Scaling Strategies

This document outlines the scaling strategies for the AI Agent Orchestration Platform.

Overview

The platform is designed to scale horizontally and vertically to handle increasing workloads, user bases, and data volumes. This document covers scaling approaches for different components, load balancing, auto-scaling, and performance optimization.

Scaling Principles

The platform follows these scaling principles:

1. Horizontal Scaling: Add more instances to distribute load
2. Vertical Scaling: Increase resources for existing instances
3. Stateless Design: Enable easy scaling of stateless components
4. Data Partitioning: Distribute data across multiple instances
5. Caching: Reduce load on backend systems
6. Asynchronous Processing: Decouple time-intensive operations
7. Load Balancing: Distribute traffic across instances
8. Auto-Scaling: Automatically adjust resources based on demand

Component Scaling Strategies

API Services

Scaling strategy for API services:

• Horizontal Scaling: Deploy multiple API service instances
• Load Balancing: Distribute requests across instances
• Auto-Scaling: Adjust instance count based on CPU/memory usage and request rate
• Rate Limiting: Prevent abuse and ensure fair resource allocation
• Connection Pooling: Efficiently manage database connections

Example API service scaling configuration:

# api-scaling.yaml
api_service:
  deployment:
    min_replicas: 3
    max_replicas: 20
    target_cpu_utilization: 70
    target_memory_utilization: 80

  resources:
    requests:
      cpu: 500m
      memory: 512Mi
    limits:
      cpu: 2000m
      memory: 2Gi

  load_balancing:
    algorithm: round_robin
    session_affinity: false
    health_check:
      path: /health
      port: 8000
      initial_delay: 30s
      period: 10s

  rate_limiting:
    requests_per_minute: 1000
    burst: 100

Workflow Engine

Scaling strategy for the workflow engine:

• Horizontal Scaling: Deploy multiple workflow engine instances
• Workflow Partitioning: Distribute workflows across instances
• State Management: Maintain workflow state in shared storage
• Resource Allocation: Allocate resources based on workflow complexity
• Priority Queuing: Process workflows based on priority

Example workflow engine scaling configuration:

# workflow-engine-scaling.yaml
workflow_engine:
  deployment:
    min_replicas: 2
    max_replicas: 10
    target_cpu_utilization: 70
    target_memory_utilization: 80

  resources:
    requests:
      cpu: 1000m
      memory: 1Gi
    limits:
      cpu: 4000m
      memory: 4Gi

  partitioning:
    strategy: consistent_hashing
    partitions: 10

  state_management:
    storage: postgresql
    cache: redis

  queuing:
    high_priority_queue_size: 100
    normal_priority_queue_size: 500
    low_priority_queue_size: 1000

Agent Execution

Scaling strategy for agent execution:

• Dynamic Provisioning: Create agent instances on demand
• Resource Isolation: Run agents in isolated containers
• Resource Limits: Set CPU, memory, and storage limits
• Execution Pooling: Reuse agent instances when possible
• Batch Processing: Process multiple inputs in batch when appropriate

Example agent execution scaling configuration:

# agent-execution-scaling.yaml
agent_execution:
  deployment:
    strategy: dynamic
    idle_pool_size: 5
    max_concurrent_agents: 100

  resources:
    default:
      cpu: 500m
      memory: 512Mi
      ephemeral_storage: 1Gi

    large:
      cpu: 2000m
      memory: 4Gi
      ephemeral_storage: 5Gi

    gpu:
      cpu: 1000m
      memory: 2Gi
      gpu: 1
      ephemeral_storage: 10Gi

  isolation:
    type: container
    runtime: docker

  batch_processing:
    enabled: true
    max_batch_size: 10
    batch_timeout: 5s

Database

Scaling strategy for the database:

• Read Replicas: Distribute read queries across replicas
• Connection Pooling: Efficiently manage database connections
• Sharding: Partition data across multiple database instances
• Caching: Cache frequently accessed data
• Query Optimization: Optimize database queries for performance

Example database scaling configuration:

# database-scaling.yaml
database:
  primary:
    resources:
      cpu: 4000m
      memory: 16Gi
      storage: 100Gi

  read_replicas:
    count: 3
    resources:
      cpu: 2000m
      memory: 8Gi
      storage: 100Gi

  connection_pooling:
    max_connections: 500
    min_connections: 10
    max_client_connections: 100

  sharding:
    enabled: false  # Enable for very large deployments
    shards: 4
    strategy: hash

  caching:
    enabled: true
    type: redis
    ttl: 300s

Storage

Scaling strategy for storage:

• Object Storage: Use scalable object storage for files
• Content Delivery Network: Distribute static content
• Storage Tiering: Move less frequently accessed data to lower-cost storage
• Data Lifecycle Management: Archive or delete old data
• Compression: Reduce storage requirements

Example storage scaling configuration:

# storage-scaling.yaml
storage:
  object_storage:
    provider: s3
    bucket: meta-agent-files
    region: us-west-2

  cdn:
    enabled: true
    provider: cloudfront
    ttl: 86400

  tiering:
    hot_tier:
      storage_class: standard
      max_age: 30d

    warm_tier:
      storage_class: infrequent_access
      max_age: 90d

    cold_tier:
      storage_class: glacier
      max_age: 365d

  lifecycle:
    temporary_files_retention: 24h
    execution_results_retention: 90d
    audit_logs_retention: 365d

  compression:
    enabled: true
    algorithm: gzip
    min_size: 1KB

Load Balancing

Load Balancer Configuration

The platform uses load balancers to distribute traffic:

• Layer 7 Load Balancing: HTTP/HTTPS traffic
• Layer 4 Load Balancing: TCP/UDP traffic
• Global Load Balancing: Distribute traffic across regions
• Health Checks: Verify instance health
• SSL Termination: Handle SSL/TLS connections

Example load balancer configuration:

# load-balancer.yaml
load_balancers:
  - name: api-lb
    type: layer7
    protocol: https
    port: 443
    algorithm: least_connections
    ssl_certificate: meta-agent.example.com
    backends:
      - service: api
        port: 8000
        weight: 1
    health_check:
      path: /health
      port: 8000
      interval: 10s
      timeout: 5s
      healthy_threshold: 2
      unhealthy_threshold: 3

  - name: workflow-lb
    type: layer7
    protocol: https
    port: 8443
    algorithm: round_robin
    ssl_certificate: workflow.meta-agent.example.com
    backends:
      - service: workflow-engine
        port: 8080
        weight: 1
    health_check:
      path: /health
      port: 8080
      interval: 10s
      timeout: 5s
      healthy_threshold: 2
      unhealthy_threshold: 3

Auto-Scaling

Auto-Scaling Configuration

The platform implements auto-scaling:

• Horizontal Pod Autoscaler: Scale Kubernetes pods
• Vertical Pod Autoscaler: Adjust pod resources
• Cluster Autoscaler: Scale Kubernetes nodes
• Custom Metrics: Scale based on custom metrics
• Scheduled Scaling: Scale based on time of day

Example auto-scaling configuration:

# auto-scaling.yaml
horizontal_pod_autoscalers:
  - name: api-hpa
    target:
      kind: Deployment
      name: api
    min_replicas: 3
    max_replicas: 20
    metrics:
      - type: Resource
        resource:
          name: cpu
          target_average_utilization: 70
      - type: Resource
        resource:
          name: memory
          target_average_utilization: 80

  - name: workflow-engine-hpa
    target:
      kind: Deployment
      name: workflow-engine
    min_replicas: 2
    max_replicas: 10
    metrics:
      - type: Resource
        resource:
          name: cpu
          target_average_utilization: 70
      - type: Pods
        pods:
          metric_name: workflow_queue_length
          target_average_value: 10

vertical_pod_autoscaler:
  enabled: true
  targets:
    - name: api
      update_mode: Auto
      resource_policy:
        container_policies:
          - container_name: api
            min_allowed:
              cpu: 500m
              memory: 512Mi
            max_allowed:
              cpu: 4000m
              memory: 4Gi

    - name: workflow-engine
      update_mode: Auto
      resource_policy:
        container_policies:
          - container_name: workflow-engine
            min_allowed:
              cpu: 1000m
              memory: 1Gi
            max_allowed:
              cpu: 8000m
              memory: 8Gi

cluster_autoscaler:
  enabled: true
  min_nodes: 3
  max_nodes: 20
  scale_down_delay: 10m
  scale_down_unneeded_time: 10m
  scale_down_utilization_threshold: 0.5

scheduled_scaling:
  - name: business-hours
    schedule: "0 8 * * 1-5"  # 8:00 AM Monday-Friday
    min_replicas:
      api: 5
      workflow-engine: 3

  - name: non-business-hours
    schedule: "0 18 * * 1-5"  # 6:00 PM Monday-Friday
    min_replicas:
      api: 2
      workflow-engine: 1

Caching

Caching Strategy

The platform implements caching:

• Application Cache: Cache application data
• Database Cache: Cache database queries
• Content Cache: Cache static content
• Distributed Cache: Share cache across instances
• Cache Invalidation: Update cache when data changes

Example caching configuration:

# caching.yaml
caches:
  - name: application-cache
    type: redis
    endpoints:
      - host: redis-master
        port: 6379
    replicas: 2
    eviction_policy: volatile-lru
    max_memory: 1GB
    ttl: 300s

  - name: database-cache
    type: redis
    endpoints:
      - host: redis-db-cache
        port: 6379
    replicas: 2
    eviction_policy: allkeys-lru
    max_memory: 2GB
    ttl: 600s

  - name: content-cache
    type: cdn
    provider: cloudfront
    origins:
      - domain: static.meta-agent.example.com
        path: /assets
    ttl: 86400s
    invalidation_paths:
      - /assets/js/*
      - /assets/css/*
      - /assets/images/*

cache_keys:
  - prefix: user
    pattern: "user:{id}"
    ttl: 3600s

  - prefix: workflow
    pattern: "workflow:{id}"
    ttl: 300s

  - prefix: agent
    pattern: "agent:{id}"
    ttl: 3600s

  - prefix: execution
    pattern: "execution:{id}"
    ttl: 60s

cache_invalidation:
  - event: user_update
    keys:
      - "user:{id}"
      - "user_permissions:{id}"

  - event: workflow_update
    keys:
      - "workflow:{id}"
      - "workflow_list"

  - event: agent_update
    keys:
      - "agent:{id}"
      - "agent_list"

Performance Optimization

Performance Tuning

The platform implements performance optimization:

• Code Optimization: Optimize application code
• Database Optimization: Optimize database queries and indexes
• Network Optimization: Reduce network latency and overhead
• Resource Allocation: Allocate resources based on workload
• Monitoring and Profiling: Identify performance bottlenecks

Example performance optimization configuration:

# performance-optimization.yaml
application:
  timeouts:
    http_request: 30s
    database_query: 5s
    cache_operation: 1s
    agent_execution: 300s

  connection_pools:
    database:
      max_connections: 100
      min_connections: 10
      max_idle_time: 300s

    http_client:
      max_connections: 200
      max_connections_per_host: 20
      keep_alive: 300s

  concurrency:
    max_goroutines: 10000
    worker_pool_size: 100

database:
  query_optimization:
    slow_query_threshold: 1s
    log_slow_queries: true

  indexes:
    - table: workflows
      columns: [user_id, status, created_at]

    - table: workflow_executions
      columns: [workflow_id, status, started_at]

    - table: agents
      columns: [type, status, created_at]

  connection_tuning:
    max_connections: 500
    shared_buffers: 4GB
    work_mem: 64MB
    maintenance_work_mem: 256MB
    effective_cache_size: 12GB

network:
  compression:
    enabled: true
    min_size: 1KB

  http2:
    enabled: true

  keepalive:
    enabled: true
    timeout: 300s

  tcp_tuning:
    tcp_keepalive_time: 300
    tcp_keepalive_intvl: 75
    tcp_keepalive_probes: 9

Multi-Region Scaling

Global Deployment

The platform supports multi-region deployment:

• Regional Deployments: Deploy in multiple regions
• Global Load Balancing: Route users to nearest region
• Data Replication: Replicate data across regions
• Disaster Recovery: Recover from regional outages
• Compliance: Meet data residency requirements

Example multi-region configuration:

# multi-region.yaml
regions:
  - name: us-west
    primary: true
    zone: us-west-2
    components:
      - api
      - workflow-engine
      - database-primary
      - cache

  - name: us-east
    primary: false
    zone: us-east-1
    components:
      - api
      - workflow-engine
      - database-replica
      - cache

  - name: eu-west
    primary: false
    zone: eu-west-1
    components:
      - api
      - workflow-engine
      - database-replica
      - cache

global_load_balancer:
  type: dns
  provider: route53
  routing_policy: latency
  health_checks:
    path: /health
    interval: 30s
    failure_threshold: 3

data_replication:
  database:
    type: postgresql
    replication_mode: asynchronous
    primary_region: us-west
    replica_regions: [us-east, eu-west]

  cache:
    type: redis
    replication_mode: active-active
    regions: [us-west, us-east, eu-west]

  object_storage:
    type: s3
    replication_mode: cross-region
    primary_region: us-west
    replica_regions: [us-east, eu-west]

disaster_recovery:
  rto: 1h  # Recovery Time Objective
  rpo: 15m  # Recovery Point Objective
  failover:
    automatic: true
    verification: true
    fallback: true

Edge Scaling

Edge Deployment Scaling

The platform supports edge deployment scaling:

• Edge Locations: Deploy to edge locations
• Content Delivery: Deliver content from edge
• Edge Computing: Process data at the edge
• Edge Caching: Cache data at the edge
• Edge-to-Core Synchronization: Sync edge and core data

Example edge scaling configuration:

# edge-scaling.yaml
edge_locations:
  - name: us-east
    provider: cloudflare
    services: [content-delivery, edge-computing]

  - name: us-west
    provider: cloudflare
    services: [content-delivery, edge-computing]

  - name: eu-west
    provider: cloudflare
    services: [content-delivery, edge-computing]

  - name: asia-east
    provider: cloudflare
    services: [content-delivery, edge-computing]

content_delivery:
  enabled: true
  cache_control:
    static_assets: "public, max-age=86400"
    api_responses: "private, max-age=60"
    dynamic_content: "no-store"

edge_computing:
  enabled: true
  functions:
    - name: request-validation
      path: /api/*
      script: |
        addEventListener('fetch', event => {
          event.respondWith(handleRequest(event.request))
        })

        async function handleRequest(request) {
          // Validate request
          // ...
          return fetch(request)
        }

    - name: response-transformation
      path: /api/data/*
      script: |
        addEventListener('fetch', event => {
          event.respondWith(handleRequest(event.request))
        })

        async function handleRequest(request) {
          const response = await fetch(request)
          const data = await response.json()
          // Transform data
          // ...
          return new Response(JSON.stringify(data), {
            headers: { 'Content-Type': 'application/json' }
          })
        }

edge_caching:
  enabled: true
  cache_rules:
    - pattern: /api/workflows
      ttl: 60s

    - pattern: /api/agents
      ttl: 300s

    - pattern: /assets/*
      ttl: 86400s

Scaling Scripts

Scripts for scaling management are located in /infra/scripts/:

• scale_up.sh - Scale up resources
• scale_down.sh - Scale down resources
• performance_test.sh - Test performance under load
• optimize_database.sh - Optimize database performance
• cache_warmup.sh - Warm up cache with frequently accessed data

Example scaling script:

#!/bin/bash
# scale_up.sh - Scale up resources

COMPONENT=$1
REPLICAS=$2

if [ -z "$COMPONENT" ] || [ -z "$REPLICAS" ]; then
  echo "Usage: ./scale_up.sh [component] [replicas]"
  echo "Example: ./scale_up.sh api 10"
  exit 1
fi

echo "Scaling up $COMPONENT to $REPLICAS replicas..."

# Scale up the component
kubectl scale deployment $COMPONENT --replicas=$REPLICAS

# Wait for scaling to complete
kubectl rollout status deployment/$COMPONENT

echo "Scaling complete for $COMPONENT"

Best Practices

• Design for horizontal scaling
• Implement auto-scaling
• Use load balancing
• Optimize database queries
• Implement caching
• Monitor performance
• Test scalability
• Plan for future growth
• Document scaling procedures
• Regularly review and optimize

References

• Deployment Infrastructure
• Containerization
• Monitoring Infrastructure
• Database Infrastructure
• Edge Infrastructure
• Architecture Design

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Last updated: 2025-04-18