AI in the Murray-Darling Basin

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  1. 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.* ⚠️

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