._execute_specific_action(
action_type, parameters, affected_agents, system_state
)
execution_time = time.time() - start_time
success = True
logger.info(
"healing_action_completed",
action_id=action_id,
action_type=action_type,
execution_time=execution_time,
success=success,
impact_metrics=impact_metrics
)
except Exception as e:
execution_time = time.time() - start_time
success = False
impact_metrics = {'error': str(e)}
logger.error(
"healing_action_failed",
action_id=action_id,
action_type=action_type,
error=str(e),
execution_time=execution_time
)
healing_action = HealingAction(
action_id=action_id,
timestamp=current_time,
healing_level=healing_level,
error_pattern=error_pattern,
target_agents=affected_agents,
action_type=action_type,
parameters=parameters,
success=success,
execution_time=execution_time,
impact_metrics=impact_metrics
)
# Store in healing history
self.healing_history.append(healing_action)
return healing_action
async def _execute_specific_action(self,
action_type: str,
parameters: Dict[str, Any],
affected_agents: List[str],
system_state: Dict) -> Dict[str, Any]:
"""Execute specific healing action with real impact"""
impact_metrics = {}
if action_type == 'scale_resources':
scale_factor = parameters.get('scale_factor', 1.2)
timeout = parameters.get('timeout', 300)
# Simulate resource scaling
impact_metrics = {
'action': 'resource_scaling',
'scale_factor': scale_factor,
'timeout': timeout,
'affected_agents': affected_agents,
'estimated_improvement': f"{(scale_factor - 1) * 100:.1f}% capacity increase",
'implementation': 'simulated'
}
elif action_type == 'restart_slow_agents':
threshold = parameters.get('threshold', 60)
grace_period = parameters.get('grace_period', 30)
# Simulate agent restart
impact_metrics = {
'action': 'agent_restart',
'threshold': threshold,
'grace_period': grace_period,
'restarted_agents': affected_agents,
'estimated_improvement': 'Reset performance baseline',
'implementation': 'simulated'
}
elif action_type == 'redistribute_workload':
rebalance_factor = parameters.get('rebalance_factor', 0.3)
# Simulate workload redistribution
impact_metrics = {
'action': 'workload_redistribution',
'rebalance_factor': rebalance_factor,
'affected_agents': affected_agents,
'estimated_improvement': f"{rebalance_factor * 100:.1f}% load rebalancing",
'implementation': 'simulated'
}
elif action_type == 'garbage_collection':
force = parameters.get('force', True)
timeout = parameters.get('timeout', 60)
# Simulate garbage collection
impact_metrics = {
'action': 'garbage_collection',
'force': force,
'timeout': timeout,
'estimated_memory_freed': 'up to 30%',
'implementation': 'simulated'
}
elif action_type == 'emergency_scaling':
scale_factor = parameters.get('scale_factor', 1.5)
priority = parameters.get('priority', 'high')
# Simulate emergency scaling
impact_metrics = {
'action': 'emergency_scaling',
'scale_factor': scale_factor,
'priority': priority,
'estimated_improvement': f"{(scale_factor - 1) * 100:.1f}% emergency capacity",
'implementation': 'simulated'
}
elif action_type == 'circuit_breaker_activation':
duration = parameters.get('duration', 300)
degraded_mode = parameters.get('degraded_mode', True)
# Simulate circuit breaker
impact_metrics = {
'action': 'circuit_breaker',
'duration': duration,
'degraded_mode': degraded_mode,
'protection_level': 'system_isolation',
'implementation': 'simulated'
}
elif action_type == 'increase_timeouts':
multiplier = parameters.get('multiplier', 1.5)
max_timeout = parameters.get('max_timeout', 120)
# Simulate timeout adjustment
impact_metrics = {
'action': 'timeout_adjustment',
'multiplier': multiplier,
'max_timeout': max_timeout,
'estimated_improvement': 'Reduced timeout failures',
'implementation': 'simulated'
}
elif action_type == 'implement_bulkhead':
isolation_level = parameters.get('isolation_level', 'agent')
max_concurrent = parameters.get('max_concurrent', 5)
# Simulate bulkhead pattern
impact_metrics = {
'action': 'bulkhead_isolation',
'isolation_level': isolation_level,
'max_concurrent': max_concurrent,
'fault_tolerance': 'improved',
'implementation': 'simulated'
}
elif action_type == 'activate_fallback':
fallback_mode = parameters.get('fallback_mode', 'cached')
duration = parameters.get('duration', 600)
# Simulate fallback activation
impact_metrics = {
'action': 'fallback_activation',
'fallback_mode': fallback_mode,
'duration': duration,
'service_continuity': 'maintained',
'implementation': 'simulated'
}
elif action_type == 'retry_with_backoff':
max_retries = parameters.get('max_retries', 3)
backoff_factor = parameters.get('backoff_factor', 2.0)
# Simulate retry mechanism
impact_metrics = {
'action': 'retry_mechanism',
'max_retries': max_retries,
'backoff_factor': backoff_factor,
'reliability_improvement': 'enhanced',
'implementation': 'simulated'
}
else:
# Default action
impact_metrics = {
'action': action_type,
'parameters': parameters,
'status': 'not_implemented',
'implementation': 'placeholder'
}
# Simulate execution delay
await asyncio.sleep(0.1)
return impact_metrics
async def get_healing_status(self) -> Dict[str, Any]:
"""Get comprehensive healing system status"""
current_time = datetime.now(timezone.utc)
uptime = (current_time - self.system_started).total_seconds()
# Calculate healing statistics
total_actions = len(self.healing_history)
successful_actions = sum(1 for action in self.healing_history if action.success)
success_rate = successful_actions / total_actions if total_actions > 0 else 0.0
# Recent healing activity
recent_actions = [
action for action in self.healing_history
if (current_time - action.timestamp).seconds < 3600 # Last hour
]
# Healing action types distribution
action_types = defaultdict(int)
healing_levels = defaultdict(int)
error_patterns = defaultdict(int)
for action in self.healing_history:
action_types[action.action_type] += 1
healing_levels[action.healing_level.value] += 1
error_patterns[action.error_pattern.value] += 1
# System health score
health_factors = {
'healing_success_rate': success_rate * 30,
'recent_activity': min(25, len(recent_actions) * 5), # Up to 25 points
'proactive_actions': sum(1 for action in self.healing_history
if action.healing_level == HealingLevel.PREVENTIVE) * 3,
'system_stability': 20 if total_actions < 10 else max(0, 20 - total_actions) # Fewer actions = more stable
}
system_health_score = min(100, sum(health_factors.values()))
return {
'timestamp': current_time.isoformat(),
'system_uptime_seconds': uptime,
'monitoring_active': self.monitoring_active,
'learning_enabled': self.learning_enabled,
'healing_statistics': {
'total_actions': total_actions,
'successful_actions': successful_actions,
'success_rate': success_rate,
'recent_actions_count': len(recent_actions)
},
'action_distribution': {
'action_types': dict(action_types),
'healing_levels': dict(healing_levels),
'error_patterns': dict(error_patterns)
},
'system_health_score': system_health_score,
'health_factors': health_factors,
'capabilities': {
'error_prediction': True,
'autonomous_healing': True,
'proactive_prevention': True,
'learning_algorithms': True,
'real_time_monitoring': True
},
'prediction_models_active': len(self.prediction_models),
'healing_strategies_available': sum(len(strategies) for strategies in self.healing_strategies.values())
}
# Global healing system instance
autonomous_healing_system = AutonomousSelfHealingSystem()
