AI in the Siberian Wilderness
AI in the Siberian Wilderness: Server Configuration
This article details the server configuration for the "AI in the Siberian Wilderness" project, a remote research initiative utilizing artificial intelligence for environmental monitoring and analysis. This guide is intended for new system administrators joining the project and outlines the hardware, software, and networking setup. Understanding these details is crucial for maintaining system stability and facilitating ongoing research. This project relies heavily on Distributed Computing and Edge Computing principles due to the remote location.
Overview
The project utilizes a tiered server architecture. The primary server, located in a hardened, climate-controlled facility in Novosibirsk, Russia, handles data aggregation, model training, and central management. Remote "edge" servers, deployed at specific research sites within the Siberian wilderness, collect data from sensors and perform initial data processing using lightweight AI models. Communication between the edge servers and the central server is handled via a combination of Satellite Communication and Long-Range Wireless Communication. Robust Data Backup and Disaster Recovery procedures are paramount given the environmental challenges.
Central Server Configuration (Novosibirsk)
The central server is the core of the system, responsible for computationally intensive tasks. It's built for high availability and scalability.
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Xeon Gold 6338 (32 cores, 64 threads) | 2 |
| RAM | 512 GB DDR4 ECC Registered | 1 |
| Storage | 4 x 16TB SAS 7.2k RPM HDD (RAID 10) | 1 |
| Storage (SSD) | 2 x 4TB NVMe PCIe Gen4 SSD (RAID 1 - OS & Databases) | 1 |
| GPU | NVIDIA RTX A6000 (48GB GDDR6) | 2 |
| Network Interface | Dual Port 100 Gigabit Ethernet | 1 |
| Power Supply | 2 x 1600W Redundant Power Supplies | 1 |
The operating system is Ubuntu Server 22.04 LTS. Key software components include PostgreSQL for data storage, Python 3.10 for AI model development and deployment, TensorFlow and PyTorch for machine learning frameworks, and Kubernetes for container orchestration. The server is monitored using Prometheus and Grafana for real-time performance analysis. SSH Access is strictly controlled using key-based authentication. The server's firewall is configured using iptables with a highly restrictive policy.
Edge Server Configuration (Remote Sites)
Edge servers are deployed in ruggedized enclosures to withstand extreme temperatures and environmental conditions. They are designed for low power consumption and autonomous operation.
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Core i5-12400 (6 cores, 12 threads) | 1 |
| RAM | 32 GB DDR4 | 1 |
| Storage | 1TB NVMe PCIe Gen3 SSD | 1 |
| Network Interface | 4G/5G Cellular Modem + WiFi 6 | 1 |
| Power Supply | 12V DC Input (Solar/Battery Powered) | 1 |
| Operating System | Debian 11 (Minimal Installation) | 1 |
These servers run a streamlined version of Python 3.9 with TensorFlow Lite for running optimized AI models. Data is pre-processed locally, and only relevant information is transmitted to the central server. rsync is used for periodic data synchronization. Remote management is achieved through a secure VPN connection and a lightweight Webmin interface. A key component of the edge server configuration is the Watchdog Timer, which automatically reboots the system in case of a software failure.
Networking and Communication
The communication infrastructure is critical for the success of the project.
| Connection Type | Bandwidth | Latency (Approx.) | Reliability |
|---|---|---|---|
| Satellite Link (Central Server) | 10 Mbps Down / 2 Mbps Up | 600-800ms | Moderate (Weather Dependent) |
| Long-Range Wireless (Edge-to-Edge) | 50 Mbps | < 50ms | High (Line of Sight Required) |
| Cellular (Edge Server) | Variable (2G/3G/4G/5G) | 50-200ms | Moderate (Coverage Dependent) |
| VPN (Remote Access) | Variable (Dependent on Connection) | Variable | High (Encrypted) |
The central server maintains a persistent SSH Tunnel to each edge server for secure access. DNS Resolution is managed internally to ensure consistent naming across the network. Network monitoring uses Nagios to alert administrators to connectivity issues. The project also employs a custom Message Queue system built on RabbitMQ for asynchronous communication between servers, particularly for handling sensor data streams.
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.* ⚠️