AI in the Tongan Rainforest
- AI in the Tongan Rainforest: Server Configuration
This article details the server configuration powering the "AI in the Tongan Rainforest" project, a long-term ecological monitoring initiative utilizing artificial intelligence for data analysis. This guide is intended for new contributors to the MediaWiki site and provides a technical overview of the infrastructure. Understanding this configuration is crucial for development, maintenance, and troubleshooting.
Project Overview
The "AI in the Tongan Rainforest" project employs a network of sensor nodes collecting data on biodiversity, climate, and environmental changes within the rainforest. This data is transmitted to a central server for processing and analysis using machine learning algorithms. The project focuses on identifying species via audio analysis, monitoring forest health through image recognition, and predicting potential environmental risks using time series analysis. This requires significant computational resources and a robust, reliable server infrastructure. See also Data Acquisition and Data Storage.
Server Hardware Specifications
The primary server, nicknamed "Moana", is housed in a secure, climate-controlled data center in Nuku'alofa. It serves as the central hub for data processing, model training, and data dissemination. The following table details the key hardware components:
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Xeon Gold 6248R (24 cores) | 2 |
| RAM | 256 GB DDR4 ECC Registered | 1 |
| Storage (OS) | 512 GB NVMe SSD | 1 |
| Storage (Data) | 64 TB SAS HDD (RAID 6) | 1 Array |
| Network Interface | 10 Gigabit Ethernet | 2 |
| Power Supply | 1600W Redundant Power Supplies | 2 |
This configuration provides ample processing power and storage capacity for the current project demands, with scalability in mind for future expansion. Refer to the Hardware Inventory for detailed asset tracking.
Software Stack
The server operates on a Linux-based operating system and utilizes a variety of software tools for data management, machine learning, and web serving. We chose Ubuntu Server 22.04 LTS for its stability and extensive package availability.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Server OS |
| Database | PostgreSQL 14 | Data Storage & Management |
| Machine Learning Framework | TensorFlow 2.12 | Model Training & Inference |
| Web Server | Apache 2.4 | Serving Web Applications & APIs |
| Programming Language | Python 3.10 | Scripting & Data Processing |
| Data Visualization | Matplotlib 3.7 | Creating Charts and Graphs |
All software is regularly updated to ensure security and stability. See Software Updates for our patching schedule. The API Documentation details how to access the project’s data.
Network Configuration
The server is connected to the internet via a dedicated 10 Gigabit Ethernet connection. A firewall, configured with iptables, protects the server from unauthorized access. The network is segmented into distinct zones for security:
- **Public Zone:** Accessible from the internet (limited to specific ports for web access).
- **Internal Zone:** Used for communication between server components and other internal systems.
- **Data Zone:** Restricted access to the data storage array.
The following table summarizes key network settings:
| Setting | Value |
|---|---|
| IP Address | 192.168.1.10 |
| Subnet Mask | 255.255.255.0 |
| Gateway | 192.168.1.1 |
| DNS Servers | 8.8.8.8, 8.8.4.4 |
| Firewall | iptables |
Detailed network diagrams are available in the Network Documentation. Please review the Security Protocols before making any network changes.
Data Backup and Recovery
Regular data backups are crucial for ensuring data integrity and preventing data loss. The server utilizes a combination of full and incremental backups, stored both locally and offsite. Backups are performed daily and tested regularly to ensure recoverability. We use rsync for efficient incremental backups. The backup retention policy is documented in Backup Procedures. Consider reviewing Disaster Recovery Plan as well.
Future Considerations
As the project evolves, the server infrastructure will need to be scaled to accommodate increasing data volumes and more complex machine learning models. Potential future upgrades include:
- Adding more RAM
- Expanding storage capacity
- Implementing a distributed computing framework (e.g., Apache Spark)
- Deploying GPUs for accelerated machine learning
Server Monitoring is used to track resource utilization and identify potential bottlenecks.
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.* ⚠️