AI in the Congo River
- AI in the Congo River: Server Configuration and Deployment
This article details the server infrastructure established to support the "AI in the Congo River" project, a research initiative focused on real-time analysis of riverine ecosystems using artificial intelligence. This document is intended for new team members and those interested in the technical aspects of the deployment. It covers hardware, software, and network considerations.
Project Overview
The "AI in the Congo River" project involves deploying a network of sensors along the Congo River to collect data on water quality, biodiversity, and river flow. This data is then processed in real-time using machine learning algorithms to identify patterns, predict potential environmental changes, and provide insights into the health of the river ecosystem. The core of this system relies on a robust and reliable server infrastructure. Data Acquisition is the first step, followed by Data Processing and finally Data Visualization.
Server Hardware Configuration
The server infrastructure consists of three primary tiers: Edge Servers, Regional Aggregators, and the Central Data Center. Each tier has specific hardware requirements. Due to the challenging environment, redundancy and resilience are paramount.
Edge Servers
Edge servers are deployed directly near sensor clusters, providing localized processing and data aggregation. They must be robust to handle variable power conditions and limited network connectivity.
| Component | Specification |
|---|---|
| Processor | Intel Xeon E-2388G (8 Cores, 3.2 GHz) |
| RAM | 64 GB DDR4 ECC |
| Storage | 2 x 2TB NVMe SSD (RAID 1) |
| Network | 2 x 1 GbE Ethernet |
| Power Supply | Redundant 800W Power Supplies |
| Cooling | Passive Cooling with Convection |
Regional Aggregators
Regional aggregators collect data from multiple edge servers, perform more complex analysis, and forward data to the central data center. These servers require higher processing power and network bandwidth. Network Topology is critical here.
| Component | Specification |
|---|---|
| Processor | Dual Intel Xeon Silver 4310 (12 Cores, 2.1 GHz each) |
| RAM | 128 GB DDR4 ECC |
| Storage | 4 x 4TB NVMe SSD (RAID 10) |
| Network | 4 x 10 GbE Ethernet |
| Power Supply | Redundant 1200W Power Supplies |
| Cooling | Liquid Cooling |
Central Data Center
The central data center hosts the core machine learning models, data storage, and visualization tools. This is the most powerful tier of the infrastructure. Data Security is a major concern at this level.
| Component | Specification |
|---|---|
| Processor | Dual AMD EPYC 7763 (64 Cores, 2.45 GHz each) |
| RAM | 512 GB DDR4 ECC |
| Storage | 16 x 8TB SAS HDD (RAID 6) + 4 x 2TB NVMe SSD (Caching) |
| Network | 4 x 40 GbE Ethernet |
| Power Supply | Redundant 2000W Power Supplies |
| Cooling | Advanced Liquid Cooling System |
Software Configuration
All servers run Ubuntu Server 22.04 LTS. Specific software packages are deployed based on the server tier. Operating System Security is regularly audited.
- **Edge Servers:** Docker containers running lightweight data processing scripts (Python with libraries like Pandas and NumPy), MQTT client for communication with regional aggregators, and basic monitoring tools.
- **Regional Aggregators:** Kubernetes cluster for orchestrating data processing pipelines, PostgreSQL database for storing aggregated data, and Prometheus for monitoring.
- **Central Data Center:** TensorFlow and PyTorch for machine learning model training and inference, Elasticsearch for log analysis, Grafana for data visualization, and a larger PostgreSQL database for long-term data storage. Version Control using Git is essential.
Network Configuration
The network infrastructure is a hybrid solution, utilizing a combination of fiber optic cables and satellite communication. Edge servers connect to regional aggregators via a dedicated wireless network. Regional aggregators connect to the central data center via a high-bandwidth fiber optic link. Satellite communication provides a backup connection in case of fiber optic outages. Firewall Configuration is paramount. VPN Access is restricted to authorized personnel.
Data Flow
1. Sensors collect data and transmit it to the nearest Edge Server. 2. Edge Servers perform initial data cleaning and aggregation. 3. Aggregated data is transmitted to the Regional Aggregator via MQTT. 4. Regional Aggregators perform more complex analysis and store data in PostgreSQL. 5. Data is forwarded to the Central Data Center. 6. The Central Data Center runs machine learning models and provides data visualization through Grafana.
Future Considerations
Future upgrades will include implementing a more robust caching layer, exploring the use of edge computing frameworks, and investigating the potential of using renewable energy sources to power the server infrastructure. Scalability Planning is an ongoing process. Disaster Recovery Planning is also actively maintained.
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 |
Order Your Dedicated Server
Configure and order your ideal server configuration
Need Assistance?
- Telegram: @powervps Servers at a discounted price
⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️