# Top 10 E2E Use Cases for Kubernaut

**Document Version**: 1.0
**Date**: January 2025
**Status**: Implementation Ready
**Author**: Kubernaut Development Team

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## Executive Summary

This document defines the **Top 10 End-to-End Use Cases** for Kubernaut testing, designed to validate AI-driven Kubernetes remediation capabilities under realistic failure conditions. These use cases are prioritized based on business requirements, infrastructure capabilities (Kind cluster + oss-gpt:20b model), and development guidelines.

### Testing Environment Overview

- **Platform**: Kubernetes cluster (OCP) 4.18
- **AI Backend**: oss-gpt:20b model running at localhost:8080
- **Data Storage**: PostgreSQL with vector extensions
- **Runtime**: Kubernaut CLI via Go implementation
- **Chaos Engineering**: LitmusChaos framework for controlled failure injection
- **Test Framework**: Ginkgo/Gomega BDD framework (aligned with development guidelines)

---

## 🎯 **Top 10 E2E Use Cases**

### **1. AI-Driven Pod Resource Exhaustion Recovery**
**Priority**: 🔴 Critical | **Business Requirements**: BR-AI-001, BR-PA-008, BR-WF-001

#### **Scenario Description**
Memory pressure chaos triggers OOMKilled scenarios → AI analyzes resource patterns → Executes scaling decisions → Validates recovery metrics

#### **Technical Implementation**
```yaml
chaos_experiment: pod-memory-hog
target_workload: test-applications/memory-intensive-app
ai_decision_engine: oss-gpt:20b
expected_actions: [increase_resources, scale_deployment, cleanup_workload]
```

#### **Test Flow**
1. **Chaos Injection**: LitmusChaos injects memory pressure (80-95% memory utilization)
2. **Alert Generation**: Prometheus generates HighMemoryUsage alerts
3. **AI Analysis**: oss-gpt:20b model analyzes alert context and historical patterns
4. **Action Execution**: Kubernaut executes resource scaling or pod cleanup
5. **Recovery Validation**: Metrics confirm workload stability and performance restoration

#### **Success Criteria**
- **Recovery Rate**: 95% successful recovery within SLA
- **Response Time**: <30 seconds from alert to action execution
- **Accuracy**: AI correctly identifies root cause in 90% of scenarios
- **Stability**: No oscillation or resource thrashing post-recovery

#### **Business Value Validation**
- Validates BR-AI-001: Intelligent alert analysis with contextual awareness
- Demonstrates 60-80% improvement in incident response time
- Proves automated remediation reduces manual intervention by 70%

---

### **2. Multi-Node Failure with Workload Migration**
**Priority**: 🔴 Critical | **Business Requirements**: BR-AI-002, BR-SAFETY-001

#### **Scenario Description**
Node failure cascade triggers workload disruption → AI recognizes failure patterns → Orchestrates workload migration → Ensures zero data loss

#### **Technical Implementation**
```yaml
chaos_experiments: [node-cpu-hog, kubelet-service-kill]
target_infrastructure: worker-nodes-2,3
ai_pattern_recognition: historical_failure_analysis
expected_actions: [drain_node, cordon_node, migrate_workload]
```

#### **Test Flow**
1. **Multi-Node Chaos**: Simultaneous CPU exhaustion and kubelet service disruption
2. **Pattern Recognition**: AI identifies cascading failure pattern from historical data
3. **Workload Assessment**: Evaluate affected StatefulSets, Deployments, and PVCs
4. **Migration Orchestration**: Automated workload migration to healthy nodes
5. **Data Integrity Validation**: Verify zero data loss and service continuity

#### **Success Criteria**
- **Zero Data Loss**: 100% data integrity maintained during migration
- **Migration Time**: <5 minutes for complete workload migration
- **Service Availability**: <10 seconds total downtime per service
- **Pattern Recognition**: AI correctly identifies failure cascade in 85% of scenarios

#### **Business Value Validation**
- Validates BR-SAFETY-001: Fail-safe operation under extreme conditions
- Demonstrates 99.9%+ availability requirements under infrastructure failures
- Proves intelligent workload placement reduces service disruption

---

### **3. HolmesGPT Investigation Pipeline Under Load**
**Priority**: 🔴 Critical | **Business Requirements**: BR-AI-011, BR-AI-012, BR-AI-013

#### **Scenario Description**
Complex alert storm overwhelms monitoring → HolmesGPT conducts deep investigation → Context API enriches analysis → AI correlates evidence and provides root cause

#### **Technical Implementation**
```yaml
load_scenario: 50_concurrent_alerts_varied_complexity
integration_chain: Kubernaut → HolmesGPT → Context_API → oss-gpt:20b
investigation_types: [root_cause_analysis, evidence_correlation, time_series_analysis]
expected_outcomes: [intelligent_investigation, evidence_based_recommendations]
```

#### **Test Flow**
1. **Alert Storm Generation**: 50+ alerts across multiple namespaces and resource types
2. **HolmesGPT Dispatch**: Intelligent triage and investigation assignment
3. **Context Enrichment**: Dynamic context gathering via Context API
4. **Evidence Correlation**: Cross-reference logs, metrics, and historical patterns
5. **Root Cause Identification**: AI-powered analysis with supporting evidence

#### **Success Criteria**
- **Investigation Accuracy**: 80% correct root cause identification
- **Response Time**: <2 minutes per investigation under load
- **Evidence Quality**: 95% of investigations include relevant supporting evidence
- **Alert Correlation**: 90% noise reduction through intelligent grouping

#### **Business Value Validation**
- Validates BR-AI-011: Intelligent alert investigation with historical patterns
- Validates BR-AI-012: Root cause identification with supporting evidence
- Validates BR-AI-013: Alert correlation across time/resource boundaries
- Demonstrates 25% improvement in recommendation accuracy

---

### **4. Network Partition Recovery with Service Mesh**
**Priority**: 🟡 High | **Business Requirements**: BR-WF-002, BR-MONITOR-001

#### **Scenario Description**
Network split-brain condition disrupts service mesh → DNS resolution fails → AI diagnoses network topology issues → Executes network policy updates

#### **Technical Implementation**
```yaml
chaos_experiments: [pod-network-delay, dns-chaos]
service_mesh: istio/openshift-service-mesh
network_policies: dynamic_policy_updates
expected_actions: [restart_network, update_network_policy, reconfigure_ingress]
```

#### **Test Flow**
1. **Network Chaos**: Inject network latency and DNS resolution failures
2. **Service Discovery Impact**: Monitor service-to-service communication degradation
3. **AI Network Analysis**: Diagnose network topology and policy conflicts
4. **Policy Remediation**: Update NetworkPolicies and Istio configurations
5. **Service Restoration**: Validate restored service mesh connectivity

#### **Success Criteria**
- **Service Restoration**: <10 seconds for service mesh recovery
- **Network Policy Accuracy**: 90% correct policy updates
- **Zero Permanent Disruption**: No lasting service-to-service communication issues
- **Topology Understanding**: AI correctly maps network dependencies

#### **Business Value Validation**
- Validates complex multi-step workflow execution reliability
- Demonstrates intelligent network troubleshooting capabilities
- Proves automated network policy management

---

### **5. Storage Failure with Vector Database Persistence**
**Priority**: 🟡 High | **Business Requirements**: BR-VDB-001, BR-STORAGE-001

#### **Scenario Description**
ODF storage corruption threatens vector database → Pattern history at risk → AI executes storage recovery → Vector embeddings preserved

#### **Technical Implementation**
```yaml
chaos_experiment: disk-fill + custom_odf_storage_chaos
storage_backend: openshift_data_foundation
vector_database: postgresql_with_pgvector
expected_actions: [expand_pvc, backup_data, restore_vector_embeddings]
```

#### **Test Flow**
1. **Storage Chaos**: Fill disk space and corrupt ODF storage components
2. **Vector DB Impact Assessment**: Evaluate pattern history and embedding integrity
3. **Storage Recovery**: Automated PVC expansion and data migration
4. **Vector Embedding Restoration**: Rebuild corrupted embeddings from source data
5. **Pattern History Validation**: Verify AI decision-making capability preservation

#### **Success Criteria**
- **Data Loss Minimization**: <1% pattern data loss during recovery
- **Recovery Time**: <15 minutes for complete storage restoration
- **Embedding Integrity**: 99% vector embedding accuracy post-recovery
- **AI Capability Preservation**: No degradation in decision-making quality

#### **Business Value Validation**
- Validates vector database resilience and backup strategies
- Demonstrates intelligent storage management capabilities
- Proves pattern learning persistence under storage failures

---

### **6. AI Model Timeout Cascade with Fallback Logic**
**Priority**: 🟡 High | **Business Requirements**: BR-AI-003, BR-SAFETY-002

#### **Scenario Description**
oss-gpt:20b model overload causes timeout cascade → AI system detects degradation → Activates fallback decision engine → Maintains service continuity

#### **Technical Implementation**
```yaml
stress_test: 100_concurrent_model_requests_localhost:8080
fallback_engine: rule_based_decision_system
circuit_breaker: timeout_based_degradation_detection
expected_actions: [enable_degraded_mode, activate_circuit_breaker, queue_requests]
```

#### **Test Flow**
1. **Model Overload**: Saturate oss-gpt:20b with 100+ concurrent requests
2. **Timeout Detection**: Monitor model response times and failure rates
3. **Fallback Activation**: Switch to rule-based decision engine
4. **Request Queuing**: Queue non-critical requests for later processing
5. **Recovery Monitoring**: Detect model recovery and restore normal operation

#### **Success Criteria**
- **Zero Alert Drops**: 100% of critical alerts processed during degradation
- **Fallback Activation**: <5 seconds to activate degraded mode
- **Service Continuity**: Maintain basic remediation capabilities
- **Automatic Recovery**: Seamless transition back to AI mode when available

#### **Business Value Validation**
- Validates fail-safe operation under AI system overload
- Demonstrates resilient architecture design
- Proves continuity of critical operations during AI unavailability

---

### **7. Cross-Namespace Resource Contention Resolution**
**Priority**: 🟡 High | **Business Requirements**: BR-ORK-001, BR-RBAC-001

#### **Scenario Description**
Resource quota exhaustion affects multiple namespaces → Cross-tenant impact detected → AI optimizes resource distribution → RBAC-compliant resolution

#### **Technical Implementation**
```yaml
multi_tenant_chaos: resource_exhaustion_across_dev_staging_prod
rbac_validation: permission_aware_action_execution
resource_optimization: ai_driven_quota_redistribution
expected_actions: [adjust_quotas, migrate_workloads, scale_across_namespaces]
```

#### **Test Flow**
1. **Multi-Namespace Resource Exhaustion**: Simulate quota limits across environments
2. **Cross-Tenant Impact Analysis**: Assess resource contention effects
3. **RBAC-Aware Decision Making**: Ensure actions respect namespace permissions
4. **Resource Redistribution**: AI-optimized quota adjustments
5. **Tenant Isolation Validation**: Verify security boundaries maintained

#### **Success Criteria**
- **Zero Privilege Escalation**: 100% RBAC compliance during remediation
- **Balanced Resource Distribution**: Optimal resource allocation across namespaces
- **Tenant Isolation**: No cross-namespace security boundary violations
- **Resolution Time**: <2 minutes for resource contention resolution

#### **Business Value Validation**
- Validates multi-tenant security and resource management
- Demonstrates intelligent resource optimization
- Proves RBAC-aware automated operations

---

### **8. Prometheus Alertmanager Integration Storm**
**Priority**: 🔴 Critical | **Business Requirements**: BR-MONITOR-002, BR-WF-003

#### **Scenario Description**
Alert storm (1000+ alerts/min) overwhelms monitoring → AI correlation engine activates → Alerts grouped and prioritized → Batched remediation executed

#### **Technical Implementation**
```yaml
alert_generation: custom_alert_generator_realistic_scenarios
correlation_engine: ai_powered_alert_deduplication
batch_processing: grouped_remediation_workflows
expected_actions: [batch_processing, alert_correlation, priority_triage]
```

#### **Test Flow**
1. **Alert Storm Generation**: Generate realistic alert patterns at scale
2. **AI Correlation**: Group related alerts and identify root causes
3. **Priority Triage**: Rank alerts by business impact and urgency
4. **Batched Remediation**: Execute grouped actions efficiently
5. **Noise Reduction Validation**: Measure actual vs perceived incident count

#### **Success Criteria**
- **Noise Reduction**: 90% reduction in alert noise through correlation
- **True Issue Identification**: <100 actual issues identified from 1000+ alerts
- **Processing Efficiency**: Handle 1000+ alerts/min without degradation
- **Priority Accuracy**: 95% correct priority assignment for critical alerts

#### **Business Value Validation**
- Validates intelligent monitoring and alerting capabilities
- Demonstrates massive noise reduction in operational overhead
- Proves scalable alert processing under extreme load

---

### **9. Security Incident Response with Pod Quarantine**
**Priority**: 🔴 Critical | **Business Requirements**: BR-SECURITY-001, BR-AI-004

#### **Scenario Description**
Suspected security breach detected → AI threat assessment conducted → Automated quarantine procedures → Forensic data collection

#### **Technical Implementation**
```yaml
security_simulation: malicious_pod_behavior_patterns
ai_security_analysis: pattern_based_threat_detection_oss-gpt:20b
quarantine_procedures: automated_isolation_workflows
expected_actions: [quarantine_pod, isolate_workload, collect_forensics]
```

#### **Test Flow**
1. **Security Threat Simulation**: Inject malicious behavior patterns
2. **AI Threat Assessment**: Analyze patterns using oss-gpt:20b security models
3. **Automated Quarantine**: Isolate suspected compromised workloads
4. **Forensic Collection**: Gather logs, network data, and system state
5. **Lateral Movement Prevention**: Validate containment effectiveness

#### **Success Criteria**
- **Threat Isolation**: <30 seconds from detection to quarantine
- **Zero Lateral Movement**: 100% containment of simulated threats
- **Forensic Completeness**: Complete audit trail and evidence collection
- **False Positive Rate**: <5% false positive threat detection

#### **Business Value Validation**
- Validates automated security incident response
- Demonstrates AI-powered threat detection capabilities
- Proves rapid containment and forensic capabilities

---

### **10. End-to-End Disaster Recovery Validation**
**Priority**: 🟡 High | **Business Requirements**: BR-BACKUP-001, BR-WF-004

#### **Scenario Description**
Complete cluster failure simulation → Backup restoration procedures → State reconstruction → Service continuity validation

#### **Technical Implementation**
```yaml
disaster_simulation: controlled_cluster_failure_etcd_corruption
recovery_procedures: full_kubernaut_state_restoration_postgresql
state_reconstruction: vector_embeddings_pattern_history_rebuild
expected_actions: [restore_cluster_state, rebuild_vector_embeddings, resume_operations]
```

#### **Test Flow**
1. **Disaster Simulation**: Controlled cluster failure with ETCD corruption
2. **Backup Assessment**: Validate backup integrity and completeness
3. **State Restoration**: Restore Kubernaut state from PostgreSQL backups
4. **Vector Database Rebuild**: Reconstruct embeddings and pattern history
5. **Service Resumption**: Validate full operational capability restoration

#### **Success Criteria**
- **Recovery Time Objective (RTO)**: <30 minutes for full restoration
- **Recovery Point Objective (RPO)**: <5 minutes data loss maximum
- **Pattern History Preservation**: 99% of historical patterns restored
- **Operational Capability**: 100% of AI decision-making capability restored

#### **Business Value Validation**
- Validates disaster recovery and business continuity procedures
- Demonstrates backup strategy effectiveness
- Proves resilient architecture design for critical operations

---

## 📊 **Success Metrics & Business Value**

### **Primary Success Metrics**

| Metric Category | Target | Measurement Method |
|----------------|--------|-------------------|
| **AI Accuracy** | 90% correct decisions | Business outcome validation |
| **Response Time** | <30s average | End-to-end timing measurement |
| **Recovery Rate** | 95% successful recovery | Automated success validation |
| **Availability** | 99.9% uptime | Continuous monitoring |
| **Data Integrity** | Zero critical data loss | Comprehensive data validation |

### **Business Value Validation**

| Business Requirement | Success Criteria | Validation Method |
|---------------------|------------------|------------------|
| BR-AI-001 | 25% improvement in recommendation accuracy | A/B testing against baseline |
| BR-SAFETY-001 | Zero critical system failures during chaos | Continuous safety monitoring |
| BR-MONITOR-002 | 90% alert noise reduction | Alert correlation effectiveness |
| BR-VDB-001 | 40% cost reduction in embedding services | Resource utilization analysis |
| BR-WF-001 | 60-80% faster incident resolution | Time-to-resolution measurement |

---

## 🔄 **Integration with Existing Infrastructure**

### **Development Guidelines Compliance**
- **Code Reuse**: Leverages existing `test/integration/shared/test_factory.go`
- **Business Requirements**: Each use case maps to specific BR-XXX requirements
- **Test Framework**: Uses Ginkgo/Gomega BDD framework per guidelines
- **Error Handling**: Comprehensive error logging and validation
- **Mock Usage**: Extends existing mock patterns for consistency

### **Infrastructure Integration**
- **Kubernetes cluster Cluster**: Full integration with Kubernetes 4.18 features
- **oss-gpt:20b Model**: Direct integration with localhost:8080 endpoint
- **PostgreSQL**: Vector database persistence and pattern storage
- **LitmusChaos**: Chaos engineering framework integration
- **Prometheus/Grafana**: Monitoring and metrics collection

### **Testing Framework Integration**
- **StandardTestSuite**: Extends existing test infrastructure
- **Business Metrics Validation**: Automated BR requirement validation
- **Chaos Orchestration**: Integrated failure injection and recovery
- **Real-time Monitoring**: Live metrics and SLA validation

---

## 🚀 **Next Steps**

1. **Review and Approval**: Stakeholder review of use cases and success criteria
2. **Infrastructure Preparation**: Kind cluster and testing environment setup
3. **Implementation Planning**: Detailed implementation timeline and resource allocation
4. **Tool Development**: Custom testing tools and automation framework
5. **Execution and Validation**: Systematic use case implementation and validation

This document serves as the foundation for implementing comprehensive e2e testing that validates both technical capabilities and business value delivery for Kubernaut's AI-driven Kubernetes remediation system.
