AIOS DNA

UAICP_ARCHITECTURE

UAICP Architecture Documentation

Overview

The Universal AI Control Plane (UAICP) is the top-level orchestrator for planetary-scale AI operations in AIOSx. It coordinates all 6 core functions to provide global orchestration, multi-cloud abstraction, GPU arbitration, multi-model routing, cross-domain scheduling, global ROI optimization, autonomous self-healing, and reliability/SLO governance.

Core Functions

1. Routing Engine

Purpose: Sends tasks to correct domain kernel.

Location: aiosx/uaicp/routing_engine.py

Features:

  • Extends existing mesh orchestrator with global context awareness
  • Domain-aware task routing
  • Cross-domain workflow coordination
  • Cloud, GPU, and cost-aware routing decisions

Integration: Extends aiosx/mesh/orchestrator.py

2. Execution Planner

Purpose: Splits tasks across cloud/edge/factory/home.

Location: aiosx/uaicp/execution_planner.py

Features:

  • Multi-location execution planning
  • Strategy selection (single, parallel, sequential, distributed)
  • Cost and latency estimation
  • Location type selection (cloud, edge, factory, home)

3. Resource Arbiter

Purpose: Chooses GPUs/CPU/models based on cost/performance.

Location: aiosx/uaicp/resource_arbiter.py

Features:

  • GPU resource selection (NVIDIA, AMD, Intel)
  • CPU resource allocation
  • Model selection optimization
  • Cost/performance balancing

4. Reliability Manager

Purpose: Monitors health, triggers self-healing.

Location: aiosx/uaicp/reliability_manager.py

Features:

  • Global health monitoring across all domains and clouds
  • Cross-domain self-healing coordination
  • Health aggregation by domain and cloud
  • Critical health detection and escalation

Integration: Extends aiosx/health/healing/healing_engine.py

5. Optimization Loop

Purpose: AKO multi-armed bandit tuning.

Location: aiosx/uaicp/optimization_loop.py

Features:

  • Coordinates optimization across all domains
  • Integration with AKO controller
  • Optimization cycle tracking
  • Performance improvement estimation

Integration: Integrates with aiosx/ako/controller.py

6. ROI Engine

Purpose: Decides workload layout based on value impact.

Location: aiosx/uaicp/roi_engine.py

Features:

  • ROI calculation for workloads
  • Business value assessment
  • Workload placement recommendations
  • Cost/revenue tracking

Multi-Cloud Orchestration

Cloud Providers

Location: aiosx/uaicp/cloud/

Supported Providers:

  • AWS (aws_provider.py)
  • Azure (azure_provider.py)
  • GCP (gcp_provider.py)
  • CoreWeave (coreweave_provider.py)
  • Private Datacenters (private_datacenter_provider.py)

Features:

  • Unified cloud provider abstraction
  • Resource provisioning and deprovisioning
  • Cost tracking
  • Region management

Workload Router

Location: aiosx/uaicp/cloud/workload_router.py

Routes workloads to optimal cloud based on:

  • Cost constraints
  • Latency requirements
  • Availability
  • Region preferences

GPU Optimization

GPU Providers

Location: aiosx/uaicp/gpu/

Supported Providers:

  • NVIDIA (nvidia_provider.py) - CUDA, TensorRT
  • AMD (amd_provider.py) - ROCm
  • Intel (intel_provider.py) - oneAPI

Features:

  • GPU resource management
  • Performance benchmarking
  • Chip-level performance comparison
  • Cost/performance optimization

GPU Arbitrator

Location: aiosx/uaicp/gpu/gpu_arbitrator.py

Compares chip-level performance and routes workloads to optimal GPUs based on:

  • Performance requirements
  • Cost constraints
  • Memory requirements
  • Availability

Integration Points

With Existing Systems

  1. Mesh Orchestrator: Extended for global routing
  2. AKO Controller: Integrated for optimization decisions
  3. Self-Healing Engine: Extended for global healing coordination
  4. SLO Monitor: Extended for global SLO compliance
  5. Kernel Registry: Used for kernel discovery and routing

Configuration

UAICP Configuration

File: config/uaicp.yaml

Configuration for:

  • Routing policies
  • Execution strategies
  • Resource preferences
  • Reliability thresholds
  • Optimization settings
  • ROI tracking

Cloud Provider Configuration

File: config/cloud_providers.yaml

Configuration for:

  • Cloud provider credentials
  • Available regions
  • Provider-specific settings

GPU Provider Configuration

File: config/gpu_providers.yaml

Configuration for:

  • GPU provider credentials
  • Available GPU models
  • Capability settings

API Endpoints

All UAICP endpoints are available under /uaicp/:

  • GET /uaicp/overview - Global system overview
  • GET /uaicp/clouds - List cloud providers
  • GET /uaicp/gpus - List GPU resources
  • POST /uaicp/workloads/route - Route workload
  • GET /uaicp/roi - ROI metrics
  • GET /uaicp/global-health - Global health status
  • GET /uaicp/policies - List policies
  • POST /uaicp/policies - Create/update policy

Usage Example

python
from aiosx.uaicp.uaicp_controller import UAICPController


# Initialize UAICP (done automatically on startup)
# Access via API or directly


# Route and execute a task
result = await uaicp_controller.route_and_execute(
    task_id="task_123",
    task_type="llm_inference",
    domain="llm",
    requirements={
        "gpu_required": True,
        "latency_slo_ms": 500,
        "cost_constraint": 5.0,
    }
)

Architecture Diagram

┌─────────────────────────────────────────┐
│      UAICP Controller                  │
│  (Coordinates all 6 functions)        │
└─────────────────────────────────────────┘
           │
           ├─── Routing Engine ───> Mesh Orchestrator
           ├─── Execution Planner ───> Cloud/Edge/Factory/Home
           ├─── Resource Arbiter ───> GPU/CPU/Model Selection
           ├─── Reliability Manager ───> Self-Healing Engine
           ├─── Optimization Loop ───> AKO Controller
           └─── ROI Engine ───> Value-Based Scheduling

Future Enhancements

  • Advanced ML-based routing
  • Predictive resource allocation
  • Cross-cloud workload migration
  • Real-time cost optimization
  • Advanced GPU benchmarking
  • Multi-model ensemble routing

Was this helpful?