AI in the Great Barrier Reef
- AI in the Great Barrier Reef: Server Configuration
This article details the server configuration supporting the "AI in the Great Barrier Reef" project, a data-intensive initiative focused on coral reef health monitoring and predictive analysis. It’s geared towards newcomers to our MediaWiki environment, outlining the hardware, software, and network infrastructure involved. This project leverages Machine learning to analyze data gathered from underwater sensors and aerial surveys, requiring significant computational resources.
Project Overview
The "AI in the Great Barrier Reef" project uses a combination of data sources including high-resolution imagery from drones, sonar data from AUVs, and environmental data from fixed sensor buoys. This data is processed using deep learning models to identify coral bleaching, disease outbreaks, and changes in reef biodiversity. The system aims to provide early warnings to conservationists, enabling targeted interventions. The core of the system relies on a distributed computing architecture. We utilize a combination of Cloud computing and on-premise servers for optimal performance and data security.
Hardware Configuration
The project's infrastructure is divided into three primary tiers: data ingestion, processing, and serving. Each tier utilizes dedicated hardware.
| Tier | Server Role | Server Count | CPU | RAM | Storage |
|---|---|---|---|---|---|
| Data Ingestion | Edge Servers (Data Collection) | 6 | Intel Xeon Silver 4210R | 64 GB | 4 TB RAID 1 |
| Data Processing | Core Processing Servers | 12 | AMD EPYC 7763 | 256 GB | 16 TB RAID 5 |
| Data Serving | API/Visualization Servers | 4 | Intel Xeon Gold 6248R | 128 GB | 2 TB NVMe SSD |
These servers are housed in a dedicated, climate-controlled server room with redundant power supplies and network connectivity. The server room follows stringent Security protocols to safeguard sensitive data. We also utilize Virtualization technologies, specifically KVM, to maximize resource utilization.
Software Stack
The software stack is designed for scalability, reliability, and ease of maintenance. We prioritize open-source solutions whenever possible.
| Software Category | Software | Version | Purpose |
|---|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS | Server OS |
| Database | PostgreSQL | 14 | Data Storage & Management |
| Machine Learning Framework | TensorFlow | 2.12 | Deep Learning Model Training & Inference |
| Data Pipeline | Apache Kafka | 3.3.1 | Real-time data streaming |
| Web Server | Nginx | 1.23 | Serving API and Static Content |
The machine learning models are developed in Python using libraries like TensorFlow and PyTorch. We employ Docker for containerization, ensuring consistent deployment across different environments. The data pipeline is orchestrated using Kubernetes for automated scaling and management. Regular Software updates are performed to address security vulnerabilities and improve performance.
Network Configuration
The network infrastructure is critical for ensuring high-bandwidth, low-latency communication between the various components of the system.
| Component | Specification | Notes |
|---|---|---|
| Core Network | 100 Gbps Ethernet | Redundant fiber optic links |
| Server Room Switch | Cisco Nexus 9332C | Layer 3 switch with advanced features |
| Firewall | Palo Alto Networks PA-820 | Next-generation firewall with intrusion detection and prevention |
| Load Balancer | HAProxy | Distributes traffic across API servers |
| DNS | BIND9 | Internal and external DNS resolution |
The network is segmented using VLANs to isolate different tiers of the infrastructure. We utilize a Reverse proxy configuration for enhanced security and performance. All network traffic is monitored using Network monitoring tools such as Nagios and Zabbix. The system integrates with the central IT infrastructure for unified management.
Future Considerations
Future upgrades include the implementation of GPU acceleration for faster model training and inference, as well as the exploration of federated learning techniques to enable collaborative model development without sharing sensitive data. We are also investigating the use of Edge computing to perform some data processing closer to the source, reducing latency and bandwidth requirements. This project also uses a Data backup and recovery strategy to ensure data integrity.
Main Page Data analysis Coral reefs Environmental monitoring Distributed computing Database management Network security System administration Server maintenance Performance tuning Disaster recovery Data visualization API development Cloud infrastructure AI applications
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.* ⚠️