AI in Greenland
- AI in Greenland: Server Configuration & Deployment
This article details the server configuration for the "AI in Greenland" project, a research initiative focused on analyzing climate data using artificial intelligence. This guide is intended for new system administrators and engineers contributing to the project. It covers hardware, software, networking, and security considerations.
Project Overview
The "AI in Greenland" project requires significant computational resources to process large datasets from satellite imagery, ice core samples, and weather stations. The primary goal is to develop AI models capable of predicting glacial melt rates and understanding the impacts of climate change on the Greenland ice sheet. The server infrastructure is designed for scalability, reliability, and data security.
Hardware Configuration
The server infrastructure consists of three tiers: ingestion, processing, and storage. Each tier utilizes specialized hardware optimized for its respective task.
| Tier | Server Role | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|---|
| Ingestion | Data Acquisition & Preprocessing | 2 x Intel Xeon Silver 4310 | 128 GB DDR4 ECC | 2 x 4TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Processing | AI Model Training & Inference | 4 x NVIDIA A100 GPUs, 2 x AMD EPYC 7763 | 512 GB DDR4 ECC | 1 x 8TB NVMe SSD (OS), 2 x 16TB HDD (Scratch) | 100 Gbps InfiniBand |
| Storage | Long-Term Data Archiving | - | 64 GB DDR4 ECC | 12 x 18TB SATA HDD (RAID 6) | 40 Gbps Ethernet |
All servers are housed in a secure data center in Nuuk, Greenland, with redundant power and cooling systems. The data center utilizes a UPS system and a backup generator to ensure continuous operation during power outages.
Software Stack
The software stack is built upon a Linux foundation, utilizing open-source tools wherever possible.
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker and Kubernetes are used for application deployment and orchestration.
- Programming Languages: Python, R, and C++ are the primary languages used for AI model development.
- AI Frameworks: TensorFlow, PyTorch, and scikit-learn are utilized for machine learning tasks.
- Data Storage: Ceph is employed as a distributed object storage system.
- Database: PostgreSQL is used for metadata management and data cataloging.
- Monitoring: Prometheus and Grafana provide real-time system monitoring and alerting.
Networking Configuration
The network infrastructure is designed for high bandwidth and low latency, essential for transferring large datasets.
| Component | Description | IP Address Range |
|---|---|---|
| Core Router | Connects the data center to the external internet. | 192.168.1.0/24 |
| Ingestion Servers | Handles incoming data streams. | 10.0.0.0/24 |
| Processing Servers | Runs AI model training and inference. | 10.1.0.0/24 |
| Storage Servers | Provides long-term data storage. | 10.2.0.0/24 |
| Management Network | Dedicated network for server administration. | 172.16.0.0/24 |
A firewall is configured to restrict access to the server infrastructure, allowing only authorized traffic. VPN access is provided for remote administration. Network segmentation is implemented to isolate different tiers of the infrastructure.
Security Considerations
Security is paramount, given the sensitive nature of the climate data.
- Access Control: Role-Based Access Control (RBAC) is implemented to restrict access to data and resources.
- Data Encryption: Data is encrypted both in transit and at rest using TLS/SSL and AES-256 encryption.
- Intrusion Detection: IDS/IPS systems are deployed to detect and prevent malicious activity.
- Regular Backups: Data is backed up regularly to a geographically diverse location.
- Vulnerability Scanning: Servers are regularly scanned for vulnerabilities using Nessus and other security tools.
- Multi-Factor Authentication: MFA is required for all administrative access.
Scalability and Future Expansion
The infrastructure is designed to be scalable to accommodate future growth in data volume and computational demands. Horizontal scaling is achieved through Kubernetes, allowing us to easily add more processing nodes as needed. We anticipate adding additional storage capacity and upgrading the network infrastructure to 200 Gbps InfiniBand in the next phase of the project. The Ceph storage cluster is designed to be easily expandable.
| Area | Current Capacity | Projected Expansion |
|---|---|---|
| Processing Nodes | 4 | 8 |
| Storage Capacity | 288 TB | 576 TB |
| Network Bandwidth | 100 Gbps InfiniBand | 200 Gbps InfiniBand |
Related Links
- Data Acquisition Protocols
- AI Model Deployment Guide
- Ceph Cluster Administration
- Kubernetes Configuration Management
- Security Incident Response Plan
- Server Monitoring Dashboard
- Backup and Recovery Procedures
- Network Topology Diagram
- GPU Driver Installation
- PostgreSQL Database Schema
- Firewall Ruleset
- VPN Configuration
- User Account Management
- Software Licensing Information
- Hardware Inventory
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️