Auto Scaling Guide¶

This guide provides comprehensive information on implementing auto scaling for ZeroTrustKerberosLink to ensure optimal performance, cost efficiency, and high availability under varying load conditions.

Overview¶

Auto scaling allows ZeroTrustKerberosLink to automatically adjust the number of instances based on current demand, ensuring optimal performance during peak times and cost efficiency during periods of low demand.

Auto Scaling Architecture¶

Basic Auto Scaling Architecture¶

                          ┌─────────────────┐
                          │                 │
                          │  Load Balancer  │
                          │                 │
                          └────────┬────────┘
                                   │
                 ┌─────────────────┼─────────────────┐
                 │                 │                 │
        ┌────────▼─────┐  ┌────────▼─────┐  ┌────────▼─────┐
        │              │  │              │  │              │
        │ ZeroTrust    │  │ ZeroTrust    │  │ ZeroTrust    │
        │ Instance 1   │  │ Instance 2   │  │ Instance 3   │
        │              │  │              │  │              │
        └──────────────┘  └──────────────┘  └──────────────┘
                 ▲                 ▲                 ▲
                 │                 │                 │
                 └─────────────────┼─────────────────┘
                                   │
                          ┌────────▼────────┐
                          │                 │
                          │  Auto Scaling   │
                          │  Controller     │
                          │                 │
                          └─────────────────┘

Auto Scaling Implementation¶

AWS Auto Scaling¶

For AWS deployments, use AWS Auto Scaling:

auto_scaling:
  provider: "aws"
  aws:
    min_instances: 2
    max_instances: 10
    desired_capacity: 3
    cooldown_period: 300  # seconds
    health_check_grace_period: 120  # seconds
    health_check_type: "ELB"  # EC2 or ELB
    termination_policies:
      - "OldestInstance"
      - "Default"

Kubernetes Horizontal Pod Autoscaler¶

For Kubernetes deployments, use Horizontal Pod Autoscaler:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: zerotrustkerberos
  namespace: zerotrustkerberos
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: zerotrustkerberos
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
    scaleUp:
      stabilizationWindowSeconds: 60

Docker Swarm¶

For Docker Swarm deployments:

version: '3.8'
services:
  zerotrustkerberos:
    image: zerotrustkerberos:latest
    deploy:
      replicas: 3
      update_config:
        parallelism: 1
        delay: 10s
        order: start-first
      restart_policy:
        condition: any
        delay: 5s
        max_attempts: 3
        window: 120s
      resources:
        limits:
          cpus: '0.5'
          memory: 1G
      mode: replicated
      placement:
        constraints:
          - node.role == worker
      autoscaling:
        min_replicas: 2
        max_replicas: 10
        cpu_percentage: 70

Scaling Metrics¶

Configure which metrics trigger scaling actions:

CPU-Based Scaling¶

auto_scaling:
  metrics:
    cpu:
      enabled: true
      target_utilization: 70  # percentage
      scale_out_threshold: 80
      scale_in_threshold: 60

Memory-Based Scaling¶

auto_scaling:
  metrics:
    memory:
      enabled: true
      target_utilization: 80  # percentage
      scale_out_threshold: 85
      scale_in_threshold: 70

Request Rate Scaling¶

auto_scaling:
  metrics:
    request_rate:
      enabled: true
      target_requests_per_second: 100
      scale_out_threshold: 120
      scale_in_threshold: 80

Custom Metrics¶

auto_scaling:
  metrics:
    custom:
      - name: "authentication_rate"
        target_value: 50
        scale_out_threshold: 60
        scale_in_threshold: 40

Scaling Policies¶

Configure how scaling actions are performed:

Step Scaling¶

auto_scaling:
  policies:
    step_scaling:
      enabled: true
      scale_out_steps:
        - threshold: 70
          adjustment: 1
        - threshold: 85
          adjustment: 2
        - threshold: 95
          adjustment: 3
      scale_in_steps:
        - threshold: 40
          adjustment: -1
        - threshold: 25
          adjustment: -2

Target Tracking¶

auto_scaling:
  policies:
    target_tracking:
      enabled: true
      target_value: 70
      scale_out_cooldown: 300
      scale_in_cooldown: 300

Scheduled Scaling¶

auto_scaling:
  policies:
    scheduled:
      - name: "business-hours"
        schedule: "0 8 * * 1-5"  # 8 AM Monday-Friday
        min_instances: 5
        max_instances: 10
      - name: "off-hours"
        schedule: "0 18 * * 1-5"  # 6 PM Monday-Friday
        min_instances: 2
        max_instances: 5

Instance Lifecycle Management¶

Configure how instances are managed during scaling events:

Instance Warm-Up¶

auto_scaling:
  instance_lifecycle:
    warm_up_period: 120  # seconds
    pre_warm_instances: 1

Graceful Termination¶

auto_scaling:
  instance_lifecycle:
    termination_grace_period: 60  # seconds
    drain_connections: true

Stateless Design Considerations¶

To support auto scaling, ZeroTrustKerberosLink must be designed to be stateless:

Session Management: Store sessions in Redis rather than in-memory
Configuration: Use centralized configuration management
Caching: Use distributed caching with Redis
Logging: Send logs to a centralized logging system

Security Considerations¶

When implementing auto scaling, follow these best practices:

Secure Instance Initialization

Ensure that new instances are securely initialized with appropriate configurations and secrets.

Immutable Infrastructure

Use immutable infrastructure patterns to ensure consistent and secure deployments.

Instance Hardening

Apply security hardening to all instances in the auto scaling group.

Secure Termination

Implement secure instance termination procedures to prevent data leakage.

Monitoring and Alerting

Implement monitoring and alerting for auto scaling events to detect potential issues.

Monitoring Auto Scaling¶

ZeroTrustKerberosLink provides metrics for monitoring auto scaling:

Instance Count: Current number of instances
Scaling Events: Number of scale-out and scale-in events
Resource Utilization: CPU, memory, and other resource utilization
Request Rate: Number of requests per second
Response Time: Average response time

These metrics are available through the monitoring endpoints. See the Monitoring Guide for details.

Troubleshooting¶

Common auto scaling issues include:

Issue	Possible Causes	Resolution
Instances not scaling out	Incorrect scaling thresholds, resource constraints	Adjust scaling thresholds, check resource limits
Instances not scaling in	Incorrect scaling thresholds, minimum instance count	Adjust scaling thresholds, check minimum instance count
Frequent scaling events	Threshold too close to target, insufficient cooldown	Adjust thresholds, increase cooldown period
New instances failing health checks	Configuration issues, application errors	Check instance configuration, review application logs
Uneven load distribution	Load balancer configuration, session stickiness	Adjust load balancer configuration, disable session stickiness