AI in the Indus River
AI in the Indus River: Server Configuration & Deployment
This article details the server configuration used for the "AI in the Indus River" project, a research initiative focused on utilizing artificial intelligence to monitor and predict water quality and flow patterns within the Indus River basin. This document is intended for individuals new to the MediaWiki platform and provides a detailed look at the infrastructure supporting this project. We will cover hardware specifications, software stack, network topology, and security considerations. This document assumes a basic understanding of server administration and networking concepts.
Project Overview
The "AI in the Indus River" project relies on a distributed network of sensors collecting real-time data on various parameters, including water level, temperature, salinity, dissolved oxygen, and pollutants. This data is processed locally at each sensor node, then transmitted to a central server cluster for further analysis using machine learning models. The goal is to provide early warnings for floods, droughts, and water contamination events. Understanding the underlying server infrastructure is crucial for maintaining and scaling the project.
Hardware Specifications
The central server cluster consists of three primary types of servers: data ingestion servers, processing servers (hosting the AI models), and database servers. Each type is optimized for its specific role.
| Server Type | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Data Ingestion | Intel Xeon Gold 6248R (24 cores) | 128 GB DDR4 ECC | 4 x 2TB NVMe SSD (RAID 10) | 10 Gbps Ethernet |
| Processing (AI) | 2 x AMD EPYC 7763 (64 cores each) | 256 GB DDR4 ECC | 8 x 4TB NVMe SSD (RAID 0) + 4 x NVIDIA A100 GPUs | 100 Gbps InfiniBand |
| Database | Intel Xeon Platinum 8280 (28 cores) | 512 GB DDR4 ECC | 16 x 8TB SAS HDD (RAID 6) | 10 Gbps Ethernet |
These servers are housed in a secure, climate-controlled data center with redundant power and cooling systems. Redundancy is a key consideration, as uninterrupted data flow is paramount. We employ RAID configurations to safeguard against data loss. Detailed server schematics can be found on the Server Hardware Documentation page.
Software Stack
The software stack is built around a Linux-based operating system (Ubuntu Server 22.04 LTS) to provide a stable and flexible platform.
| Layer | Software | Version | Purpose |
|---|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS | Base operating system |
| Database | PostgreSQL | 14.7 | Data storage and retrieval |
| Programming Language | Python | 3.10 | AI model development and deployment |
| Machine Learning Framework | TensorFlow | 2.12 | Training and inference of AI models |
| Data Ingestion | Apache Kafka | 3.3.1 | Real-time data streaming |
| Web Server | Nginx | 1.23 | Serving API endpoints and web interface |
The data ingestion pipeline utilizes Apache Kafka to handle the high volume of sensor data. The AI models are deployed using TensorFlow Serving for efficient inference. A RESTful API, built with Flask, provides access to the processed data and model predictions. Further information on the software dependencies can be found at Software Dependency List. We use Docker containers for consistent deployment and environment management.
Network Topology & Security
The server cluster is connected to the internet via a high-bandwidth fiber optic connection. A firewall is implemented to restrict access to only authorized personnel and services. The network is segmented into different zones (DMZ, internal network) to isolate critical systems.
| Network Zone | Purpose | Access Control |
|---|---|---|
| DMZ | Web server, API gateway | Public access (limited to specific ports) |
| Internal Network | Data ingestion, processing, database servers | Restricted to authorized personnel and services |
| Management Network | Server administration and monitoring | Highly restricted access (VPN required) |
All communication between servers is encrypted using TLS/SSL. Regular security audits are conducted to identify and address vulnerabilities. We implement intrusion detection systems to monitor for malicious activity. The project adheres to all relevant data privacy regulations, including GDPR. Detailed network diagrams are available at Network Topology Diagrams.
Data Flow
1. Sensor data is transmitted to the data ingestion servers via wireless communication protocols (LoRaWAN, NB-IoT). 2. Data ingestion servers receive the data and publish it to an Apache Kafka topic. 3. Processing servers subscribe to the Kafka topic and retrieve the data. 4. Processing servers run the AI models to analyze the data and generate predictions. 5. Predictions and processed data are stored in the PostgreSQL database. 6. The web server provides an API endpoint for accessing the data and predictions. 7. Users can access the data and predictions through a web interface.
Future Considerations
Future enhancements include incorporating additional sensor data sources, developing more sophisticated AI models, and scaling the infrastructure to handle increasing data volumes. We are also exploring the use of edge computing to perform some data processing closer to the sensors, reducing latency and bandwidth requirements. The project documentation is constantly updated and can be found on the Project Documentation Portal. We are also investigating Kubernetes for improved orchestration and scalability.
Server Administration Data Backup and Recovery Monitoring and Alerting Security Best Practices Troubleshooting Guide
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.* ⚠️