AI in the Kiribati Rainforest
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- AI in the Kiribati Rainforest: Server Configuration
This article details the server configuration used to support the "AI in the Kiribati Rainforest" project. This project utilizes artificial intelligence to analyze data collected from sensors deployed within the rainforest ecosystem of Kiribati, focusing on biodiversity monitoring and climate change impact assessment. This guide is designed for new contributors and system administrators managing the project’s infrastructure. Understanding this configuration is crucial for System Maintenance and Data Analysis.
Project Overview
The "AI in the Kiribati Rainforest" project relies on a distributed server architecture to handle the high volume of data generated by the sensor network. Data is collected via Sensor Networks and transmitted to a central processing cluster for analysis. The AI models, primarily based on Machine Learning, are trained and deployed on this cluster. The project’s goals include identifying species, tracking population changes, and predicting environmental shifts. Data Security is paramount, given the sensitive nature of the ecological data. The system is designed for high availability and scalability, leveraging Virtualization technologies.
Server Hardware Configuration
The core of the system consists of three primary server types: Input Servers, Processing Servers, and Database Servers. Each type has a specialized hardware configuration tailored to its role.
| Server Type | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Input Servers | Intel Xeon Silver 4310 (12 Cores) | 64 GB DDR4 ECC | 2 x 4TB SSD (RAID 1) | 10 Gbps Ethernet |
| Processing Servers | AMD EPYC 7763 (64 Cores) | 256 GB DDR4 ECC | 4 x 8TB NVMe SSD (RAID 0) | 100 Gbps InfiniBand |
| Database Servers | Intel Xeon Gold 6338 (32 Cores) | 128 GB DDR4 ECC | 8 x 16TB SAS HDD (RAID 6) | 25 Gbps Ethernet |
These servers are housed in a climate-controlled Data Center located in Suva, Fiji, to minimize latency and ensure reliable operation. Power redundancy is provided via dual UPS systems and a backup generator. Regular Hardware Monitoring is performed to identify and address potential failures.
Software Stack
The software stack is built around a Linux operating system (Ubuntu Server 22.04 LTS) and utilizes a combination of open-source and proprietary tools. This provides a robust and flexible platform for Software Deployment and Application Management.
| Component | Version | Description |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system for all servers. |
| Database | PostgreSQL 14 | Stores sensor data, model metadata, and analysis results. |
| AI Framework | TensorFlow 2.12 | Used for developing and deploying machine learning models. |
| Programming Language | Python 3.10 | Primary language for data processing and model training. |
| Message Queue | RabbitMQ 3.9 | Facilitates asynchronous communication between servers. |
| Web Server | Nginx 1.23 | Serves the project’s web interface and API. |
The servers are managed using Configuration Management tools like Ansible for automated provisioning and updates. Version Control is handled through Git, with code repositories hosted on a private GitLab instance. Security Audits are performed regularly to identify and mitigate vulnerabilities.
Network Configuration
The network is segmented into three zones: Public, DMZ, and Private. Input servers reside in the DMZ, providing a secure entry point for data from the sensor network. Processing and Database servers are located in the Private zone, protected by a firewall. Network Security is a critical aspect of the overall system design.
| Zone | IP Range | Access Control |
|---|---|---|
| Public | 203.0.113.0/24 | Limited access, restricted to necessary ports. |
| DMZ | 192.168.1.0/24 | Access to Public and Private zones via firewall rules. |
| Private | 10.0.0.0/16 | Restricted access, only allowed from authorized servers. |
All communication between servers is encrypted using TLS/SSL. Intrusion Detection Systems are deployed to monitor for malicious activity. Regular Network Monitoring is performed to ensure optimal performance and availability. The project also utilizes a Content Delivery Network to improve access speeds for users accessing the web interface.
Future Enhancements
Planned future enhancements include integrating a Big Data Platform like Apache Spark for faster data processing and exploring the use of Edge Computing to perform some analysis directly on the sensor nodes. We also aim to improve the Data Visualization capabilities of the web interface.
Server Documentation Troubleshooting Guide Contact Support Data Flow Diagram Security Policy
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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.* ⚠️