OP.PL.4 Dimensionamiento y Gestión de Capacidades

Documentos de referencia

• ISO/IEC 27000
  • 27002:2013
    • 12.1.3 - Gestión de capacidades
• NIST SP 800-53 rev.4
  • [SA-2] Allocation of Resources
  • [AU-4] Audit Storage Capacity

Definiciones

• EAL. Evaluation Assurance Level. Niveles de confianza en la evaluación.
• TOE. Target of Evaluation. Objetivo de evaluación.

Guía de implantación

83. Conviene destacar que esta medida de seguridad no es meramente técnica, sino que tiene implicaciones presupuestarias y por ello debe gestionarse con tiempo para que las necesidades queden debidamente recogidas en los presupuestos. Si en todas las medidas de seguridad hay que huir de la improvisación, en esta con mayor razón.

84. Nótese que en entornos flexibles como es el empleo de recursos en la nube, el dimensionado efectivo del sistema puede ser dinámico, adecuándose a las necesidades del servicio.

Implementación en Legit Health Plus

1. Marco de Dimensionamiento y Gestión de Capacidades

1.1 Estrategia de Capacity Management

Legit Health Plus implementa un enfoque proactivo de gestión de capacidades que abarca:

• Planificación de capacidad basada en proyecciones de crecimiento
• Monitorización continua de recursos y rendimiento
• Escalado automático en infraestructura cloud
• Optimización de costes mediante rightsizing
• Gestión de picos de demanda estacionales o excepcionales
• Planificación presupuestaria a medio y largo plazo

1.2 Modelo de Capacidad Multinivel

La gestión de capacidades se estructura en múltiples niveles:

2. Dimensionamiento por Componentes

2.1 Infraestructura de Aplicación

Servicios Core - Dimensionamiento Actual:

Componente	Configuración Actual	Capacidad Máxima	Utilización Target
API Gateway	4x ECS Tasks (2 vCPU, 4GB)	1000 req/sec	70% CPU
ML Inference	2x GPU instances (g4dn.xlarge)	100 concurrent	80% GPU
Image Processing	Auto-scaling 2-10 tasks	500 images/min	75% CPU
Auth Service	2x ECS Tasks (1 vCPU, 2GB)	200 auth/sec	60% CPU
Background Jobs	3x ECS Tasks (1 vCPU, 2GB)	1000 jobs/hour	70% CPU

2.2 Infraestructura de Datos

Almacenamiento - Capacidad Planificada:

Sistema	Configuración	Capacidad Actual	Proyección 12M	Crecimiento Anual
DocumentDB	3-node cluster (r6g.large)	1TB	5TB	400%
S3 Storage	Standard + IA tiers	50TB	200TB	300%
Redis Cache	2x r6g.large cluster	32GB	128GB	300%
CloudWatch Logs	30-day retention	500GB/month	2TB/month	300%
Backup Storage	S3 Glacier Deep Archive	100TB	500TB	400%

2.3 Red y Conectividad

Ancho de Banda Planificado:

Network Capacity Planning:
  Internet Gateway:
    current: 10 Gbps
    projected: 50 Gbps
    bottlenecks: Image upload bursts

  Inter-AZ Traffic:
    current: 5 Gbps
    projected: 20 Gbps
    pattern: DB replication, cross-AZ failover

  VPN Connections:
    current: 2x 1 Gbps
    projected: 4x 10 Gbps
    usage: Healthcare provider integrations

  CDN (CloudFront):
    current: Unlimited
    cost_optimization: Regional caching strategy

3. Modelos de Demanda y Proyecciones

3.1 Patrones de Uso Identificados

Análisis de Demanda Histórica:

Métrica	Q1 2024	Q2 2024	Q3 2024	Q4 2024	Proyección Q1 2025
Usuarios Activos	5,000	8,000	12,000	18,000	27,000
Imágenes/Día	10,000	16,000	24,000	36,000	54,000
API Calls/Día	100K	160K	240K	360K	540K
Storage (TB)	15	25	38	55	85
Concurrent Users	200	320	480	720	1,080

3.2 Factores de Crecimiento

Drivers de Demanda:

• Expansión geográfica: +200% usuarios por nueva región
• Nuevas especialidades médicas: +50% por especialidad
• Integraciones con HIS/EHR: +300% API calls por integración
• Mejoras en algoritmos: +25% precisión → +40% retención
• Programas de screening: Picos estacionales +500%

3.3 Modelado Predictivo

Modelos de Forecasting:

# Modelo de predicción de capacidad
capacity_model = {
    'base_growth': 0.15,  # 15% monthly growth
    'seasonal_factor': {
        'Q1': 1.2,  # Peak screening season
        'Q2': 0.9,  # Low season
        'Q3': 1.0,  # Normal
        'Q4': 1.1   # Conference season
    },
    'expansion_multipliers': {
        'new_region': 2.0,
        'new_specialty': 1.5,
        'enterprise_client': 3.0
    },
    'confidence_intervals': {
        'p50': 'base_forecast',
        'p80': 'base_forecast * 1.4',
        'p95': 'base_forecast * 1.8'
    }
}

4. Auto-scaling y Elasticidad

4.1 Políticas de Auto-scaling

ECS Auto-scaling Configuration:

api_service_scaling:
  target_tracking:
    cpu_utilization: 70%
    memory_utilization: 75%
  step_scaling:
    scale_out:
      - metric: RequestCount > 500/min
        scaling: +2 tasks
      - metric: RequestCount > 1000/min
        scaling: +4 tasks
    scale_in:
      - metric: RequestCount < 200/min
        scaling: -1 task (min 2)

ml_inference_scaling:
  scheduled_scaling:
    business_hours:
      min_capacity: 4 instances
      max_capacity: 20 instances
    off_hours:
      min_capacity: 2 instances
      max_capacity: 10 instances

  predictive_scaling:
    enable: true
    forecast_period: 7 days
    buffer: 20%

4.2 Database Auto-scaling

DocumentDB Scaling Strategy:

Métrica	Threshold	Acción
CPU > 80%	5 min sustained	Add read replica
Connections > 90%	2 min sustained	Scale up instance class
Storage > 85%	Alert only	Manual review required
Network I/O > 80%	10 min sustained	Consider sharding

4.3 Storage Tiering Automático

S3 Lifecycle Policies:

storage_lifecycle:
  medical_images:
    - transition: Standard → IA (30 days)
    - transition: IA → Glacier (90 days)
    - transition: Glacier → Deep Archive (365 days)
    - expiration: Never (regulatory retention)

  application_logs:
    - transition: Standard → IA (7 days)
    - transition: IA → Glacier (30 days)
    - expiration: 2555 days (7 years regulatory)

  backup_data:
    - immediate: Glacier Deep Archive
    - expiration: 2920 days (8 years)

5. Monitorización y Alerting

5.1 Métricas de Capacidad Críticas

Dashboard Principal - KPIs de Capacidad:

Métrica	SLA	Warning	Critical	Frecuencia
API Response Time	< 500ms p95	> 400ms	> 800ms	1 min
Image Processing Time	< 30s p95	> 25s	> 45s	1 min
Database CPU	< 70% avg	> 60%	> 85%	5 min
Storage Usage	< 80%	> 70%	> 90%	15 min
Concurrent Users	N/A	> 80% capacity	> 95% capacity	1 min
Error Rate	< 0.1%	> 0.05%	> 0.2%	1 min

5.2 Alerting Automático

Alert Routing Matrix:

alerts:
  capacity_warning:
    recipients: [devops, sre-team]
    escalation_time: 30min
    channels: [slack, email]

  capacity_critical:
    recipients: [oncall, cto, devops]
    escalation_time: 5min
    channels: [pagerduty, phone, slack]
    auto_actions: [trigger_scaling, create_incident]

  budget_alerts:
    recipients: [finops, cto]
    thresholds: [50%, 80%, 95%, 100%]
    frequency: daily

5.3 Observabilidad Avanzada

Stack de Monitorización:

Herramienta	Función	Métricas Clave
CloudWatch	AWS metrics, logs	Infrastructure, application metrics
DataDog	APM, synthetics	End-to-end latency, user experience
Grafana	Dashboards	Business metrics, capacity trends
Prometheus	Custom metrics	Application-specific KPIs
ELK Stack	Log analysis	Error patterns, usage analytics

6. Optimización de Costes

6.1 FinOps - Gestión Financiera de Cloud

Estructura de Costes Actual (Mensual):

Categoría	Coste	% Total	Optimización Identificada
Compute (ECS/EC2)	$15,000	35%	Reserved Instances: -25%
Storage (S3/EBS)	$8,000	18%	Lifecycle policies: -30%
Database	$12,000	28%	Rightsizing: -20%
Network/CDN	$5,000	12%	Caching optimization: -15%
Monitoring/Logs	$3,000	7%	Retention policies: -25%
Total	$43,000	100%	Potential savings: -23%

6.2 Strategies de Optimización

Cost Optimization Roadmap:

Q1_2025:
  - implement: Reserved Instance strategy
    savings: $3,750/month
  - implement: S3 lifecycle policies
    savings: $2,400/month

Q2_2025:
  - implement: Database rightsizing
    savings: $2,400/month
  - implement: Log retention optimization
    savings: $750/month

Q3_2025:
  - implement: Spot instances for batch workloads
    savings: $1,500/month
  - implement: CDN optimization
    savings: $750/month

Annual_Savings: $139,800
ROI_on_optimization: 340%

7. Capacity Planning - Presupuestos

7.1 Proyecciones Presupuestarias

Budget Planning FY2025:

Trimestre	Usuarios Proyectados	Coste Infraestructura	Crecimiento
Q1	30,000	$52,000/mes	+21%
Q2	40,000	$68,000/mes	+31%
Q3	55,000	$89,000/mes	+31%
Q4	75,000	$118,000/mes	+33%
Total FY	-	$1,308,000/año	+180%

7.2 Contingency Planning

Escenarios de Capacidad:

Escenario	Probabilidad	Impacto Coste	Contingencia
Crecimiento Base	60%	Budget baseline	Planned scaling
Crecimiento Acelerado	25%	+40% budget	Emergency scaling fund
Pandemia/Screening masivo	10%	+200% capacity	Spot instances + CDN boost
Recession/Slow growth	5%	-30% demand	Scale-down automation

8. Gestión de Recursos Especializados

8.1 GPU Computing para ML

Configuración GPU Clusters:

ml_gpu_capacity:
  training_cluster:
    instances: 4x p3.8xlarge (V100)
    usage_pattern: Batch training jobs
    cost_optimization: Spot instances (70% savings)

  inference_cluster:
    instances: 6x g4dn.xlarge (T4)
    usage_pattern: Real-time inference
    scaling: Auto-scale 2-20 instances

  development:
    instances: 2x g4dn.large
    usage_pattern: Model development
    scheduling: Shared resource pool

8.2 Specialized Storage Requirements

Medical Imaging Storage:

Tipo	Rendimiento	Capacidad	Coste/TB/mes	Use Case
EFS (General)	100 MB/s	1TB	$300	Shared model artifacts
S3 Standard	N/A	50TB	$23	Active image processing
S3 IA	N/A	150TB	$12.50	Recent images (30-90 days)
S3 Glacier	Minutes	500TB	$4	Archive (90+ days)
S3 Deep Archive	Hours	2PB	$1	Long-term regulatory

9. Business Continuity y Disaster Recovery

9.1 RTO/RPO Requirements

Recovery Objectives por Servicio:

Servicio	RTO	RPO	DR Strategy
API Core	15 min	1 min	Multi-AZ + auto-failover
ML Inference	30 min	5 min	Multi-region model deployment
Database	1 hour	15 min	Cross-region read replica
File Storage	4 hours	1 hour	Cross-region replication
Monitoring	5 min	Real-time	Multi-region deployment

9.2 Capacity for DR

DR Infrastructure Sizing:

disaster_recovery:
  primary_region: eu-west-1 (100% capacity)
  dr_region: eu-central-1 (warm standby)

  dr_capacity_allocation:
    compute: 50% of primary (scale on demand)
    storage: 100% (continuous replication)
    network: 100% (redundant connections)

  failover_scenarios:
    planned_maintenance: Zero downtime
    availability_zone_failure: < 15min RTO
    region_failure: < 4hour RTO

  cost_impact: +35% infrastructure cost

10. Compliance y Auditoría

10.1 Capacity Management Audit Trail

Auditoría de Decisiones de Capacidad:

audit_requirements:
  capacity_changes:
    approval_required: > 25% capacity change
    documentation: Business justification + technical assessment
    retention: 7 years

  budget_variances:
    threshold: +/- 15% monthly budget
    escalation: CFO + CTO notification
    review_cycle: Monthly

  performance_slas:
    monitoring: Continuous
    reporting: Monthly SLA reports
    compliance: 99%+ SLA adherence required

10.2 Regulatory Compliance para Healthcare

Medical Device Capacity Requirements:

Regulación	Requirement	Implementation
FDA QSR	Change control for capacity	Formal approval process
EU MDR	Performance monitoring	Continuous metrics collection
HIPAA	Availability requirements	99.9% uptime SLA
GDPR	Data processing capacity	Privacy by design scaling

11. Automatización y Tooling

11.1 Infrastructure as Code

Capacity Automation Stack:

automation_tools:
  infrastructure:
    - terraform: Infrastructure provisioning
    - ansible: Configuration management
    - helm: Kubernetes deployments

  monitoring:
    - prometheus: Metrics collection
    - grafana: Visualization
    - alertmanager: Alert routing

  optimization:
    - aws_cost_explorer: Cost analysis
    - rightsizing_recommendations: AWS Compute Optimizer
    - custom_scripts: Cleanup automation

11.2 Self-Healing Infrastructure

Automated Remediation:

# Ejemplo de auto-remediation
def handle_capacity_alert(alert_type, metrics):
    if alert_type == "high_cpu":
        if can_scale_horizontally():
            trigger_auto_scaling()
        else:
            create_incident("Manual intervention required")

    elif alert_type == "storage_full":
        if is_log_storage():
            cleanup_old_logs()
        else:
            expand_storage_tier()

    elif alert_type == "memory_pressure":
        restart_memory_intensive_services()
        if not improved():
            scale_up_instance_size()

12. Métricas y KPIs

12.1 Operational Excellence KPIs

Métrica	Target	Q4 2024	Trend
Capacity Utilization	70-80%	74%	✅
Cost per Transaction	< $0.02	$0.018	↓
Auto-scaling Events	< 50/day	32/day	✅
Capacity Planning Accuracy	±10%	±8%	✅
Time to Scale	< 5 min	3.2 min	✅
Budget Variance	±5%	+3%	✅

12.2 Business Impact Metrics

Business KPI	Target	Current	Impact
Revenue per GB	$0.15	$0.18	↑ 20%
User Satisfaction	> 4.5/5	4.7/5	↑
Time to Market	< 2 weeks	10 days	↑ 30%
Cost of Downtime	< $1K/hour	$0/hour	✅

13. Roadmap de Evolución

13.1 Capacity Management Maturity

Niveles de Madurez:

1. Reactive (Actual): ✅ Monitoring, alerting, manual scaling
2. Proactive (Q1 2025): 🔄 Predictive scaling, cost optimization
3. Predictive (Q3 2025): 📋 ML-based forecasting, automated optimization
4. Autonomous (2026): 📋 Self-managing infrastructure, AI-driven decisions

13.2 Technology Evolution

Emerging Technologies:

technology_roadmap:
  2025:
    - serverless_ml: AWS Lambda + SageMaker inference
    - spot_fleet: 80% cost savings on batch workloads
    - graviton_processors: 20% better price/performance

  2026:
    - kubernetes_adoption: EKS for better resource utilization
    - service_mesh: Istio for traffic management
    - chaos_engineering: Automated resilience testing

  2027:
    - quantum_ready: Quantum-safe cryptography planning
    - edge_computing: Regional inference deployment
    - ai_ops: Fully automated operations

Anexo A: Capacity Sizing Calculator

class CapacityCalculator:
    def __init__(self, growth_rate=0.15, seasonal_factor=1.0):
        self.growth_rate = growth_rate
        self.seasonal_factor = seasonal_factor

    def project_capacity(self, current_usage, months_ahead):
        base_projection = current_usage * (1 + self.growth_rate) ** months_ahead
        return base_projection * self.seasonal_factor

    def calculate_cost(self, capacity_requirements):
        cost_model = {
            'compute': capacity_requirements['cpu_hours'] * 0.05,
            'storage': capacity_requirements['gb_storage'] * 0.023,
            'network': capacity_requirements['gb_transfer'] * 0.09,
            'ml_inference': capacity_requirements['ml_requests'] * 0.001
        }
        return sum(cost_model.values())

Anexo B: Emergency Scaling Runbook

Procedimiento de Escalado de Emergencia:

1. Detección: Alert automática o manual
2. Evaluación: Determinar scope y urgencia (< 5 min)
3. Autorización: Auto-aprobada si < 200% capacity
4. Ejecución: Terraform apply + monitoring
5. Validación: Verificar métricas en 15 min
6. Comunicación: Stakeholder notification
7. Post-mortem: Análisis de causa raíz en 48h

Signature meaning

The signatures for the approval process of this document can be found in the verified commits at the repository for the QMS. As a reference, the team members who are expected to participate in this document and their roles in the approval process, as defined in Annex I Responsibility Matrix of the GP-001, are:

• Author: Team members involved
• Reviewer: JD-003, JD-004
• Approver: JD-001