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Service Mapping Overview

Service Mapping in NopeSight automatically discovers and maps the relationships between business services and their underlying IT infrastructure. Using AI-powered discovery and real-time dependency tracking, it provides complete visibility into how your services are constructed and how they interact with each other.

What is Service Mapping?​

Service Mapping creates a comprehensive, real-time map of your business services by:

  • 🔍 Discovering all components that make up a service
  • 🔗 Mapping dependencies between components
  • 📊 Visualizing service architecture and data flows
  • 🎯 Tracking service health and performance
  • 🤖 Analyzing impact of changes and failures
  • 📈 Optimizing service design and resource allocation

Architecture​

Key Concepts​

Business Services​

Business Service Example:
  name: "Online Shopping"
  description: "E-commerce platform for customer purchases"
  criticality: "Critical"
  owner: "E-commerce Team"
  
  components:
    entry_points:
      - web_frontend
      - mobile_api
      - cdn_service
      
    application_layer:
      - shopping_cart_service
      - product_catalog_service
      - payment_service
      - order_service
      
    middleware:
      - message_queue
      - cache_cluster
      - api_gateway
      
    data_layer:
      - customer_database
      - product_database
      - order_database
      
    infrastructure:
      - load_balancers
      - web_servers
      - app_servers
      - database_servers

Service Dependencies​

Service Hierarchy​

Service Hierarchy:
  Business Service:
    - Customer-facing service
    - Business value focused
    - SLA-driven
    
    Technical Service:
      - Supporting infrastructure
      - Shared components
      - Technical capabilities
      
      Application Component:
        - Individual applications
        - Microservices
        - Databases
        
        Infrastructure:
          - Servers
          - Network devices
          - Storage systems

Discovery Methods​

Automated Discovery​

Traffic-Based Discovery

class TrafficBasedDiscovery:
    def discover_dependencies(self, service_entry_point):
        """Discover service dependencies through traffic analysis"""
        
        dependencies = []
        
        # Monitor network traffic
        traffic_flows = self.capture_traffic(
            source=service_entry_point,
            duration='5m',
            include_dns=True
        )
        
        # Analyze flows
        for flow in traffic_flows:
            if self.is_service_communication(flow):
                dependency = {
                    'source': flow.source,
                    'destination': flow.destination,
                    'protocol': flow.protocol,
                    'port': flow.port,
                    'frequency': flow.packet_count,
                    'data_volume': flow.bytes_transferred,
                    'latency': flow.avg_latency
                }
                
                # Identify service type
                dependency['service_type'] = self.identify_service_type(
                    port=flow.port,
                    protocol=flow.protocol,
                    payload_signature=flow.payload_sample
                )
                
                dependencies.append(dependency)
        
        # Build dependency graph
        return self.build_dependency_graph(dependencies)

Configuration-Based Discovery

Configuration Discovery:
  Application Configs:
    - Connection strings
    - API endpoints
    - Service URLs
    - Queue names
    
  Infrastructure Configs:
    - Load balancer rules
    - Firewall policies
    - DNS records
    - Routing tables
    
  Deployment Configs:
    - Docker compose files
    - Kubernetes manifests
    - Terraform scripts
    - Ansible playbooks

AI-Powered Mapping​

class AIServiceMapper:
    def __init__(self):
        self.pattern_recognizer = ServicePatternRecognizer()
        self.anomaly_detector = DependencyAnomalyDetector()
        self.predictor = ServiceImpactPredictor()
    
    def map_service(self, entry_points):
        """Use AI to map complete service"""
        
        # Initial discovery
        components = self.discover_components(entry_points)
        
        # Pattern recognition
        service_patterns = self.pattern_recognizer.identify_patterns(
            components=components,
            known_patterns=self.get_known_patterns()
        )
        
        # Validate dependencies
        validated_deps = []
        for dep in components['dependencies']:
            confidence = self.calculate_dependency_confidence(dep)
            
            if confidence > 0.8:
                validated_deps.append(dep)
            elif confidence > 0.5:
                # Manual validation required
                validated_deps.append({**dep, 'requires_validation': True})
        
        # Predict missing components
        predicted_components = self.predictor.predict_missing_components(
            known_components=components,
            service_type=service_patterns['type']
        )
        
        return self.build_service_map(
            components=components,
            validated_dependencies=validated_deps,
            predictions=predicted_components
        )

Service Modeling​

Service Definition​

service_definition:
  metadata:
    name: "Payment Processing Service"
    version: "2.1.0"
    description: "Handles all payment transactions"
    business_criticality: "Critical"
    compliance_requirements: ["PCI-DSS", "SOX"]
    
  ownership:
    business_owner: "Finance Department"
    technical_owner: "Payment Team"
    on_call_team: "payment-oncall"
    
  sla:
    availability: 99.99%
    response_time_p99: 500ms
    throughput: "10000 tps"
    
  components:
    entry_points:
      - name: "Payment API"
        type: "REST API"
        endpoint: "https://api.payment.company.com"
        
    services:
      - name: "Payment Gateway"
        type: "Application"
        technology: "Java Spring Boot"
        instances: 5
        
      - name: "Fraud Detection"
        type: "ML Service"
        technology: "Python"
        instances: 3
        
    databases:
      - name: "Transaction DB"
        type: "PostgreSQL"
        role: "Primary"
        size: "2TB"
        
    external_dependencies:
      - name: "Bank API"
        type: "External Service"
        criticality: "High"

Dynamic Service Discovery​

class DynamicServiceMapper {
    async mapService(entryPoint) {
        // Phase 1: Entry point analysis
        const entryPointAnalysis = await this.analyzeEntryPoint(entryPoint);
        
        // Phase 2: Follow connections
        const connections = await this.traceConnections(entryPointAnalysis);
        
        // Phase 3: Component identification
        const components = await this.identifyComponents(connections);
        
        // Phase 4: Dependency mapping
        const dependencies = await this.mapDependencies(components);
        
        // Phase 5: Service boundary detection
        const serviceBoundaries = await this.detectServiceBoundaries(
            components,
            dependencies
        );
        
        // Phase 6: Build service model
        return this.buildServiceModel({
            entryPoint,
            components,
            dependencies,
            boundaries: serviceBoundaries,
            metadata: await this.gatherMetadata(components)
        });
    }
    
    async traceConnections(entryPoint) {
        const connections = new Map();
        const queue = [entryPoint];
        const visited = new Set();
        
        while (queue.length > 0) {
            const current = queue.shift();
            if (visited.has(current.id)) continue;
            
            visited.add(current.id);
            
            // Get all connections from this component
            const outgoing = await this.getOutgoingConnections(current);
            const incoming = await this.getIncomingConnections(current);
            
            connections.set(current.id, {
                component: current,
                outgoing,
                incoming
            });
            
            // Add unvisited connections to queue
            [...outgoing, ...incoming].forEach(conn => {
                if (!visited.has(conn.target.id)) {
                    queue.push(conn.target);
                }
            });
        }
        
        return connections;
    }
}

Visualization​

Interactive Service Maps​

Visualization Features:
  Layout Algorithms:
    - Hierarchical: Business service tree
    - Force-directed: Organic relationships
    - Circular: Service ecosystems
    - Matrix: Dependency grid
    
  Visual Elements:
    Components:
      - Shape: By component type
      - Color: By health status
      - Size: By importance/traffic
      - Icons: Technology indicators
      
    Connections:
      - Thickness: Data volume
      - Style: Connection type
      - Animation: Real-time flow
      - Color: Latency/health
      
  Interactivity:
    - Zoom and pan
    - Component details on hover
    - Click to drill down
    - Filter by various criteria
    - Real-time updates

Service Health Visualization​

// Real-time service health monitoring
class ServiceHealthVisualizer {
    constructor(serviceId) {
        this.serviceId = serviceId;
        this.healthMetrics = new HealthMetricsCollector();
        this.visualization = new D3ServiceMap();
    }
    
    updateHealthStatus() {
        // Collect health metrics
        const componentHealth = this.healthMetrics.getComponentHealth(this.serviceId);
        
        // Update visualization
        componentHealth.forEach(component => {
            const node = this.visualization.getNode(component.id);
            
            // Update node appearance based on health
            node.update({
                color: this.getHealthColor(component.health),
                pulseAnimation: component.hasIncident,
                metrics: {
                    cpu: component.cpu,
                    memory: component.memory,
                    errorRate: component.errorRate,
                    responseTime: component.responseTime
                }
            });
            
            // Update connection health
            component.connections.forEach(conn => {
                const edge = this.visualization.getEdge(conn.id);
                edge.update({
                    color: this.getLatencyColor(conn.latency),
                    thickness: this.getTrafficThickness(conn.traffic),
                    animation: conn.isActive ? 'flow' : 'none'
                });
            });
        });
        
        // Update service-level indicators
        this.updateServiceIndicators(componentHealth);
    }
}

Impact Analysis​

Change Impact Prediction​

class ChangeImpactAnalyzer:
    def analyze_change_impact(self, component_id, change_type):
        """Analyze impact of proposed change"""
        
        service_map = self.get_service_map(component_id)
        
        impact_analysis = {
            'change_summary': {
                'component': component_id,
                'type': change_type,
                'scheduled_time': self.get_change_window()
            },
            'direct_impact': [],
            'cascading_impact': [],
            'business_impact': {},
            'risk_assessment': {}
        }
        
        # Direct dependencies
        direct_deps = service_map.get_direct_dependencies(component_id)
        for dep in direct_deps:
            impact = self.calculate_direct_impact(dep, change_type)
            impact_analysis['direct_impact'].append(impact)
        
        # Cascading effects
        cascading = self.trace_cascading_impact(
            component_id,
            change_type,
            max_depth=5
        )
        impact_analysis['cascading_impact'] = cascading
        
        # Business impact
        affected_services = self.get_affected_business_services(
            component_id,
            cascading
        )
        
        for service in affected_services:
            business_impact = {
                'service': service.name,
                'criticality': service.criticality,
                'affected_users': self.estimate_affected_users(service),
                'revenue_impact': self.estimate_revenue_impact(service),
                'sla_risk': self.assess_sla_risk(service, change_type)
            }
            impact_analysis['business_impact'][service.id] = business_impact
        
        # Risk assessment
        impact_analysis['risk_assessment'] = self.assess_overall_risk(
            impact_analysis
        )
        
        return impact_analysis

Failure Impact Simulation​

Failure Scenarios:
  Component Failure:
    - Immediate service degradation
    - Cascading failures
    - Failover activation
    - Performance impact
    
  Network Failure:
    - Connectivity loss
    - Increased latency
    - Route changes
    - Partition scenarios
    
  Dependency Failure:
    - External service outage
    - Database unavailability
    - Cache failure
    - Queue backup
    
  Simulation Results:
    - Affected services list
    - User impact estimation
    - Recovery time objective
    - Mitigation recommendations

Service Optimization​

Dependency Optimization​

class ServiceOptimizer:
    def analyze_service_architecture(self, service_id):
        """Analyze and recommend optimizations"""
        
        service = self.get_service(service_id)
        optimizations = []
        
        # Analyze dependencies
        dep_analysis = self.analyze_dependencies(service)
        
        # Check for anti-patterns
        if self.has_circular_dependencies(dep_analysis):
            optimizations.append({
                'type': 'circular_dependency',
                'severity': 'high',
                'components': self.get_circular_dependencies(dep_analysis),
                'recommendation': 'Refactor to remove circular dependencies',
                'impact': 'Improved stability and maintainability'
            })
        
        # Check for bottlenecks
        bottlenecks = self.identify_bottlenecks(service)
        for bottleneck in bottlenecks:
            optimizations.append({
                'type': 'performance_bottleneck',
                'severity': 'medium',
                'component': bottleneck.component,
                'metrics': bottleneck.metrics,
                'recommendation': bottleneck.suggestion,
                'estimated_improvement': bottleneck.improvement_estimate
            })
        
        # Check for single points of failure
        spofs = self.find_single_points_of_failure(service)
        for spof in spofs:
            optimizations.append({
                'type': 'single_point_of_failure',
                'severity': 'critical',
                'component': spof.component,
                'recommendation': f'Add redundancy for {spof.component}',
                'implementation': spof.redundancy_options
            })
        
        # Cost optimization
        cost_opts = self.analyze_cost_optimization(service)
        optimizations.extend(cost_opts)
        
        return self.prioritize_optimizations(optimizations)

Performance Analysis​

class ServicePerformanceAnalyzer {
    analyzeServicePerformance(serviceId) {
        const service = this.getService(serviceId);
        const analysis = {
            overall_health: this.calculateOverallHealth(service),
            bottlenecks: [],
            optimization_opportunities: [],
            capacity_planning: {}
        };
        
        // Analyze each component
        service.components.forEach(component => {
            const metrics = this.getComponentMetrics(component.id);
            
            // Check for performance issues
            if (metrics.responseTime.p99 > component.sla.responseTime) {
                analysis.bottlenecks.push({
                    component: component.name,
                    issue: 'High response time',
                    current: metrics.responseTime.p99,
                    target: component.sla.responseTime,
                    impact: this.calculateImpact(component, service)
                });
            }
            
            // Check resource utilization
            if (metrics.cpu.average > 80) {
                analysis.optimization_opportunities.push({
                    component: component.name,
                    type: 'scaling',
                    current_utilization: metrics.cpu.average,
                    recommendation: 'Scale out or optimize code',
                    estimated_cost: this.estimateScalingCost(component)
                });
            }
        });
        
        // Capacity planning
        analysis.capacity_planning = this.projectCapacityNeeds(service);
        
        return analysis;
    }
}

Integration Capabilities​

CI/CD Integration​

CI/CD Integration:
  Pre-deployment Validation:
    - Service dependency check
    - Impact analysis
    - Risk assessment
    - Approval routing
    
  Deployment Tracking:
    - Real-time map updates
    - Version tracking
    - Rollback mapping
    - Success validation
    
  Post-deployment:
    - Service validation
    - Performance comparison
    - Dependency verification
    - Alert configuration

Monitoring Integration​

class MonitoringIntegration:
    def integrate_with_monitoring(self, service_map):
        """Integrate service map with monitoring systems"""
        
        # Create monitoring topology
        monitoring_config = {
            'service': service_map.service_id,
            'components': []
        }
        
        for component in service_map.components:
            component_monitoring = {
                'id': component.id,
                'name': component.name,
                'type': component.type,
                'monitors': self.create_monitors(component),
                'alerts': self.create_alerts(component),
                'dashboards': self.create_dashboards(component)
            }
            
            monitoring_config['components'].append(component_monitoring)
        
        # Create service-level monitoring
        monitoring_config['service_monitors'] = self.create_service_monitors(
            service_map
        )
        
        # Deploy configuration
        self.deploy_monitoring_config(monitoring_config)
        
        return monitoring_config

Best Practices​

1. Discovery Strategy​

  • ✅ Start with critical business services
  • ✅ Use multiple discovery methods
  • ✅ Validate automated discoveries
  • ✅ Regular re-discovery cycles

2. Mapping Accuracy​

  • ✅ Verify component boundaries
  • ✅ Validate dependencies
  • ✅ Include external services
  • ✅ Document assumptions

3. Maintenance​

  • ✅ Keep maps up-to-date
  • ✅ Track service changes
  • ✅ Regular validation
  • ✅ Version control maps

4. Usage​

  • ✅ Share with all stakeholders
  • ✅ Use for change planning
  • ✅ Enable for incident response
  • ✅ Drive optimization efforts

Service Catalog​

Service Documentation​

Service Catalog Entry:
  service_id: "SVC-001"
  name: "Customer Portal"
  
  description: |
    Web-based portal for customer self-service including
    account management, order tracking, and support.
    
  business_information:
    owner: "Customer Experience Team"
    criticality: "High"
    users: "External Customers"
    revenue_impact: "$10M/month"
    
  technical_information:
    architecture: "Microservices"
    technology_stack:
      - "React Frontend"
      - "Node.js APIs"
      - "PostgreSQL Database"
      - "Redis Cache"
    hosting: "AWS us-east-1"
    
  dependencies:
    internal:
      - "Authentication Service"
      - "Order Management System"
      - "Payment Gateway"
    external:
      - "CDN Provider"
      - "Email Service"
      - "SMS Gateway"
      
  documentation:
    - architecture_diagram
    - runbook
    - disaster_recovery_plan
    - api_documentation

Next Steps​