AI in the Yangtze River
AI in the Yangtze River: Server Configuration & Deployment
This document details the server configuration for the "AI in the Yangtze River" project, a long-term initiative focused on environmental monitoring and predictive modeling using artificial intelligence. This guide is aimed at new system administrators and engineers joining the project and provides a detailed overview of the hardware and software deployed. We will cover the server infrastructure, networking, and key software components. This project leverages Distributed Computing principles for optimal performance.
Project Overview
The “AI in the Yangtze River” project utilizes a distributed network of servers to process data from a variety of sensors deployed along the Yangtze River. These sensors collect data on water quality, flow rate, pollutant levels, and weather conditions. The collected data is analyzed using machine learning algorithms to predict potential environmental issues, such as algal blooms or pollution spikes. The project is divided into three core components: data acquisition, data processing, and model deployment. Understanding Data Pipelines is crucial for managing this system. This infrastructure is designed for high availability and scalability using Redundancy.
Server Infrastructure
The server infrastructure is comprised of three tiers: Edge Servers, Processing Servers, and the Central Server.
Edge Servers
These servers are located near the sensor deployments along the Yangtze River. They are responsible for initial data collection, pre-processing, and transmission to the Processing Servers. They are hardened against environmental factors and operate on limited bandwidth connections. Edge server management is covered in Edge Server Maintenance.
| Edge Server Specification | Value |
|---|---|
| CPU | Intel Xeon E3-1220 v6 |
| RAM | 16 GB DDR4 ECC |
| Storage | 512 GB SSD |
| Network | 1 x Gigabit Ethernet |
| Operating System | Ubuntu Server 20.04 LTS |
| Data Pre-processing Software | Python 3.8 with Pandas & NumPy |
Processing Servers
These servers are responsible for the bulk of the data processing, including data cleaning, feature extraction, and model training. They reside in a secure data center and have access to high-bandwidth network connections. We use Server Virtualization to maximize resource use on these machines.
| Processing Server Specification | Value |
|---|---|
| CPU | 2 x Intel Xeon Gold 6248R |
| RAM | 128 GB DDR4 ECC |
| Storage | 2 x 2TB NVMe SSD (RAID 1) |
| Network | 2 x 10 Gigabit Ethernet |
| Operating System | CentOS 8 |
| Machine Learning Frameworks | TensorFlow 2.x, PyTorch 1.10 |
Central Server
The Central Server serves as the central repository for data and models. It also hosts the web interface for monitoring the system and accessing predictions. Security is paramount on the Central Server; see Security Protocols.
| Central Server Specification | Value |
|---|---|
| CPU | 2 x Intel Xeon Platinum 8280 |
| RAM | 256 GB DDR4 ECC |
| Storage | 8 x 4TB SAS HDD (RAID 6) + 1TB NVMe SSD for OS |
| Network | 4 x 10 Gigabit Ethernet |
| Operating System | Red Hat Enterprise Linux 8 |
| Database | PostgreSQL 13 |
Networking
The server network is a fully meshed Virtual Private Network (VPN) to ensure secure communication between all servers. Each server is assigned a static IP address. Firewall rules are configured to restrict access to only necessary ports. We utilize Network Monitoring Tools for real-time performance analysis. The network topology is documented in Network Diagram. DNS resolution is handled by an internal DNS server.
Software Components
The following software components are critical to the project’s functionality.
- Data Acquisition Software: Custom Python scripts running on the Edge Servers.
- Data Processing Pipeline: Apache Kafka for data streaming and Apache Spark for distributed data processing. See Apache Spark Configuration.
- Machine Learning Models: TensorFlow and PyTorch models for time series forecasting and anomaly detection.
- Database: PostgreSQL for storing historical data and model results.
- Web Interface: A Flask-based web application for visualizing data and predictions. Utilizes Web Server Best Practices.
- Monitoring Tools: Prometheus and Grafana for system monitoring and alerting. Prometheus Setup Guide details installation.
- Version Control: Git for managing code and configurations.
Security Considerations
Security is a top priority. All servers are protected by firewalls and intrusion detection systems. Regular security audits are performed. Data is encrypted both in transit and at rest. Access control is strictly enforced. See the Security Policy Document for comprehensive details.
Future Expansion
Future plans include expanding the network of Edge Servers to cover a wider area of the Yangtze River and incorporating new data sources, such as satellite imagery. We also plan to explore the use of more advanced machine learning algorithms and deploy models to edge devices for real-time predictions. Scalability Planning is an ongoing process.
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.* ⚠️