AI in the Bermuda Rainforest
AI in the Bermuda Rainforest: Server Configuration
This article details the server configuration for the "AI in the Bermuda Rainforest" project, a research initiative utilizing artificial intelligence to analyze ecological data gathered from the unique ecosystem of Bermuda's rainforests. This document is intended for newcomers to our MediaWiki site and provides a comprehensive overview of the hardware and software infrastructure. It assumes a basic understanding of server administration and Linux operating systems.
Project Overview
The "AI in the Bermuda Rainforest" project involves deploying a network of sensors throughout the rainforest to collect data on temperature, humidity, light levels, soundscapes, and species identification (via automated image and audio analysis). This data is then transmitted to a central server cluster for processing and analysis using machine learning algorithms. The goal is to create a dynamic model of the rainforest ecosystem, allowing for predictive analysis and informed conservation efforts. We utilize a combination of edge computing on the sensor nodes and centralized processing to maximize efficiency.
Hardware Configuration
Our server infrastructure is housed in a secure, climate-controlled data center. The core of the system comprises three primary server types: Data Acquisition Servers, Processing Servers, and Database Servers.
Data Acquisition Servers
These servers handle the initial ingestion of data from the sensor network. They perform basic data validation and pre-processing before forwarding the data to the Processing Servers.
| Specification | Value |
|---|---|
| CPU | Intel Xeon Silver 4310 (12 Cores) |
| RAM | 64 GB DDR4 ECC |
| Storage | 2 x 4TB SATA III HDD (RAID 1) for temporary data buffering. |
| Network Interface | 10 Gigabit Ethernet |
| Operating System | Ubuntu Server 22.04 LTS |
Processing Servers
These servers are responsible for running the machine learning algorithms and performing the bulk of the data analysis. They require significant processing power and memory.
| Specification | Value |
|---|---|
| CPU | 2 x AMD EPYC 7763 (64 Cores each) |
| RAM | 256 GB DDR4 ECC |
| Storage | 4 x 2TB NVMe PCIe Gen4 SSD (RAID 0) for fast data access. |
| GPU | 4 x NVIDIA A100 (80GB) |
| Network Interface | 2 x 10 Gigabit Ethernet |
| Operating System | CentOS 8 Stream |
Database Servers
These servers store the processed data and provide access to the results for researchers and analysts. Data integrity and availability are paramount.
| Specification | Value |
|---|---|
| CPU | Intel Xeon Gold 6338 (32 Cores) |
| RAM | 128 GB DDR4 ECC |
| Storage | 8 x 8TB SAS III HDD (RAID 6) for data redundancy. |
| Network Interface | 10 Gigabit Ethernet |
| Operating System | Rocky Linux 9 |
Software Configuration
The software stack is designed for scalability, reliability, and ease of maintenance. We leverage several open-source technologies to minimize costs and maximize flexibility. Docker and Kubernetes are used for containerization and orchestration.
Data Acquisition Software
- MQTT Broker: Handles communication with the sensor network.
- Node-RED: Used for data flow programming and initial data processing.
- InfluxDB: Time-series database for storing raw sensor data temporarily.
Processing Software
- Python: Primary programming language for machine learning algorithms.
- TensorFlow: Machine learning framework.
- PyTorch: Alternative machine learning framework.
- Jupyter Notebook: Used for data exploration and model development.
- CUDA: NVIDIA's parallel computing platform and API for the GPUs.
Database Software
- PostgreSQL: Relational database for storing processed data and metadata.
- pgAdmin: Administration and development platform for PostgreSQL.
- TimescaleDB: Time-series database built on PostgreSQL, optimized for time-series data.
Network Configuration
The server cluster is connected to the internet via a dedicated 1 Gigabit fiber optic connection. A firewall, managed by iptables, protects the servers from unauthorized access. Internal network traffic is segmented using VLANs to isolate different components of the system. We utilize DNS for name resolution.
Security Considerations
Security is a critical concern. All servers are regularly patched with the latest security updates. Access to the servers is restricted to authorized personnel only, using SSH keys and multi-factor authentication. Data is encrypted both in transit and at rest. Regular backups are performed to ensure data recovery in the event of a disaster. We also employ intrusion detection and prevention systems.
Future Expansion
We plan to expand the server infrastructure in the future to accommodate the growing volume of data and the increasing complexity of the machine learning models. This will involve adding more Processing Servers and upgrading the network infrastructure to 40 Gigabit Ethernet. The addition of a dedicated load balancer is also under consideration. We also plan to implement a more robust monitoring system using tools like Prometheus and Grafana.
Server Administration Data Analysis Machine Learning Network Security Backup and Recovery Virtualization Cloud Computing Database Management Operating Systems System Monitoring Firewall Configuration Software Updates Disaster Recovery Security Audits
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.* ⚠️