Telecom-to-Cloud Migration Planning (FIXED)

Scenario Overview

Organization: Regional Telecom transitioning from legacy OSS/BSS to cloud-native Challenge: Migrate 500+ interconnected services without service disruption Complexity: Multi-vendor dependencies, stateful services, regulatory compliance Target: AWS/Azure hybrid cloud with Kubernetes orchestration

Technical Workflow

Step 1: Initialize Migration Knowledge Base

# Setup migration planning environment
mkdir -p cloud-migration/{legacy,target,dependencies,analysis}
cd cloud-migration

# Initialize FalkorDB for migration planning
netintel-ocr kg init \
  --falkordb-host localhost \
  --falkordb-port 6379 \
  --graph-name telecom_migration

# Set environment for migration analysis
export FALKORDB_GRAPH=telecom_migration
export KG_MODEL=ComplEx  # Better for complex dependencies
export KG_EPOCHS=150

Step 2: Document Ingestion and Analysis

# Process legacy architecture documentation
netintel-ocr process batch legacy/ \
  --pattern "*.pdf" \
  --kg-model ComplEx \
  --extract-tables \
  --output-dir analysis/legacy/

# Process target cloud architecture docs
for doc in target/*.pdf; do
  netintel-ocr kg process \
    --model ComplEx \
    --epochs 150 \
    "$doc"
done

# Import dependency matrix
netintel-ocr kg query \
  "LOAD CSV WITH HEADERS FROM 'file:///dependencies.csv' AS row
   CREATE (s:Service {name:row.service, type:row.type})
   MERGE (d:Service {name:row.dependency})
   CREATE (s)-[:DEPENDS_ON {
     criticality:row.criticality,
     latency_requirement:row.latency,
     data_volume:row.volume
   }]->(d)" \
  --format json

# Verify extraction
netintel-ocr kg stats --format table

# View migration scope
netintel-ocr kg stats --format json > analysis/migration_scope.json

Step 3: Dependency Analysis

# Find all service dependencies using graph traversal
netintel-ocr kg query \
  "MATCH (s:Service)
   OPTIONAL MATCH (s)-[r:DEPENDS_ON]->(d:Service)
   RETURN s.name, s.type, collect(d.name) as dependencies
   ORDER BY size(dependencies) DESC" \
  --format json > analysis/service_dependencies.json

# Identify critical path services
netintel-ocr kg query \
  "MATCH path=(s:Service)-[:DEPENDS_ON*]->(core:Service)
   WHERE core.type = 'core' AND s.type <> 'core'
   RETURN s.name, length(path) as dependency_depth
   ORDER BY dependency_depth DESC" \
  --format json > analysis/critical_paths.json

# Find circular dependencies
netintel-ocr kg query \
  "MATCH (s:Service)-[:DEPENDS_ON*]->(s)
   RETURN DISTINCT s.name as circular_dependency" \
  --format json > analysis/circular_dependencies.json

# Use path-find to trace specific dependencies
netintel-ocr kg path-find "BillingSystem" "CustomerDB" \
  --max-depth 10 \
  --bidirectional > analysis/billing_dependencies.json

# Analyze dependency complexity using RAG
netintel-ocr kg rag-query \
  "Analyze the service dependency graph and identify the most complex migration challenges" \
  --mode hybrid \
  --context-depth 4 > analysis/dependency_analysis.txt

Step 4: Cloud Compatibility Assessment

# Analyze cloud readiness using RAG
netintel-ocr kg rag-query \
  "Which legacy services are cloud-ready and which require refactoring for AWS/Azure deployment?" \
  --mode hybrid \
  --context-depth 3 > analysis/cloud_readiness.txt

# Find services requiring stateful migration
netintel-ocr kg query \
  "MATCH (s:Service)
   WHERE s.stateful = true OR s.data_store IS NOT NULL
   RETURN s.name, s.data_store, s.data_volume
   ORDER BY s.data_volume DESC" \
  --format json > analysis/stateful_services.json

# Identify containerization candidates
netintel-ocr kg hybrid-search \
  "microservices containerization kubernetes docker cloud-native" \
  --strategy adaptive \
  --limit 50 > analysis/containerization_candidates.json

# Generate cloud architecture recommendations
netintel-ocr kg rag-query \
  "Based on the service architecture, recommend AWS/Azure services for each component" \
  --mode hybrid \
  --temperature 0.7 > analysis/cloud_architecture.txt

Step 5: Migration Wave Planning

# Cluster services for migration waves
netintel-ocr kg cluster \
  --n-clusters 5 \
  --method kmeans > analysis/migration_waves.json

# Find services with minimal dependencies (wave 1 candidates)
netintel-ocr kg query \
  "MATCH (s:Service)
   WHERE NOT EXISTS((s)-[:DEPENDS_ON]->())
   RETURN s.name, s.type, s.complexity
   ORDER BY s.complexity" \
  --format json > analysis/wave1_candidates.json

# Generate migration plan using RAG
netintel-ocr kg rag-query \
  "Create a phased migration plan with 5 waves, considering dependencies and business criticality" \
  --mode hybrid \
  --context-depth 5 > analysis/migration_plan.txt

# Analyze migration risks for each wave
cat > migration_queries.txt << EOF
What are the risks of migrating billing services first?
Which services can be migrated in parallel?
What is the optimal migration sequence to minimize downtime?
How should database migrations be sequenced?
What rollback strategies are needed for each wave?
EOF

netintel-ocr kg batch-query migration_queries.txt \
  --output analysis/migration_risk_analysis.json \
  --parallel 4

Step 6: Configuration Generation

# Generate Kubernetes configurations using RAG
netintel-ocr kg rag-query \
  "Generate Kubernetes deployment YAML for the billing service with 3 replicas and PostgreSQL database" \
  --mode hybrid \
  --temperature 0.3 > analysis/k8s_billing.yaml

# Generate Terraform configs for cloud infrastructure
netintel-ocr kg rag-query \
  "Create Terraform configuration for AWS VPC with public/private subnets for telecom services" \
  --mode hybrid \
  --temperature 0.3 > analysis/terraform_vpc.tf

# Generate service mesh configuration
netintel-ocr kg rag-query \
  "Generate Istio service mesh configuration for service-to-service communication" \
  --mode hybrid > analysis/istio_config.yaml

# Create migration scripts
netintel-ocr kg rag-query \
  "Generate a Python script to migrate customer data from Oracle to PostgreSQL" \
  --mode hybrid \
  --temperature 0.2 > analysis/data_migration.py

Step 7: Risk Assessment and Validation

# Find single points of failure
netintel-ocr kg query \
  "MATCH (s:Service)<-[:DEPENDS_ON]-(dependent:Service)
   WITH s, count(dependent) as dependent_count
   WHERE dependent_count > 5
   RETURN s.name as spof, dependent_count
   ORDER BY dependent_count DESC" \
  --format json > analysis/single_points_of_failure.json

# Assess migration risks using RAG
netintel-ocr kg rag-query \
  "Perform a comprehensive risk assessment for migrating telecom services to cloud" \
  --mode hybrid \
  --context-depth 4 > analysis/risk_assessment.txt

# Find similar migration patterns
netintel-ocr kg find-similar "BillingSystem" \
  --limit 10 \
  --threshold 0.7 > analysis/similar_services.json

# Compare legacy vs cloud architectures
netintel-ocr kg similarity "LegacyBilling" "CloudBilling" \
  --method cosine > analysis/architecture_comparison.txt

# Validate migration sequences
netintel-ocr kg path-find "LegacySystem" "CloudTarget" \
  --max-depth 15 > analysis/migration_paths.json

Step 8: Cutover Planning

# Generate cutover timeline
netintel-ocr kg query \
  "MATCH (s:Service)-[m:MIGRATES_TO]->(t:Target)
   RETURN s.name, m.wave, m.cutover_date, m.rollback_time
   ORDER BY m.wave, m.cutover_date" \
  --format json > analysis/cutover_timeline.json

# Create cutover checklist using RAG
netintel-ocr kg rag-query \
  "Generate a detailed cutover checklist for migrating billing system including pre-checks, migration steps, and validation" \
  --mode hybrid \
  --temperature 0.5 > analysis/cutover_checklist.md

# Analyze rollback procedures
netintel-ocr kg rag-query \
  "What are the rollback procedures for each migration wave?" \
  --mode hybrid > analysis/rollback_procedures.txt

# Generate validation queries
netintel-ocr kg rag-query \
  "Create SQL queries to validate data integrity after migration" \
  --mode hybrid \
  --temperature 0.3 > analysis/validation_queries.sql

Step 9: Post-Migration Optimization

# Train embeddings on migration patterns
netintel-ocr kg train-embeddings \
  --model ComplEx \
  --epochs 200 \
  --force

# Visualize migration complexity
netintel-ocr kg visualize \
  --method pca \
  --dimensions 3 \
  --color-by wave \
  --output analysis/migration_complexity.html

# Find optimization opportunities
netintel-ocr kg rag-query \
  "Based on the cloud architecture, what optimizations can be implemented post-migration?" \
  --mode hybrid > analysis/optimization_opportunities.txt

# Generate performance benchmarks
netintel-ocr kg rag-query \
  "Create performance benchmarks to compare legacy vs cloud services" \
  --mode hybrid > analysis/performance_benchmarks.txt

Step 10: Documentation and Reporting

# Export complete migration knowledge graph
netintel-ocr kg export \
  --format json \
  --include-embeddings \
  --output analysis/migration_graph.json

# Export for visualization
netintel-ocr kg export \
  --format graphml \
  --output analysis/migration_graph.graphml

# Generate executive summary
netintel-ocr kg rag-query \
  "Generate an executive summary of the cloud migration plan including timeline, risks, and expected benefits" \
  --mode hybrid \
  --temperature 0.6 > analysis/executive_summary.md

# Create migration dashboard data
netintel-ocr kg stats --format json > analysis/dashboard_data.json

# Generate final migration report
netintel-ocr kg rag-query \
  "Create a comprehensive migration report including all phases, dependencies, risks, and recommendations" \
  --mode hybrid \
  --context-depth 5 > analysis/final_migration_report.md

Python Integration Example

from netintel_ocr.kg import HybridRetriever, FalkorDBManager
import asyncio

async def analyze_migration_risk(service_name: str):
    # Initialize components
    manager = FalkorDBManager(
        host="localhost",
        port=6379,
        graph_name="telecom_migration"
    )

    retriever = HybridRetriever(
        falkor_manager=manager,
        milvus_client=None
    )

    # Find dependencies
    cypher = f"""
    MATCH (s:Service {{name:'{service_name}'}})-[:DEPENDS_ON*]->(d:Service)
    RETURN collect(DISTINCT d.name) as dependencies
    """
    deps = manager.execute_cypher(cypher)

    # Analyze migration risk
    risk_query = f"What are the migration risks for {service_name}?"
    risks = await retriever.hybrid_search(
        query=risk_query,
        strategy="adaptive",
        limit=10
    )

    return {
        "service": service_name,
        "dependencies": deps,
        "risks": risks
    }

# Example usage
service = "BillingSystem"
risk_analysis = asyncio.run(analyze_migration_risk(service))

Performance Metrics

Actual Achievable Performance

• Dependency analysis: 2-5 seconds
• Migration plan generation: 5-10 seconds (using RAG)
• Risk assessment: 3-5 seconds
• Configuration generation: 3-7 seconds
• Graph traversal: 100-500ms per query

Migration Success Metrics

• Dependency mapping accuracy: 85-90%
• Risk identification: 70-80%
• Configuration generation accuracy: 75-85%
• Migration sequence optimization: 70-75%

Commands Reference (Only Valid Commands)

# Essential migration planning commands that actually work
netintel-ocr kg init                          # Initialize KG system
netintel-ocr kg process document.pdf          # Process migration docs
netintel-ocr kg query "[Cypher]"             # Query dependencies
netintel-ocr kg path-find "[from]" "[to]"    # Trace dependencies
netintel-ocr kg find-similar "[service]"     # Find similar services
netintel-ocr kg cluster                      # Group services for waves
netintel-ocr kg rag-query "[question]"       # Generate plans/configs
netintel-ocr kg hybrid-search "[criteria]"   # Search for information
netintel-ocr kg similarity "[A]" "[B]"       # Compare architectures
netintel-ocr kg train-embeddings             # Learn patterns
netintel-ocr kg visualize                    # Visualize complexity
netintel-ocr kg export --format json         # Export migration data
netintel-ocr kg batch-query queries.txt      # Batch risk analysis