AI in the Tuvalu Rainforest
# AI in the Tuvalu Rainforest: Server Configuration
This article details the server configuration supporting the "AI in the Tuvalu Rainforest" project. This project utilizes artificial intelligence to analyze biodiversity data collected from remote sensors deployed within the Tuvalu Rainforest. This document is intended for new system administrators and developers contributing to the infrastructure.
Project Overview
The “AI in the Tuvalu Rainforest” project aims to monitor and understand the unique ecosystem of the Tuvalu Rainforest using a network of embedded sensors and edge computing devices. Data collected, including audio recordings, thermal images, and environmental readings, is processed initially at the edge, then aggregated and analyzed by a central server cluster. The central servers run machine learning models for species identification, anomaly detection (e.g., illegal logging), and long-term trend analysis. Data Acquisition is handled by specialized hardware. Sensor Network Topology details the arrangement of physical sensors.
Server Hardware Specifications
The project utilizes a cluster of four dedicated servers, each with specific roles. Below are the specifications for each server.
| Server Role | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Primary AI Processing (ai-primary) | 2 x Intel Xeon Gold 6338 | 256 GB DDR4 ECC | 4 x 4TB NVMe SSD (RAID 10) | 100 Gbps Ethernet |
| Secondary AI Processing (ai-secondary) | 2 x Intel Xeon Silver 4310 | 128 GB DDR4 ECC | 2 x 4TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Database Server (db-primary) | 1 x AMD EPYC 7763 | 128 GB DDR4 ECC | 8 x 8TB SAS HDD (RAID 6) | 10 Gbps Ethernet |
| Data Storage & Backup (storage-primary) | 2 x Intel Xeon Bronze 3430 | 64 GB DDR3 ECC | 16 x 16TB SATA HDD (RAID 6) | 25 Gbps Ethernet |
These servers are housed in a secure, climate-controlled data center with redundant power supplies and network connectivity. Data Center Security Protocols outlines the physical security measures.
Software Configuration
The servers run Ubuntu Server 22.04 LTS. The primary AI processing server utilizes the following software stack:
- Python 3.10
- TensorFlow 2.12
- PyTorch 2.0
- CUDA Toolkit 11.8
- cuDNN 8.6
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The database server runs PostgreSQL 14 with the PostGIS extension for geospatial data management. Database Schema Documentation details the database structure. The storage server utilizes Ceph for distributed storage and data replication. Ceph Cluster Configuration outlines the storage setup.
Network Configuration
The server cluster is connected to the institution’s internal network via a dedicated VLAN. The following table outlines the key network settings:
| Server | IP Address | Subnet Mask | Gateway |
|---|---|---|---|
| ai-primary | 192.168.10.10 | 255.255.255.0 | 192.168.10.1 |
| ai-secondary | 192.168.10.11 | 255.255.255.0 | 192.168.10.1 |
| db-primary | 192.168.10.12 | 255.255.255.0 | 192.168.10.1 |
| storage-primary | 192.168.10.13 | 255.255.255.0 | 192.168.10.1 |
Firewall rules are configured using `ufw` to restrict access to necessary ports only. Firewall Ruleset provides a comprehensive list of allowed connections.
Monitoring and Alerting
The server cluster is monitored using Prometheus and Grafana. Key metrics, including CPU usage, memory usage, disk I/O, and network traffic, are collected and visualized in Grafana dashboards. Grafana Dashboard Links provides access to the monitoring dashboards. Alerts are configured to notify administrators of critical events, such as high CPU usage or disk space exhaustion. Alerting Configuration Details details the alert thresholds. We also use Log Analysis Tools to monitor server logs.
Security Considerations
Security is paramount for this project. All servers are regularly patched with the latest security updates. SSH access is restricted to authorized users only, and key-based authentication is enforced. A robust backup and disaster recovery plan is in place to ensure data availability. Backup and Recovery Procedures details the data backup schedule. We also employ Intrusion Detection Systems.
Future Expansion
As the project evolves and data volumes increase, we anticipate expanding the server cluster with additional nodes. Future considerations include utilizing GPU acceleration for more complex AI models and implementing a distributed database solution. Scalability Planning Documents outlines potential expansion strategies. Hardware Procurement Process details the process for acquiring new hardware.
Main Page Project Documentation Contact Information Troubleshooting Guide API Documentation
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.* ⚠️