AI in the United Kingdom Rainforest

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  1. AI in the United Kingdom Rainforest: Server Configuration

This article details the server configuration supporting the "AI in the United Kingdom Rainforest" project, a research initiative utilizing artificial intelligence to monitor and analyze the unique ecosystem of the UK's rainforests. It is aimed at newcomers to our MediaWiki site and provides a technical overview of the infrastructure. This project relies heavily on data processing and requires a robust and scalable server environment.

Project Overview

The "AI in the United Kingdom Rainforest" project involves deploying a network of sensors collecting data on various environmental factors, including temperature, humidity, soil composition, and species identification via audio and visual analysis. This data is transmitted to a central server farm for processing using machine learning algorithms. The processed data is then used to create real-time maps and predictive models of rainforest health. Data Acquisition is a crucial part of the process. We utilize Edge Computing to pre-process some data locally.

Server Hardware

The core of the operation resides within a dedicated server farm located in a secure, climate-controlled facility. The servers are interconnected via a high-speed Network Topology utilizing fiber optic cabling. Redundancy is built into every layer of the infrastructure to ensure high availability.

Server Role Quantity CPU RAM Storage
Data Ingestion 3 Intel Xeon Gold 6248R (24 cores) 256 GB DDR4 ECC 16 TB RAID 6 HDD
Machine Learning (Training) 4 AMD EPYC 7763 (64 cores) 512 GB DDR4 ECC 32 TB NVMe SSD
Machine Learning (Inference) 6 Intel Xeon Silver 4210 (10 cores) 128 GB DDR4 ECC 8 TB NVMe SSD
Database Server 2 (Active/Passive) Intel Xeon Gold 6230 (20 cores) 128 GB DDR4 ECC 24 TB RAID 10 SSD
Web Server (API & Dashboard) 2 (Load Balanced) Intel Xeon E-2224 (6 cores) 64 GB DDR4 ECC 4 TB SSD

Software Stack

The servers utilize a Linux-based operating system, specifically Ubuntu Server 22.04 LTS. This provides a stable and secure foundation for the software stack. We employ Docker containers for application deployment, ensuring consistency across environments and simplifying scaling.

Here's a breakdown of the key software components:

Network Configuration

The server farm is isolated from the public internet by a firewall. Access is restricted to authorized personnel via secure VPN connections. Internal networking utilizes a VLAN structure to segment traffic and enhance security. We leverage Load Balancing to distribute traffic across multiple web servers.

Network Component IP Address Range Subnet Mask Description
Firewall 192.168.1.1 255.255.255.0 Perimeter Security
Data Ingestion Servers 10.0.0.0/24 255.255.255.0 Receiving Data from Sensors
Machine Learning Servers 10.0.1.0/24 255.255.255.0 Training and Inference
Database Server 10.0.2.0/24 255.255.255.0 Data Storage
Web Servers 10.0.3.0/24 255.255.255.0 API and Dashboard Access

Data Storage and Backup

All data is stored redundantly across multiple servers using RAID configurations. Regular backups are performed to an offsite location to protect against data loss. We utilize a combination of full, incremental, and differential backups. Data Redundancy is a top priority. We have implemented a disaster recovery plan that allows for rapid restoration of services in the event of a major outage.

Backup Type Frequency Retention Period Storage Location
Full Backup Weekly 6 Months Offsite Cloud Storage
Incremental Backup Daily 1 Month Onsite NAS
Differential Backup Daily 1 Week Onsite NAS

Future Considerations

We are exploring the use of Kubernetes for orchestrating container deployments and automating scaling. We are also investigating the integration of GPU Acceleration for faster machine learning model training. Continued monitoring and optimization of the server infrastructure will be essential to support the growing demands of the "AI in the United Kingdom Rainforest" project. Scalability is an ongoing focus.


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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