AI in the Mekong River
AI in the Mekong River: Server Configuration & Deployment
This article details the server configuration used to support the "AI in the Mekong River" project, a research initiative utilizing machine learning to monitor and predict environmental changes within the Mekong River basin. This guide is intended for newcomers to our MediaWiki site and provides a technical overview of the deployed infrastructure. Understanding these configurations is crucial for maintenance, scaling, and contributing to the project.
Project Overview
The “AI in the Mekong River” project collects data from various sources including satellite imagery, river gauge stations, and local sensor networks. This data is then processed using machine learning models to predict flood events, monitor water quality, and assess the impact of climate change. The server infrastructure is designed for high throughput, scalability, and reliability to handle the continuous stream of data and complex computations involved. Access to the Data Acquisition Systems is heavily monitored and secured.
Server Architecture
The system employs a distributed architecture consisting of three primary tiers: Data Ingestion, Processing, and Serving. Each tier is comprised of a cluster of servers, strategically located for redundancy and performance. We utilize a hybrid cloud approach, leveraging both on-premise hardware and cloud services from Cloud Provider X. Access controls are managed through Central Authentication Service.
Data Ingestion Tier
This tier is responsible for receiving, validating, and storing the raw data streams. It’s the entry point for all information.
| Component | Role | Quantity | Operating System | Key Software |
|---|---|---|---|---|
| Data Receivers | Accept data streams from sensors & satellites | 4 | Ubuntu Server 22.04 LTS | Nginx, Kafka |
| Message Queue | Buffers and distributes incoming data | 1 (Cluster) | CentOS 7 | RabbitMQ |
| Raw Data Storage | Stores raw, unprocessed data | 3 | Ubuntu Server 22.04 LTS | PostgreSQL, Object Storage Solution |
The raw data storage utilizes a combination of a relational database (PostgreSQL) for metadata and an object storage solution for large binary files such as satellite imagery. Data integrity is ensured through regular backups managed by the Backup and Recovery Team.
Processing Tier
This tier performs the core machine learning tasks, including data cleaning, feature extraction, model training, and prediction generation. It requires significant computational power.
| Component | Role | Quantity | Operating System | Key Software |
|---|---|---|---|---|
| Master Node | Coordinates distributed processing tasks | 1 | Ubuntu Server 22.04 LTS | Kubernetes, Docker |
| Worker Nodes | Executes machine learning models | 8 | Ubuntu Server 22.04 LTS | TensorFlow, PyTorch, Scikit-learn, CUDA Toolkit |
| Model Repository | Stores trained machine learning models | 2 | Ubuntu Server 22.04 LTS | MLflow, Version Control System |
| Intermediate Data Storage | Stores processed data for analysis | 2 | CentOS 8 | Apache Spark, Hadoop |
Worker nodes are equipped with high-performance GPUs to accelerate model training and inference. The distributed processing framework (Kubernetes and Docker) allows for efficient resource utilization and scalability. The Monitoring Dashboard provides real-time insights into the cluster's performance.
Serving Tier
This tier provides access to the processed data and predictions through APIs and web interfaces. It’s the interface for end-users and other applications.
| Component | Role | Quantity | Operating System | Key Software |
|---|---|---|---|---|
| API Gateway | Handles API requests and authentication | 2 (Load Balanced) | Ubuntu Server 22.04 LTS | Nginx, API Management Tool |
| Prediction Servers | Serve real-time predictions | 4 | Ubuntu Server 22.04 LTS | Flask, REST API Framework |
| Web Application Server | Hosts the web interface for data visualization | 2 (Load Balanced) | Ubuntu Server 22.04 LTS | Django, Web Server |
| Database (Result Storage) | Stores prediction results and summaries | 1 (Replicated) | PostgreSQL 14 | PostgreSQL extensions for geospatial data |
The serving tier is designed for high availability and scalability, utilizing load balancing and replicated databases. The Security Protocols implemented are paramount to protect sensitive data. The User Access Control List details permissions for different user roles.
Networking & Security
The entire infrastructure is protected by a robust firewall and intrusion detection system. All communication between tiers is encrypted using TLS/SSL. Regular security audits are conducted by the Security Team. Network segmentation isolates the different tiers to minimize the impact of potential security breaches. See the Network Diagram for a detailed view of the network topology.
Future Considerations
We are actively exploring the integration of edge computing capabilities to process data closer to the source, reducing latency and bandwidth requirements. This involves deploying smaller server instances at key locations along the Mekong River. Further improvements to the Data Pipeline are planned to optimize data flow and reduce processing time.
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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.* ⚠️