AI in the Ganges River
- AI in the Ganges River: Server Configuration
This article details the server configuration supporting the "AI in the Ganges River" project, a research initiative focused on utilizing artificial intelligence to monitor and analyze the health of the Ganges River. This guide is intended for newcomers to our MediaWiki site and provides a technical overview of the infrastructure.
Project Overview
The "AI in the Ganges River" project employs a network of sensors deployed along the river, collecting data on water quality, pollution levels, and biodiversity. This data is processed using machine learning algorithms to identify trends, predict potential issues, and provide insights for conservation efforts. The server infrastructure is designed for scalability, reliability, and efficient data processing. Data Acquisition is a critical first step. The project leverages Edge Computing for initial data filtering. See also Data Storage Solutions.
Server Architecture
The server infrastructure is a distributed system comprising three primary tiers:
1. **Data Ingestion Tier:** Responsible for receiving data from the sensors and performing initial validation. 2. **Processing Tier:** Executes the machine learning algorithms and analyzes the data. 3. **Presentation Tier:** Provides a user interface for visualizing the data and accessing the results. User Interface Design is paramount.
These tiers are interconnected via a high-bandwidth, low-latency network. Network Topology is detailed in a separate document.
Data Ingestion Servers
These servers are responsible for receiving data streams from the sensor network. They are configured to handle high volumes of data and perform basic error checking.
| Server Name | Operating System | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|---|
| ingestion-01 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6248R (24 cores) | 64 GB DDR4 ECC | 2 x 2 TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| ingestion-02 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6248R (24 cores) | 64 GB DDR4 ECC | 2 x 2 TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
The ingestion servers utilize Kafka as a message queue to buffer incoming data and ensure reliable delivery to the processing tier. Message Queuing Systems are essential for this process.
Processing Servers
These servers are the core of the AI system, running the machine learning models and performing data analysis. They require significant computational resources.
| Server Name | Operating System | CPU | RAM | GPU | Storage | Network Interface |
|---|---|---|---|---|---|---|
| processing-01 | CentOS Stream 9 | AMD EPYC 7763 (64 cores) | 128 GB DDR4 ECC | NVIDIA A100 (80GB) | 4 x 4 TB NVMe SSD (RAID 10) | 100 Gbps Ethernet |
| processing-02 | CentOS Stream 9 | AMD EPYC 7763 (64 cores) | 128 GB DDR4 ECC | NVIDIA A100 (80GB) | 4 x 4 TB NVMe SSD (RAID 10) | 100 Gbps Ethernet |
| processing-03 | CentOS Stream 9 | AMD EPYC 7763 (64 cores) | 128 GB DDR4 ECC | NVIDIA A100 (80GB) | 4 x 4 TB NVMe SSD (RAID 10) | 100 Gbps Ethernet |
The processing servers utilize Kubernetes for container orchestration and resource management. Containerization is vital for reproducibility and scalability. We also employ TensorFlow and PyTorch for model training and inference.
Presentation Servers
These servers host the web application that provides access to the data and results of the AI analysis.
| Server Name | Operating System | CPU | RAM | Storage | Web Server | Network Interface |
|---|---|---|---|---|---|---|
| presentation-01 | Debian 11 | Intel Core i7-12700K (12 cores) | 32 GB DDR5 | 1 x 1 TB NVMe SSD | Nginx | 10 Gbps Ethernet |
| presentation-02 | Debian 11 | Intel Core i7-12700K (12 cores) | 32 GB DDR5 | 1 x 1 TB NVMe SSD | Nginx | 10 Gbps Ethernet |
The presentation servers utilize a PostgreSQL database to store metadata and user information. Database Management is a key operational task. The front end is built using React.
Software Stack
The following software components are used throughout the infrastructure:
- Operating Systems: Ubuntu Server, CentOS Stream, Debian
- Containerization: Docker, Kubernetes
- Message Queue: Kafka
- Machine Learning Frameworks: TensorFlow, PyTorch
- Database: PostgreSQL
- Web Server: Nginx
- Programming Languages: Python, JavaScript
Security Considerations
Security is a paramount concern. All servers are protected by firewalls and intrusion detection systems. Regular security audits are conducted. Security Protocols are strictly enforced. Data is encrypted both in transit and at rest. Data Encryption is a standard practice.
Future Expansion
We plan to expand the infrastructure to accommodate increasing data volumes and more complex machine learning models. This will involve adding more processing servers and increasing the capacity of the data storage systems. Scalability Planning is ongoing.
System Monitoring is crucial for maintaining optimal performance. Backup and Recovery procedures are in place to ensure data integrity.
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.* ⚠️