AI in the Antarctic Rainforest
AI in the Antarctic Rainforest: Server Configuration
This article details the server configuration used to support the “AI in the Antarctic Rainforest” project, a research initiative deploying artificial intelligence for real-time environmental monitoring and analysis in a simulated Antarctic rainforest environment within a controlled laboratory setting. This guide is intended for new members of the support team and outlines the hardware, software, and networking components. This project relies heavily on Data analysis and robust server infrastructure.
Project Overview
The “AI in the Antarctic Rainforest” project utilizes a network of sensors collecting data on temperature, humidity, light levels, simulated precipitation, and the behavior of robotic “fauna” representing Antarctic organisms. This data is processed using machine learning algorithms to identify ecological patterns, predict environmental changes, and optimize resource allocation within the simulated environment. The system demands high throughput, low latency, and significant storage capacity. See also Project Goals for a detailed description of the research aims.
Hardware Configuration
The server infrastructure consists of three primary server nodes: a primary data processing server, a secondary backup and redundancy server, and a dedicated database server. Each server utilizes identical hardware configurations for ease of maintenance and scalability.
| Component | Specification | Quantity per Server |
|---|---|---|
| CPU | Intel Xeon Gold 6248R (24 cores, 3.0 GHz) | 1 |
| RAM | 256 GB DDR4 ECC Registered (3200 MHz) | 1 |
| Storage (OS & Applications) | 2 x 1 TB NVMe SSD (RAID 1) | 1 |
| Storage (Data) | 8 x 8 TB SATA HDD (RAID 6) | 1 |
| Network Interface | 10 Gigabit Ethernet | 2 |
| Power Supply | 1200W Redundant Power Supplies | 2 |
The servers are housed in a climate-controlled rack within the laboratory’s dedicated server room. Power redundancy is provided by a UPS (Uninterruptible Power Supply) system. Refer to the Server Room Documentation for detailed information on environmental controls and power management.
Software Stack
The software stack is built around a Linux distribution and includes various tools for data processing, machine learning, and database management. We utilize a containerization strategy for application deployment.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system |
| Containerization | Docker 24.0.5 | Application deployment and isolation |
| Orchestration | Kubernetes 1.27 | Container management and scaling |
| Programming Languages | Python 3.10, R 4.3 | Data analysis and machine learning |
| Machine Learning Frameworks | TensorFlow 2.12, PyTorch 2.0 | Model training and inference |
| Database | PostgreSQL 15 | Data storage and retrieval |
All software is managed using a centralized configuration management system, Ansible, to ensure consistency across all servers. Detailed instructions on using Ansible can be found in the Ansible Playbook Documentation. Security updates are applied automatically using unattended-upgrades.
Networking Configuration
The server network is isolated from the general laboratory network for security reasons. Access is restricted to authorized personnel only. The servers are connected via a dedicated 10 Gigabit Ethernet switch.
| Network Parameter | Value |
|---|---|
| Server IP Addresses | 192.168.10.10, 192.168.10.11, 192.168.10.12 |
| Gateway | 192.168.10.1 |
| DNS Servers | 8.8.8.8, 8.8.4.4 |
| Subnet Mask | 255.255.255.0 |
| Firewall | UFW (Uncomplicated Firewall) |
Firewall rules are configured to allow only necessary traffic, such as SSH access from authorized IP addresses and communication between the server nodes. Detailed firewall rules are documented in the Network Security Policy. Network monitoring is performed using Prometheus and Grafana to ensure optimal performance and identify potential issues. See Monitoring Dashboard Access for more information. Regular network security audits are conducted. The project also utilizes VPN access for remote support.
Data Backup and Recovery
Data backups are performed nightly to a separate, off-site storage location. The backup system utilizes a combination of full and incremental backups to minimize storage space and recovery time. Regular disaster recovery drills are conducted to ensure the effectiveness of the backup and recovery procedures. See Disaster Recovery Plan for details. The primary and secondary servers are configured for automatic failover in case of hardware or software failure.
Future Considerations
Future upgrades to the server infrastructure may include the addition of GPUs for accelerated machine learning and an expansion of the storage capacity. We are also investigating the use of a distributed file system to improve data access performance. Further information can be found in the Future Infrastructure Roadmap.
Server Documentation Data Storage Procedures Security Protocols Troubleshooting Guide Contact Support
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.* ⚠️