AI in the Murray-Darling Basin
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- AI in the Murray-Darling Basin: Server Configuration
This article details the server configuration supporting the "AI in the Murray-Darling Basin" project. This project utilizes Artificial Intelligence to model and predict water availability, salinity levels, and ecological health within the Murray-Darling Basin. This document is intended for new contributors and system administrators involved in maintaining the project's infrastructure. Please review the System Architecture Overview before proceeding.
Project Overview
The "AI in the Murray-Darling Basin" project relies on a distributed server architecture to process large datasets from various sources, including Remote Sensing Data, Water Level Sensors, Salinity Monitoring Stations, and historical Climate Data. The AI models, primarily Deep Learning Algorithms and Time Series Analysis, require significant computational resources for training and real-time prediction. Data is ingested via Data Ingestion Pipeline and stored in a Database Schema. Results are visualized using Data Visualization Tools.
Server Hardware Specifications
The core infrastructure consists of three tiers: Data Ingestion, Processing, and Serving. Each tier utilizes dedicated server hardware, detailed below.
| Tier | Server Role | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|---|
| Data Ingestion | Data Collector 1 | Intel Xeon Gold 6248R (24 cores) | 128 GB DDR4 ECC | 8 TB RAID 10 SSD | 10 Gbps Ethernet |
| Data Ingestion | Data Collector 2 | Intel Xeon Gold 6248R (24 cores) | 128 GB DDR4 ECC | 8 TB RAID 10 SSD | 10 Gbps Ethernet |
| Processing | Model Trainer 1 | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC | 16 TB RAID 0 NVMe SSD | 100 Gbps Infiniband |
| Processing | Model Trainer 2 | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC | 16 TB RAID 0 NVMe SSD | 100 Gbps Infiniband |
| Serving | Prediction Server 1 | Intel Xeon Silver 4210 (10 cores) | 64 GB DDR4 ECC | 4 TB RAID 1 SSD | 1 Gbps Ethernet |
| Serving | Prediction Server 2 | Intel Xeon Silver 4210 (10 cores) | 64 GB DDR4 ECC | 4 TB RAID 1 SSD | 1 Gbps Ethernet |
Software Stack
The software stack is built on a Linux foundation, leveraging containerization for scalability and reproducibility. See the Software Deployment Guide for detailed instructions.
| Component | Software | Version | Configuration Notes |
|---|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS | Minimal installation; SSH access only. |
| Containerization | Docker | 20.10.12 | Utilizes Docker Compose for orchestration. |
| Orchestration | Kubernetes | 1.24.0 | Managed cluster on Google Kubernetes Engine (GKE). |
| Programming Languages | Python | 3.9 | Primary language for AI model development. |
| AI Framework | TensorFlow | 2.8.0 | Used for Deep Learning models. |
| Data Storage | PostgreSQL | 14.5 | Stores processed data and model metadata. |
Network Configuration
The server network is segmented into three zones: Public, DMZ, and Private. The Data Ingestion servers reside in the DMZ, while the Processing and Serving servers are located in the Private network. All communication between tiers is encrypted using TLS/SSL Encryption. Firewall rules are managed using iptables.
| Zone | Servers | IP Range | Access Control |
|---|---|---|---|
| Public | - | 203.0.113.0/24 | Limited access via load balancer. |
| DMZ | Data Collector 1, Data Collector 2 | 192.168.1.0/24 | Access to Public zone for data ingestion. |
| Private | Model Trainer 1, Model Trainer 2, Prediction Server 1, Prediction Server 2 | 10.0.0.0/16 | Restricted access; internal communication only. |
Security Considerations
Security is paramount. Regular security audits are conducted according to the Security Policy. All servers are patched regularly. Access control is strictly enforced using Role-Based Access Control. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) are deployed to monitor for malicious activity. See the Incident Response Plan for details on handling security breaches.
Future Enhancements
Planned future enhancements include migrating to a Serverless Architecture and leveraging GPU Acceleration for faster model training. We are also investigating the use of Edge Computing to reduce latency for real-time predictions.
Main Page Project Documentation Contact Us Frequently Asked Questions Troubleshooting Guide
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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.* ⚠️