🔄 Self-Healing Logic¶

Overview¶

The self-healing system is designed to automatically detect and recover from various types of failures in the Kubernetes cluster. It operates continuously, monitoring the cluster state and taking proactive actions to maintain system health.

Core Principles¶

1. Proactive Monitoring¶

Continuous Health Checks: Regular monitoring of all cluster components
Early Detection: Identify issues before they impact users
Predictive Analysis: Use metrics to predict potential failures

2. Automated Recovery¶

Immediate Response: Quick recovery actions for critical failures
Gradual Escalation: Progressive recovery strategies
Fallback Mechanisms: Multiple recovery options for complex failures

3. Minimal Disruption¶

Rolling Updates: Zero-downtime deployments
Graceful Degradation: Maintain service with reduced functionality
User Experience: Prioritize user-facing service availability

Architecture¶

graph TB
    subgraph "Monitoring Layer"
        HC[Health Checks]
        MET[Metrics Collection]
        LOG[Log Analysis]
    end

    subgraph "Analysis Layer"
        DET[Failure Detection]
        PRI[Priority Assessment]
        PLAN[Recovery Planning]
    end

    subgraph "Action Layer"
        EXE[Action Execution]
        VER[Verification]
        NOT[Notifications]
    end

    HC --> DET
    MET --> DET
    LOG --> DET
    DET --> PRI
    PRI --> PLAN
    PLAN --> EXE
    EXE --> VER
    VER --> NOT

Health Check Types¶

1. Node Health Checks¶

def check_node_health(self):
    """Monitor node status and health"""
    nodes = self.k8s_client.list_node()

    for node in nodes.items:
        # Check node conditions
        for condition in node.status.conditions:
            if condition.type == "Ready" and condition.status == "False":
                self.handle_node_failure(node)

            if condition.type == "MemoryPressure" and condition.status == "True":
                self.handle_memory_pressure(node)

            if condition.type == "DiskPressure" and condition.status == "True":
                self.handle_disk_pressure(node)

2. Pod Health Checks¶

def check_pod_health(self):
    """Monitor pod status and restart counts"""
    pods = self.k8s_client.list_pod_for_all_namespaces()

    for pod in pods.items:
        # Check pod phase
        if pod.status.phase == "Failed":
            self.handle_pod_failure(pod)

        # Check restart count
        for container in pod.status.container_statuses:
            if container.restart_count > self.pod_restart_threshold:
                self.handle_pod_restart_failure(pod, container)

        # Check readiness
        if not pod.status.ready:
            self.handle_pod_not_ready(pod)

3. Service Health Checks¶

def check_service_health(self):
    """Monitor service availability and endpoints"""
    services = self.k8s_client.list_service_for_all_namespaces()

    for service in services.items:
        # Check if service has endpoints
        endpoints = self.k8s_client.read_namespaced_endpoints(
            name=service.metadata.name,
            namespace=service.metadata.namespace
        )

        if not endpoints.subsets or not endpoints.subsets[0].addresses:
            self.handle_service_no_endpoints(service)

        # Check service connectivity
        if not self.test_service_connectivity(service):
            self.handle_service_connectivity_issue(service)

Failure Detection Logic¶

1. Pattern Recognition¶

def detect_failure_patterns(self):
    """Identify common failure patterns"""
    patterns = {
        'cascading_failure': self.detect_cascading_failure(),
        'resource_exhaustion': self.detect_resource_exhaustion(),
        'network_partition': self.detect_network_partition(),
        'storage_issues': self.detect_storage_issues()
    }

    for pattern_name, detected in patterns.items():
        if detected:
            self.handle_failure_pattern(pattern_name)

2. Threshold Monitoring¶

def monitor_thresholds(self):
    """Monitor resource and performance thresholds"""
    thresholds = {
        'cpu_usage': 80,  # percentage
        'memory_usage': 85,  # percentage
        'disk_usage': 90,  # percentage
        'response_time': 1000,  # milliseconds
        'error_rate': 5  # percentage
    }

    for metric, threshold in thresholds.items():
        current_value = self.get_current_metric(metric)
        if current_value > threshold:
            self.handle_threshold_exceeded(metric, current_value, threshold)

3. Anomaly Detection¶

def detect_anomalies(self):
    """Detect unusual patterns in metrics"""
    metrics = self.get_historical_metrics()

    for metric_name, values in metrics.items():
        # Calculate baseline
        baseline = self.calculate_baseline(values)

        # Detect anomalies
        current_value = values[-1]
        if self.is_anomaly(current_value, baseline):
            self.handle_anomaly(metric_name, current_value, baseline)

Recovery Actions¶

1. Node Recovery¶

def handle_node_failure(self, node):
    """Handle node failure recovery"""
    try:
        # Step 1: Cordon the node
        self.cordon_node(node.metadata.name)

        # Step 2: Drain the node
        self.drain_node(node.metadata.name)

        # Step 3: Check if node can be recovered
        if self.can_recover_node(node):
            self.recover_node(node)
        else:
            # Step 4: Replace the node
            self.replace_node(node)

        # Step 5: Verify recovery
        self.verify_node_recovery(node)

    except Exception as e:
        self.send_alert(f"Node recovery failed: {e}")
        self.escalate_node_failure(node)

2. Pod Recovery¶

def handle_pod_failure(self, pod):
    """Handle pod failure recovery"""
    try:
        # Step 1: Analyze failure reason
        reason = self.analyze_pod_failure(pod)

        # Step 2: Choose recovery strategy
        if reason == "OOMKilled":
            self.handle_oom_failure(pod)
        elif reason == "CrashLoopBackOff":
            self.handle_crash_loop(pod)
        elif reason == "ImagePullBackOff":
            self.handle_image_pull_failure(pod)
        else:
            self.handle_generic_pod_failure(pod)

        # Step 3: Verify recovery
        self.verify_pod_recovery(pod)

    except Exception as e:
        self.send_alert(f"Pod recovery failed: {e}")
        self.escalate_pod_failure(pod)

3. Service Recovery¶

def handle_service_failure(self, service):
    """Handle service failure recovery"""
    try:
        # Step 1: Check service dependencies
        dependencies = self.get_service_dependencies(service)

        # Step 2: Verify dependencies are healthy
        for dep in dependencies:
            if not self.is_service_healthy(dep):
                self.recover_service(dep)

        # Step 3: Restart service if needed
        if not self.is_service_healthy(service):
            self.restart_service(service)

        # Step 4: Verify service recovery
        self.verify_service_recovery(service)

    except Exception as e:
        self.send_alert(f"Service recovery failed: {e}")
        self.escalate_service_failure(service)

Recovery Strategies¶

1. Restart Strategy¶

def restart_strategy(self, resource):
    """Implement restart strategy for failed resources"""
    max_restarts = 3
    restart_delay = 30  # seconds

    for attempt in range(max_restarts):
        try:
            self.restart_resource(resource)
            time.sleep(restart_delay)

            if self.is_resource_healthy(resource):
                return True

        except Exception as e:
            self.log_error(f"Restart attempt {attempt + 1} failed: {e}")

    return False

2. Scaling Strategy¶

def scaling_strategy(self, deployment):
    """Implement scaling strategy for resource issues"""
    current_replicas = deployment.spec.replicas
    max_replicas = 10

    # Scale up if under load
    if self.is_under_load(deployment):
        new_replicas = min(current_replicas * 2, max_replicas)
        self.scale_deployment(deployment, new_replicas)

    # Scale down if over-provisioned
    elif self.is_over_provisioned(deployment):
        new_replicas = max(current_replicas // 2, 1)
        self.scale_deployment(deployment, new_replicas)

3. Failover Strategy¶

def failover_strategy(self, service):
    """Implement failover strategy for critical services"""
    # Check primary instance
    if not self.is_primary_healthy(service):
        # Switch to secondary
        self.switch_to_secondary(service)

        # Start recovery of primary
        self.recover_primary(service)

        # Switch back when primary is ready
        if self.is_primary_healthy(service):
            self.switch_to_primary(service)

Verification and Validation¶

1. Health Verification¶

def verify_recovery(self, resource):
    """Verify that recovery was successful"""
    verification_steps = [
        self.verify_resource_status,
        self.verify_resource_connectivity,
        self.verify_resource_performance,
        self.verify_resource_dependencies
    ]

    for step in verification_steps:
        if not step(resource):
            return False

    return True

2. Performance Validation¶

def validate_performance(self, resource):
    """Validate performance after recovery"""
    metrics = self.get_performance_metrics(resource)

    # Check response time
    if metrics['response_time'] > self.performance_thresholds['response_time']:
        return False

    # Check throughput
    if metrics['throughput'] < self.performance_thresholds['throughput']:
        return False

    # Check error rate
    if metrics['error_rate'] > self.performance_thresholds['error_rate']:
        return False

    return True

Notification System¶

1. Alert Levels¶

def send_alert(self, message, level="info"):
    """Send alerts based on severity level"""
    alert_configs = {
        "info": {
            "slack_channel": "#monitoring",
            "email_recipients": ["team@company.com"]
        },
        "warning": {
            "slack_channel": "#alerts",
            "email_recipients": ["oncall@company.com"]
        },
        "critical": {
            "slack_channel": "#incidents",
            "email_recipients": ["emergency@company.com"],
            "pagerduty": True
        }
    }

    config = alert_configs.get(level, alert_configs["info"])

    # Send Slack notification
    self.send_slack_notification(message, config["slack_channel"])

    # Send email notification
    self.send_email_notification(message, config["email_recipients"])

    # Send PagerDuty alert for critical issues
    if level == "critical" and config.get("pagerduty"):
        self.send_pagerduty_alert(message)

2. Escalation Procedures¶

def escalate_issue(self, issue, level=1):
    """Escalate issues that cannot be automatically resolved"""
    escalation_configs = {
        1: {
            "timeout": 300,  # 5 minutes
            "notify": ["oncall@company.com"]
        },
        2: {
            "timeout": 900,  # 15 minutes
            "notify": ["manager@company.com"]
        },
        3: {
            "timeout": 1800,  # 30 minutes
            "notify": ["director@company.com"]
        }
    }

    config = escalation_configs.get(level, escalation_configs[3])

    # Set escalation timer
    self.set_escalation_timer(issue, config["timeout"])

    # Send escalation notification
    self.send_escalation_notification(issue, config["notify"])

Configuration Management¶

1. Dynamic Configuration¶

def load_configuration(self):
    """Load configuration from ConfigMap"""
    config_map = self.k8s_client.read_namespaced_config_map(
        name="self-healing-config",
        namespace="self-healing"
    )

    self.config = {
        "health_check_interval": int(config_map.data.get("health_check_interval", "30")),
        "node_failure_threshold": int(config_map.data.get("node_failure_threshold", "3")),
        "pod_restart_threshold": int(config_map.data.get("pod_restart_threshold", "5")),
        "recovery_timeout": int(config_map.data.get("recovery_timeout", "300")),
        "notification_enabled": config_map.data.get("notification_enabled", "true").lower() == "true"
    }

2. Configuration Validation¶

def validate_configuration(self, config):
    """Validate configuration parameters"""
    validation_rules = {
        "health_check_interval": lambda x: 10 <= x <= 300,
        "node_failure_threshold": lambda x: 1 <= x <= 10,
        "pod_restart_threshold": lambda x: 1 <= x <= 20,
        "recovery_timeout": lambda x: 60 <= x <= 1800
    }

    for param, rule in validation_rules.items():
        if param in config and not rule(config[param]):
            raise ValueError(f"Invalid configuration for {param}: {config[param]}")

Performance Optimization¶

1. Efficient Monitoring¶

def optimize_monitoring(self):
    """Optimize monitoring for performance"""
    # Use efficient API calls
    self.use_field_selectors()
    self.use_label_selectors()

    # Implement caching
    self.cache_node_info()
    self.cache_pod_info()

    # Use watch API for real-time updates
    self.use_watch_api()

2. Resource Management¶

def manage_resources(self):
    """Manage controller resources efficiently"""
    # Limit concurrent operations
    self.limit_concurrent_operations(10)

    # Implement rate limiting
    self.implement_rate_limiting()

    # Use connection pooling
    self.use_connection_pooling()

Testing and Validation¶

1. Unit Tests¶

def test_health_check(self):
    """Test health check functionality"""
    # Mock Kubernetes API
    mock_k8s = MockKubernetesAPI()

    # Test node health check
    result = self.check_node_health(mock_k8s)
    assert result is not None

    # Test pod health check
    result = self.check_pod_health(mock_k8s)
    assert result is not None

2. Integration Tests¶

def test_recovery_workflow(self):
    """Test complete recovery workflow"""
    # Create test scenario
    scenario = self.create_test_scenario()

    # Execute recovery
    result = self.execute_recovery(scenario)

    # Verify results
    assert result.success
    assert result.recovery_time < 300  # 5 minutes
    assert result.verification_passed