AI in the Coral Sea
- AI in the Coral Sea: Server Configuration
This document details the server configuration for the "AI in the Coral Sea" project, a research initiative utilizing artificial intelligence to monitor and analyze the health of the Coral Sea ecosystem. This article is designed for new members of the team and those unfamiliar with the server infrastructure. It covers hardware specifications, software stack, network configuration, and security considerations.
Project Overview
The "AI in the Coral Sea" project involves the deployment of underwater sensors, drones, and satellite imagery analysis. The collected data is processed by a centralized server infrastructure to identify coral bleaching events, track marine life, and detect pollution. This data analysis is performed using machine learning models, requiring significant computational resources. See also: Data Acquisition and Machine Learning Models.
Hardware Configuration
The server infrastructure consists of three primary server types: Database Servers, Application Servers, and Processing Nodes. Each server type is built with specific hardware to optimize its performance.
Database Servers
These servers are responsible for storing and managing the vast amounts of data collected by the sensors and drones. They utilize a clustered database approach for redundancy and scalability.
| Component | Specification |
|---|---|
| CPU | 2 x Intel Xeon Gold 6248R (24 cores/48 threads) |
| RAM | 512 GB DDR4 ECC Registered |
| Storage | 4 x 8TB SAS 12Gbps 7.2K RPM HDD (RAID 10) + 2 x 1TB NVMe SSD (Caching) |
| Network Interface | Dual 10 Gigabit Ethernet |
| Power Supply | Redundant 1600W Platinum |
These servers run PostgreSQL with pgBackRest for backups. See Database Administration for details.
Application Servers
These servers host the web application interface for data visualization and analysis, as well as the API endpoints for data access.
| Component | Specification |
|---|---|
| CPU | 2 x Intel Xeon Silver 4210 (10 cores/20 threads) |
| RAM | 128 GB DDR4 ECC Registered |
| Storage | 2 x 1TB NVMe SSD (RAID 1) |
| Network Interface | Dual 1 Gigabit Ethernet |
| Power Supply | Redundant 850W Gold |
The application servers are built using Python, Flask, and nginx. See Web Application Deployment for more information.
Processing Nodes
These servers are dedicated to running the computationally intensive machine learning models. They utilize powerful GPUs to accelerate the training and inference processes.
| Component | Specification |
|---|---|
| CPU | 2 x AMD EPYC 7763 (64 cores/128 threads) |
| RAM | 256 GB DDR4 ECC Registered |
| GPU | 4 x NVIDIA A100 (80GB) |
| Storage | 2 x 2TB NVMe SSD (RAID 0) |
| Network Interface | Dual 100 Gigabit Ethernet |
| Power Supply | Redundant 2000W Titanium |
These nodes utilize TensorFlow and PyTorch to run the AI models. See GPU Cluster Management for configuration details.
Software Stack
The software stack is built upon a Linux foundation, providing a stable and secure operating environment.
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker and Kubernetes are used for application deployment and orchestration.
- Programming Languages: Python is the primary language for data analysis and machine learning. JavaScript is used for the front-end web application.
- Web Server: nginx serves static content and acts as a reverse proxy.
- Database: PostgreSQL is used for persistent data storage.
- Monitoring: Prometheus and Grafana are used for system monitoring and alerting. See System Monitoring Guide.
- Version Control: Git is used for source code management.
Network Configuration
The server infrastructure is deployed in a private network with restricted access from the public internet.
- Network Topology: A star topology with a central core switch.
- IP Addressing: Static IP addresses are assigned to each server.
- Firewall: iptables is configured to allow only necessary traffic.
- VPN: A OpenVPN server provides secure remote access for authorized personnel. See VPN Configuration.
- DNS: Internal DNS server managed using BIND9.
Security Considerations
Security is paramount for the "AI in the Coral Sea" project. The following security measures are implemented:
- Regular Security Audits: Periodic security audits are conducted to identify and address vulnerabilities.
- Intrusion Detection System (IDS): Snort is used to detect malicious activity.
- Access Control: Strict access control policies are enforced using SSH keys and user authentication.
- Data Encryption: Data is encrypted both in transit and at rest. See Data Encryption Standards.
- Backup and Disaster Recovery: Regular backups are performed and a disaster recovery plan is in place. See Backup and Recovery Procedures.
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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.* ⚠️