Ruvector Global Streaming Architecture

500 Million Concurrent Streams on Google Cloud Run

Version: 1.0.0 Last Updated: 2025-11-20 Target Scale: 500M concurrent learning streams SLA Target: 99.99% availability, <10ms p50, <50ms p99

Executive Summary

This document outlines the comprehensive architecture for scaling Ruvector to support 500 million concurrent learning streams using Google Cloud Run with global multi-region deployment. The design leverages Ruvector's Rust-native performance (<0.5ms base latency) combined with GCP's global infrastructure to deliver sub-10ms p50 latency and 99.99% availability.

Key Architecture Principles:

Stateless Service Layer: Cloud Run services for horizontal scalability
Distributed State: Regional vector data stores with eventual consistency
Edge-First Routing: Cloud CDN + Load Balancer for proximity-based routing
Burst Resilience: Predictive + reactive auto-scaling with 10-50x burst capacity
Multi-Region Active-Active: 15+ global regions for low latency and fault tolerance

1. Global Multi-Region Topology

1.1 Regional Distribution

Primary Regions (15 Core Deployments):

Americas (5):
├── us-central1 (Iowa) - Primary US Hub
├── us-east1 (South Carolina) - East Coast
├── us-west1 (Oregon) - West Coast
├── southamerica-east1 (São Paulo) - LATAM Hub
└── northamerica-northeast1 (Montreal) - Canada

Europe (4):
├── europe-west1 (Belgium) - Primary EU Hub
├── europe-west2 (London) - UK/Finance
├── europe-west3 (Frankfurt) - Central Europe
└── europe-north1 (Finland) - Nordic Region

Asia-Pacific (5):
├── asia-northeast1 (Tokyo) - Japan Hub
├── asia-southeast1 (Singapore) - Southeast Asia Hub
├── australia-southeast1 (Sydney) - Australia/NZ
├── asia-south1 (Mumbai) - India Hub
└── asia-east1 (Taiwan) - Greater China

Middle East & Africa (1):
└── me-west1 (Tel Aviv) - MENA Region

Capacity Distribution (Baseline):

Tier 1 Hubs (5): 80M streams each = 400M total
- us-central1, europe-west1, asia-northeast1, asia-southeast1, southamerica-east1
Tier 2 Regions (10): 10M streams each = 100M total
- All other regions

Geographic Load Distribution Strategy:

User Location → Nearest Edge Location → Regional Cloud Run Service
                      ↓
              Cloud CDN Cache Layer
                      ↓
          Regional Vector Data Store
                      ↓
      Cross-Region Replication (async)

1.2 Network Architecture

┌─────────────────────────────────────────────────────────────┐
│  Global Layer (Anycast IPv4/IPv6)                           │
│  ┌────────────────────────────────────────────────────┐     │
│  │  Cloud Load Balancer (Global HTTPS)                │     │
│  │  - Anycast IP: 1 global IP address                 │     │
│  │  - SSL/TLS Termination (Google-managed certs)      │     │
│  │  - DDoS Protection (Cloud Armor)                   │     │
│  │  - Geo-routing based on client proximity           │     │
│  └────────────────────────────────────────────────────┘     │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│  Edge Layer (120+ Edge Locations)                           │
│  ┌────────────────────────────────────────────────────┐     │
│  │  Cloud CDN                                          │     │
│  │  - Cache query responses (5-60s TTL)               │     │
│  │  - Cache embeddings/vectors (1-5 min TTL)          │     │
│  │  - Negative caching for rate limits                │     │
│  │  - Compression (Brotli/gzip)                       │     │
│  │  - HTTP/3 (QUIC) support                           │     │
│  └────────────────────────────────────────────────────┘     │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│  Regional Layer (15 Regions)                                │
│  ┌────────────────────────────────────────────────────┐     │
│  │  Regional Backend Services                          │     │
│  │  - Load balancing algorithm: WEIGHTED_MAGLEV       │     │
│  │  - Session affinity: CLIENT_IP (5 min)             │     │
│  │  - Health checks: HTTP/2 gRPC (5s interval)        │     │
│  │  - Connection draining: 30s                        │     │
│  └────────────────────────────────────────────────────┘     │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│  Compute Layer (Cloud Run Services)                         │
│  ┌────────────────────────────────────────────────────┐     │
│  │  Ruvector Streaming Service (per region)           │     │
│  │  - 500-5,000 instances (auto-scaled)               │     │
│  │  - 100 concurrent requests per instance            │     │
│  │  - HTTP/2 + gRPC streaming                         │     │
│  │  - WebSocket support for persistent connections    │     │
│  └────────────────────────────────────────────────────┘     │
└─────────────────────────────────────────────────────────────┘

2. Cloud Run Service Design

2.1 Service Architecture

Ruvector Streaming Service Components:

// Core service structure (conceptual)
┌──────────────────────────────────────────┐
│  Cloud Run Container                     │
│  ┌────────────────────────────────────┐  │
│  │  HTTP/2 + gRPC Server              │  │
│  │  - Axum/Tonic framework            │  │
│  │  - 100 concurrent connections      │  │
│  │  - Keep-alive: 60s                 │  │
│  └────────────────────────────────────┘  │
│  ┌────────────────────────────────────┐  │
│  │  Ruvector Core Engine              │  │
│  │  - HNSW index (in-memory)          │  │
│  │  - SIMD-optimized search           │  │
│  │  - Product quantization            │  │
│  │  - Arena allocator                 │  │
│  └────────────────────────────────────┘  │
│  ┌────────────────────────────────────┐  │
│  │  Connection Pool Manager           │  │
│  │  - Redis (metadata)                │  │
│  │  - Cloud Storage (vectors)         │  │
│  │  - Pub/Sub (coordination)          │  │
│  └────────────────────────────────────┘  │
│  ┌────────────────────────────────────┐  │
│  │  Memory-Mapped Vector Store        │  │
│  │  - Local NVMe SSD (hot data)       │  │
│  │  - 8GB vector cache per instance   │  │
│  │  - LRU eviction policy             │  │
│  └────────────────────────────────────┘  │
└──────────────────────────────────────────┘

2.2 Service Configuration

Base Configuration (Per Instance):

service: ruvector-streaming
region: multi-region (15 regions)
resources:
  cpu: 4 vCPU
  memory: 16 GiB
  startup_cpu_boost: true
concurrency:
  max_per_instance: 100  # concurrent requests
  target_utilization: 0.70  # 70% target for headroom
scaling:
  min_instances: 500  # per region (baseline)
  max_instances: 5000  # per region (burst capacity)
  scale_down_delay: 180s  # 3 min cooldown
networking:
  vpc_connector: regional-vpc-connector
  vpc_egress: private-ranges-only
execution_environment: gen2
timeout: 300s  # 5 min for long-running streams
startup_timeout: 240s  # 4 min for HNSW index loading

Container Specifications:

Base Image: rust:1.77-alpine (optimized for size)
Runtime: Tokio async runtime with rayon thread pool
Binary Size: ~15MB (stripped, LTO-optimized)
Cold Start: <2s (with startup CPU boost)
Warm Start: <100ms

2.3 Regional Deployment Strategy

Deployment Topology:

Each Region Deploys:
├── Primary Cluster (Active)
│   ├── 500-5,000 Cloud Run instances
│   ├── Regional Memorystore Redis (16GB-256GB)
│   ├── Regional Cloud SQL (metadata)
│   └── Regional Cloud Storage bucket (vectors)
├── Standby Cluster (Warm Standby)
│   ├── 50-100 instances (10% of primary)
│   └── Read-only replicas
└── Monitoring Stack
    ├── Cloud Monitoring dashboards
    ├── Cloud Logging (structured logs)
    └── Cloud Trace (distributed tracing)

Traffic Distribution:

Active-Active: All regions serve traffic simultaneously
Geo-Routing: Users routed to nearest healthy region
Spillover: Overloaded regions redirect to nearest neighbor
Failover: Automatic re-routing on region failure (<30s)

3. Load Balancing & Traffic Routing

3.1 Global Load Balancer Configuration

load_balancer:
  type: EXTERNAL_MANAGED
  ip_version: IPV4_IPV6
  protocol: HTTPS

  ssl_policy:
    min_tls_version: TLS_1_2
    profile: MODERN

  backend_service:
    protocol: HTTP2
    port: 443
    timeout: 300s

    load_balancing_scheme: WEIGHTED_MAGLEV
    session_affinity: CLIENT_IP
    affinity_cookie_ttl: 300s  # 5 min

    health_check:
      type: HTTP2
      port: 8080
      request_path: /health/ready
      check_interval: 5s
      timeout: 3s
      healthy_threshold: 2
      unhealthy_threshold: 3

    cdn_policy:
      cache_mode: CACHE_ALL_STATIC
      default_ttl: 30s
      max_ttl: 300s
      client_ttl: 30s
      negative_caching: true
      negative_caching_policy:
        - code: 404
          ttl: 60s
        - code: 429  # Rate limit
          ttl: 10s

3.2 Routing Strategy

Request Flow:

1. Client Request
   ↓
2. DNS Resolution (Anycast IP)
   ↓
3. Edge Location (Cloud CDN)
   ├─→ Cache HIT: Return cached response (<5ms)
   └─→ Cache MISS: Forward to backend
       ↓
4. Global Load Balancer
   ├─→ Route to nearest region (latency-based)
   ├─→ Check region health
   └─→ Apply rate limiting (Cloud Armor)
       ↓
5. Regional Backend Service
   ├─→ Select healthy Cloud Run instance
   ├─→ Connection pooling (reuse existing)
   └─→ Session affinity (same user → same instance)
       ↓
6. Cloud Run Instance
   ├─→ Check local cache (Memorystore Redis)
   ├─→ Query HNSW index (in-memory)
   └─→ Return results
       ↓
7. Response Path
   ├─→ Cache at edge (CDN)
   ├─→ Compress (Brotli)
   └─→ Return to client

Routing Rules:

// Pseudo-code for routing logic
function routeRequest(request, regions) {
  const userLocation = geolocate(request.clientIP);
  const nearestRegions = findNearestRegions(userLocation, 3);

  for (const region of nearestRegions) {
    if (region.health === 'HEALTHY' && region.capacity > 20%) {
      return region;
    }
  }

  // Spillover to next available region
  return findLeastLoadedRegion(regions.filter(r => r.health === 'HEALTHY'));
}

3.3 Cloud CDN Configuration

Cache Strategy:

cdn_configuration:
  cache_key_policy:
    include_protocol: true
    include_host: true
    include_query_string: true
    query_string_whitelist:
      - query_vector_id
      - k  # top-k results
      - metric  # distance metric

  cache_rules:
    # Vector embedding queries (high cache hit rate)
    - path: /api/v1/embed/*
      cache_mode: CACHE_ALL
      default_ttl: 300s  # 5 min

    # Search queries (moderate cache hit rate)
    - path: /api/v1/search
      cache_mode: USE_ORIGIN_HEADERS
      default_ttl: 30s

    # Real-time updates (no cache)
    - path: /api/v1/insert
      cache_mode: FORCE_CACHE_ALL_BYPASS

  negative_caching:
    enabled: true
    ttl: 60s
    status_codes: [404, 429, 500, 502, 503, 504]

Cache Performance Targets:

Hit Rate: >60% (steady state), >80% (burst events)
Latency Reduction: 5-15ms (edge) vs 30-50ms (origin)
Bandwidth Savings: 40-60% reduction in origin traffic

4. Data Replication & Consistency

4.1 Data Architecture

Three-Tier Storage Model:

┌─────────────────────────────────────────────────────────┐
│  Tier 1: Hot Data (In-Memory)                           │
│  - Cloud Run instance memory (16GB per instance)        │
│  - HNSW index for active vectors                        │
│  - LRU cache (most recent 100K vectors per instance)    │
│  - Latency: <0.5ms                                      │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│  Tier 2: Warm Data (Regional Cache)                     │
│  - Memorystore Redis (16GB-256GB per region)            │
│  - Recently accessed vectors (1M-10M vectors)           │
│  - TTL: 1 hour (sliding window)                         │
│  - Latency: 1-3ms                                       │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│  Tier 3: Cold Data (Object Storage)                     │
│  - Cloud Storage (multi-region buckets)                 │
│  - Full vector database (billions of vectors)           │
│  - Memory-mapped files for large datasets               │
│  - Latency: 10-30ms (first access)                      │
└─────────────────────────────────────────────────────────┘

4.2 Replication Strategy

Multi-Region Replication:

Primary Region (us-central1)
    ↓ (real-time sync via Pub/Sub)
Regional Hubs (5 Tier-1 regions)
    ↓ (async replication, <5s lag)
Secondary Regions (10 Tier-2 regions)
    ↓ (periodic sync, <60s lag)
Cross-Region Backup (nearline storage)

Consistency Model:

Writes: Eventually consistent (5-60s global propagation)
Reads: Read-your-writes consistency within region
Critical Metadata: Strong consistency (Cloud Spanner or Cloud SQL with multi-region)

Replication Flow:

// Conceptual write path
1. User writes vector to regional Cloud Run instance
   ↓
2. Instance writes to:
   a) Local memory (immediate)
   b) Regional Redis (1-2ms)
   c) Regional Cloud Storage (5-10ms)
   ↓
3. Pub/Sub message published to global topic
   ↓
4. Regional subscribers receive update (100-500ms)
   ↓
5. Subscribers update:
   a) Regional Redis cache (invalidate or update)
   b) Regional Cloud Storage (async copy)
   ↓
6. Background job syncs to other regions (5-60s)

4.3 Conflict Resolution

Vector Update Conflicts:

Strategy: Last-Write-Wins (LWW) with Vector Clocks

1. Each update includes:
   - Timestamp (Unix nanoseconds)
   - Region ID
   - Version number

2. On conflict:
   - Compare timestamps
   - If same timestamp: lexicographic order by Region ID
   - Update conflict counter metric

3. Rare conflicts (<0.01% of writes):
   - Log for analysis
   - Emit monitoring alert if rate exceeds threshold

5. Edge Caching Strategy

5.1 Multi-Level Cache Hierarchy

L1: Browser/Client Cache (User Device)
    └─ TTL: 5 min
    └─ Size: ~10-50MB per client
    └─ Hit Rate: 70-80%
           ↓
L2: Cloud CDN Edge Cache (120+ edge locations)
    └─ TTL: 30-300s (content-dependent)
    └─ Size: ~100GB-1TB per edge
    └─ Hit Rate: 60-70%
           ↓
L3: Regional Memorystore Redis (15 regions)
    └─ TTL: 1 hour (sliding)
    └─ Size: 16GB-256GB per region
    └─ Hit Rate: 80-90%
           ↓
L4: Cloud Run Instance Memory (per instance)
    └─ TTL: Instance lifetime
    └─ Size: 8GB per instance
    └─ Hit Rate: 95%+
           ↓
L5: Cloud Storage (origin, multi-region)
    └─ Persistent storage
    └─ Size: Unlimited (petabytes)
    └─ Always available

5.2 Cache Warming Strategy

Pre-Event Warming (for predictable bursts):

# Example: World Cup event in 2 hours
1. Historical Analysis
   - Analyze similar events (previous World Cup matches)
   - Identify top 10K vectors likely to be queried
   - Estimate query patterns by region

2. Pre-Population (T-2 hours)
   - Batch load hot vectors into Redis (all regions)
   - Distribute to Cloud Run instances (rolling)
   - Trigger CDN cache pre-fetch for common queries

3. Validation (T-1 hour)
   - Run cache hit rate tests
   - Verify all regions have hot data
   - Scale up Cloud Run instances (50% → 100%)

4. Final Prep (T-30 min)
   - Scale to 120% capacity
   - Enable aggressive rate limiting for non-critical traffic
   - Activate burst alerting channels

Real-Time Adaptive Warming:

// Pseudo-code for adaptive cache warming
fn adaptive_cache_warming() {
    monitor_query_patterns(5min_window);

    if detect_emerging_pattern() {
        let hot_vectors = identify_trending_vectors();

        // Async pre-load to regional caches
        spawn_async(|| {
            for region in all_regions {
                redis_mset(region, hot_vectors, ttl=3600);
            }
        });

        // Update CDN cache keys
        cdn_prefetch(hot_vectors);
    }
}

5.3 Cache Invalidation

Invalidation Strategies:

invalidation_rules:
  # Vector updates (immediate invalidation)
  - trigger: vector_update
    scope: global
    method: PURGE_BY_KEY
    propagation_time: <5s

  # Batch updates (lazy invalidation)
  - trigger: batch_insert
    scope: regional
    method: EXPIRE_BY_TTL
    ttl: 60s

  # Model updates (full cache clear)
  - trigger: model_version_change
    scope: global
    method: PURGE_ALL
    notice_period: 5min  # gradual rollout

6. Connection Pooling & Streaming Protocol

6.1 Connection Pool Architecture

Regional Connection Pool:

┌───────────────────────────────────────────────────────┐
│  Cloud Run Instance (4 vCPU, 16GB)                    │
│  ┌─────────────────────────────────────────────────┐  │
│  │  HTTP/2 Connection Pool                         │  │
│  │  - Max connections: 100 concurrent              │  │
│  │  - Keep-alive: 60s                              │  │
│  │  - Idle timeout: 90s                            │  │
│  │  - Max streams per conn: 100 (HTTP/2 multiplex)│  │
│  └─────────────────────────────────────────────────┘  │
│  ┌─────────────────────────────────────────────────┐  │
│  │  Redis Connection Pool (Memorystore)            │  │
│  │  - Pool size: 50 connections                    │  │
│  │  - Max idle: 20                                 │  │
│  │  - Timeout: 5s                                  │  │
│  │  - Pipeline: 10 commands per batch              │  │
│  └─────────────────────────────────────────────────┘  │
│  ┌─────────────────────────────────────────────────┐  │
│  │  Pub/Sub Connection (coordination)              │  │
│  │  - Persistent gRPC stream                       │  │
│  │  - Auto-reconnect with exponential backoff      │  │
│  │  - Batched message publishing (100ms window)    │  │
│  └─────────────────────────────────────────────────┘  │
└───────────────────────────────────────────────────────┘

6.2 Streaming Protocol Design

Supported Protocols:

1. HTTP/2 Server-Sent Events (SSE) - Primary

GET /api/v1/stream/search HTTP/2
Host: ruvector.example.com
Accept: text/event-stream
Authorization: Bearer <token>

# Response (streaming)
HTTP/2 200 OK
Content-Type: text/event-stream
Cache-Control: no-cache

data: {"event":"search_start","query_id":"abc123"}

data: {"event":"result","vector_id":"vec_001","score":0.95}

data: {"event":"result","vector_id":"vec_002","score":0.89}

data: {"event":"search_complete","total_results":50}

2. WebSocket - For Bidirectional Streams

// Client-side
const ws = new WebSocket('wss://ruvector.example.com/api/v1/ws');

ws.send(JSON.stringify({
  type: 'search',
  query: [0.1, 0.2, 0.3, ...],
  k: 100,
  stream: true
}));

ws.onmessage = (event) => {
  const result = JSON.parse(event.data);
  // Process incremental results
};

3. gRPC Streaming - For Backend Services

service VectorSearch {
  rpc StreamSearch(SearchRequest) returns (stream SearchResult);
  rpc BidirectionalSearch(stream SearchRequest) returns (stream SearchResult);
}

message SearchRequest {
  repeated float query = 1;
  int32 k = 2;
  string metric = 3;
}

message SearchResult {
  string vector_id = 1;
  float score = 2;
  bytes metadata = 3;
}

6.3 Connection Management

Connection Lifecycle:

// Conceptual connection manager
struct ConnectionManager {
    active_connections: Arc<DashMap<ConnectionId, Connection>>,
    max_connections: usize,
    idle_timeout: Duration,
}

impl ConnectionManager {
    async fn handle_connection(&self, conn: Connection) {
        // 1. Authentication & Rate Limiting
        let user = authenticate(&conn).await?;
        check_rate_limit(&user)?;

        // 2. Register connection
        self.active_connections.insert(conn.id, conn.clone());

        // 3. Keep-alive loop
        tokio::spawn(async move {
            loop {
                select! {
                    msg = conn.recv() => process_message(msg),
                    _ = sleep(60s) => conn.send_ping(),
                    _ = sleep(idle_timeout) => break,
                }
            }
        });

        // 4. Cleanup on disconnect
        self.active_connections.remove(&conn.id);
        log_connection_metrics(&conn);
    }

    async fn handle_overload(&self) {
        if self.active_connections.len() > self.max_connections * 0.9 {
            // Shed least valuable connections
            let connections = self.find_idle_connections(older_than=5min);
            for conn in connections.iter().take(100) {
                conn.close_gracefully(reason="capacity");
            }
        }
    }
}

Load Shedding Strategy:

load_shedding:
  triggers:
    - cpu_usage > 85%
    - memory_usage > 90%
    - connection_count > 95 (per instance)
    - latency_p99 > 100ms

  actions:
    - priority: reject_new_connections
      threshold: 95%

    - priority: close_idle_connections
      idle_time: >5min
      threshold: 90%

    - priority: rate_limit_aggressive
      limit: 10 req/s per user
      threshold: 85%

    - priority: shed_non_premium_traffic
      percentage: 20%
      threshold: 95%

7. Monitoring & Observability

7.1 Key Metrics

Service-Level Indicators (SLIs):

availability:
  target: 99.99%
  measurement: successful_requests / total_requests
  window: 30 days

latency:
  p50_target: <10ms
  p95_target: <30ms
  p99_target: <50ms
  measurement: time_to_first_byte

throughput:
  target: 500M concurrent streams
  measurement: active_websocket_connections

error_rate:
  target: <0.1%
  measurement: (4xx + 5xx) / total_requests

Resource Metrics:

cloud_run:
  - instance_count (per region)
  - cpu_utilization
  - memory_utilization
  - container_startup_time
  - request_count
  - active_connections

redis:
  - cache_hit_rate
  - memory_usage
  - eviction_count
  - commands_per_second

cloud_storage:
  - read_operations
  - write_operations
  - bandwidth_usage
  - replication_lag

7.2 Distributed Tracing

Trace Propagation:

Request ID: req_abc123_us-central1_inst042

Span 1: Global Load Balancer (0-2ms)
    └─ Span 2: Cloud CDN Edge (2-5ms)
        └─ Span 3: Regional LB (5-8ms)
            └─ Span 4: Cloud Run Instance (8-15ms)
                ├─ Span 5: Redis Lookup (8-11ms)
                │   └─ Result: CACHE_MISS
                ├─ Span 6: HNSW Search (11-14ms)
                │   └─ Result: 100 vectors found
                └─ Span 7: Response Serialization (14-15ms)

Total Latency: 15ms (p50 target: <10ms) ⚠️ SLOW

7.3 Alerting Rules

Critical Alerts (PagerDuty):

alerts:
  - name: RegionDown
    condition: region_availability < 95%
    severity: critical
    notification: immediate

  - name: LatencyDegraded
    condition: p99_latency > 50ms for 5 min
    severity: critical
    notification: immediate

  - name: ErrorRateHigh
    condition: error_rate > 1% for 5 min
    severity: critical
    notification: immediate

  - name: CapacityExhausted
    condition: instance_count > 90% of max
    severity: warning
    notification: 15 min delay
    auto_remediation: scale_up

8. Disaster Recovery & Failover

8.1 Failure Scenarios

Regional Failure:

Scenario: us-central1 becomes unavailable

Automatic Response (< 30s):
1. Global LB detects unhealthy region (health checks fail)
2. Traffic re-routes to nearby regions:
   - East Coast: us-east1
   - West Coast: us-west1
3. Spillover regions scale up 2x capacity (auto-scaling)
4. CDN cache serves stale content (5 min grace period)
5. Alerts sent to on-call team

Manual Response (< 5 min):
1. Confirm outage scope and cause
2. Increase max_instances in spillover regions
3. Warm up additional regions if needed
4. Update status page

Recovery (< 30 min):
1. Region comes back online
2. Gradual traffic shift (10% every 5 min)
3. Verify metrics return to normal
4. Post-mortem analysis

Multi-Region Failure (catastrophic):

Scenario: 3+ regions simultaneously fail

Response:
1. Activate DR runbook
2. Promote standby clusters to active
3. Scale remaining healthy regions to 150% capacity
4. Enable aggressive caching (10 min TTL)
5. Activate read-only mode for non-critical operations
6. Coordinate with GCP support for expedited recovery

8.2 Backup & Recovery

Data Backup Strategy:

backups:
  vector_data:
    frequency: continuous (Cloud Storage versioning)
    retention: 30 days
    storage_class: nearline

  metadata:
    frequency: every 6 hours (Cloud SQL automated backups)
    retention: 7 days
    point_in_time_recovery: enabled

  configuration:
    frequency: on change (Git repository)
    retention: indefinite

recovery_objectives:
  rpo: <1 hour (maximum data loss)
  rto: <30 min (maximum downtime)

9. Security & Compliance

9.1 Security Architecture

┌─────────────────────────────────────────────────────┐
│  Perimeter Security                                 │
│  - Cloud Armor (DDoS protection, WAF)               │
│  - SSL/TLS 1.2+ (Google-managed certificates)       │
│  - Rate limiting (100 req/s per IP)                 │
└─────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────┐
│  Authentication & Authorization                      │
│  - OAuth 2.0 / JWT tokens                           │
│  - API keys with scoped permissions                 │
│  - Workload Identity (service-to-service)           │
└─────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────┐
│  Network Security                                    │
│  - VPC Service Controls                             │
│  - Private Service Connect (Redis, SQL)             │
│  - VPC Peering (cross-region)                       │
│  - Cloud NAT (egress only for Cloud Run)            │
└─────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────┐
│  Data Security                                       │
│  - Encryption at rest (CMEK for sensitive data)     │
│  - Encryption in transit (TLS 1.2+)                 │
│  - Customer-managed encryption keys (optional)      │
│  - Data residency controls (regional isolation)     │
└─────────────────────────────────────────────────────┘

9.2 Compliance

Certifications & Standards:

SOC 2 Type II
ISO 27001
GDPR compliant (data residency in EU for EU users)
HIPAA compliant (for healthcare use cases)
PCI DSS Level 1 (for payment-related vectors)

10. Integration with Agentic-Flow

10.1 Coordination Architecture

Agentic-Flow Integration:

// Example: Distributed agent coordination via ruvector

const { AgenticFlow } = require('agentic-flow');
const { VectorDB } = require('ruvector');

// Initialize distributed vector memory
const flow = new AgenticFlow({
  vectorStore: new VectorDB({
    endpoint: 'https://ruvector.example.com',
    region: 'auto',  // auto-selects nearest region
    streaming: true,
  }),
  topology: 'mesh',
  coordinationHooks: {
    preTask: async (task) => {
      // Store task embedding for similarity search
      const embedding = await embedTask(task);
      await flow.vectorStore.insert(task.id, embedding, {
        metadata: { type: 'task', status: 'pending' }
      });
    },
    postTask: async (task, result) => {
      // Update task with result
      await flow.vectorStore.update(task.id, {
        metadata: { status: 'completed', result }
      });
    }
  }
});

// Distributed agent search for similar tasks
async function findSimilarTasks(currentTask) {
  const stream = flow.vectorStore.searchStream(
    currentTask.embedding,
    { k: 10, filter: { type: 'task' } }
  );

  for await (const result of stream) {
    console.log(`Similar task: ${result.id}, score: ${result.score}`);
  }
}

10.2 Pub/Sub Coordination

Cross-Region Agent Coordination:

pubsub_topics:
  agent-coordination:
    regions: all
    message_retention: 7 days
    ordering_key: agent_id

  task-distribution:
    regions: all
    message_retention: 1 day
    ordering_key: task_priority

  vector-updates:
    regions: all
    message_retention: 1 hour
    ordering_key: vector_id

11. Next Steps

11.1 Implementation Phases

Phase 1: Foundation (Weeks 1-4)

Deploy to 3 pilot regions (us-central1, europe-west1, asia-northeast1)
Baseline capacity: 30M concurrent streams
Load testing and optimization

Phase 2: Global Expansion (Weeks 5-8)

Deploy to all 15 regions
Enable cross-region replication
Capacity: 100M concurrent streams

Phase 3: Optimization (Weeks 9-12)

Fine-tune auto-scaling policies
Optimize cache hit rates
Enable advanced features (predictive scaling)
Capacity: 300M concurrent streams

Phase 4: Full Scale (Weeks 13-16)

Scale to 500M concurrent streams
Burst testing (10-50x load)
Disaster recovery drills
Production readiness review

11.2 Success Metrics

Technical Metrics:

✅ p50 latency: <10ms
✅ p99 latency: <50ms
✅ Availability: 99.99%
✅ Concurrent streams: 500M+
✅ Burst capacity: 10-50x baseline

Business Metrics:

Cost per million requests: <$5
Infrastructure cost as % of revenue: <15%
Time to scale (0→500M): <30 minutes
Mean time to recovery (MTTR): <30 minutes

Appendix A: Reference Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────┐
│                                                                         │
│                          GLOBAL INTERNET                                │
│                                                                         │
└────────────────────────────────┬────────────────────────────────────────┘
                                 │
                                 │ Anycast IPv4/IPv6
                                 ↓
┌─────────────────────────────────────────────────────────────────────────┐
│                     GOOGLE CLOUD GLOBAL LOAD BALANCER                   │
│  • Single global IP address                                             │
│  • SSL/TLS termination                                                  │
│  • DDoS protection (Cloud Armor)                                        │
│  • Geo-routing (proximity-based)                                        │
└───┬─────────────────────┬───────────────────────┬─────────────────────┬─┘
    │                     │                       │                     │
    ↓                     ↓                       ↓                     ↓
┌───────────┐      ┌───────────┐         ┌───────────┐         ┌───────────┐
│ Americas  │      │  Europe   │         │Asia-Pacific│        │MENA/Africa│
│ 5 Regions │      │ 4 Regions │         │ 5 Regions │         │ 1 Region  │
│ 180M      │      │ 120M      │         │ 180M      │         │ 20M       │
│ streams   │      │ streams   │         │ streams   │         │ streams   │
└─────┬─────┘      └─────┬─────┘         └─────┬─────┘         └─────┬─────┘
      │                  │                     │                     │
      └──────────────────┴─────────────────────┴─────────────────────┘
                                 │
                     ┌───────────┴───────────┐
                     │                       │
                     ↓                       ↓
          ┌──────────────────┐    ┌──────────────────┐
          │  Cloud CDN Edge  │    │  Regional Stack  │
          │  120+ Locations  │    │  (per region)    │
          │  • Cache: 60-70% │    │                  │
          │  • Latency: 5ms  │    │  ┌────────────┐  │
          └──────────────────┘    │  │ Cloud Run  │  │
                                  │  │ 500-5000   │  │
                                  │  │ instances  │  │
                                  │  └────────────┘  │
                                  │  ┌────────────┐  │
                                  │  │Memorystore │  │
                                  │  │ Redis 256GB│  │
                                  │  └────────────┘  │
                                  │  ┌────────────┐  │
                                  │  │Cloud Storage  │
                                  │  │Multi-Region│  │
                                  │  └────────────┘  │
                                  └──────────────────┘

Document Version: 1.0.0 Last Updated: 2025-11-20 Next Review: 2025-12-20 Owner: Infrastructure Team Approval: CTO, VP Engineering

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