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⚖️ arbiter

License: MIT Status: Experimental

Caution

This project is currently experimental. It is not recommended for production use as it involves low-level hardware orchestration and next-gen scheduling algorithms that are still undergoing validation.

Dual nature—combining bare-metal virtualized hardware management (aSHARD VRAM pinning) with quantum-accelerated Kubernetes scheduling.

📖 Overview

arbiter is a specialized orchestration layer designed for high-performance computing environments. It bridges the gap between low-level hardware management and cloud-native scheduling, providing a unified interface for managing virtualized resources with precision.

🏗️ Architecture

---
title: Arbiter System Architecture
---
graph TD
    subgraph CloudNative [Cloud Native Layer]
        WL[AI Workloads] --> K8s([Kubernetes Cluster])
    end

    subgraph Orchestration [Orchestration Layer]
        Arbiter((Arbiter Core))
        QS{{Quantum Scheduler}}
    end

    subgraph Infrastructure [Infrastructure Layer]
        Pinning[aSHARD VRAM Pinning]
        BM[Bare Metal Hardware]
        GPU[GPU Resources]
    end

    K8s <--> Arbiter
    Arbiter <--> QS
    Arbiter <--> Pinning
    Pinning <--> BM
    BM --- GPU

    style Arbiter fill:#f96,stroke-width:4px
    style CloudNative stroke-dasharray: 5 5
    style Orchestration stroke-dasharray: 5 5
    style Infrastructure stroke-dasharray: 5 5
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🚀 Key Features

  • 🏗️ Infrastructure Awareness: Directly manages bare-metal resources for maximum performance.
  • 📍 VRAM Optimization: Uses aSHARD pinning to eliminate GPU memory fragmentation.
  • ⚛️ Next-Gen Scheduling: Leverages quantum-accelerated algorithms for complex Kubernetes workloads.
  • ⚖️ Unified Orchestration: A single control plane for both hardware and cluster-level operations.

🧪 Context

arbiter was created by Igor Holt (AI Architect) as part of the Genesis Conductor Engine. It serves as the resource orchestration layer for AI workloads, bridging low-level hardware management with cloud-native scheduling to ensure optimal utilization of specialized compute resources.

⚖️ License

This project is licensed under the MIT License.