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Automated server configuration article

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# AI in the Scotland Rainforest: Server Configuration

This article details the server configuration used to support the "AI in the Scotland Rainforest" project, a research initiative utilizing artificial intelligence to monitor and analyze the unique ecosystem of the Scottish rainforest. This documentation is intended for new members of the technical team and provides a comprehensive overview of the hardware and software setup.

== Project Overview ==

The "AI in the Scotland Rainforest" project involves deploying a network of sensors throughout various rainforest locations in Scotland. These sensors collect data on temperature, humidity, light levels, soundscapes (for species identification), and camera imagery. This data is transmitted to a central server cluster for processing and analysis using machine learning algorithms. The primary goals are to track biodiversity, monitor environmental changes, and develop predictive models for rainforest health. See also [[Rainforest Data Collection]], [[Sensor Network Deployment]], and [[Machine Learning Pipelines]].

== Server Hardware Configuration ==

The core of the system is a cluster of servers located at the University of the Highlands and Islands, providing the necessary computational power and storage capacity. The cluster is built around a high-performance network backbone and is designed for scalability and redundancy.

{| class="wikitable"
! Component
! Specification
! Quantity
|-
| CPU
| Intel Xeon Gold 6338 (32 cores)
| 6
|-
| RAM
| 256GB DDR4 ECC Registered
| 6
|-
| Storage (OS/Boot)
| 1TB NVMe SSD
| 6
|-
| Storage (Data)
| 16TB SAS HDD (RAID 6)
| 12
|-
| Network Interface
| 100Gbps Ethernet
| 6
|-
| Power Supply
| 1600W Redundant
| 6
|}

The servers are housed in a dedicated rack with appropriate cooling and power distribution units (PDUs). See [[Server Room Specifications]] for detailed information on environmental controls. The network infrastructure utilizes [[Virtual LANs]] to isolate different parts of the system.

== Software Stack ==

The software stack is built on a Linux foundation (Ubuntu Server 22.04 LTS) and incorporates various open-source tools for data processing, machine learning, and visualization.

=== Operating System ===

Ubuntu Server 22.04 LTS is used as the base operating system. It provides a stable and secure environment for the other software components. [[Ubuntu Server Documentation]] provides detailed installation and configuration instructions.

=== Database System ===

PostgreSQL 15 is used as the primary database for storing sensor data and metadata. It's chosen for its reliability, scalability, and support for complex queries. Data is organized using a relational schema designed for efficient analysis. See [[Database Schema Design]] for details.

=== Machine Learning Framework ===

PyTorch 2.0 is the primary machine learning framework employed for developing and training the AI models. It offers flexibility and performance for deep learning tasks. [[PyTorch Tutorials]] are available for newcomers to the framework. We also use TensorFlow 2.12 for specific models.

=== Data Processing Pipeline ===

The data processing pipeline is built using Apache Kafka for message queuing and Apache Spark for distributed data processing. This allows for real-time ingestion and analysis of sensor data. Refer to [[Kafka Configuration]] and [[Spark Cluster Management]] for details.

=== Monitoring and Logging ===

Prometheus and Grafana are used for system monitoring and visualization. The ELK stack (Elasticsearch, Logstash, Kibana) is used for log aggregation and analysis. [[Prometheus Setup Guide]] and [[ELK Stack Deployment]] provide detailed instructions.

== Networking Configuration ==

The server cluster is connected to the university network via a 100Gbps Ethernet link. A dedicated VLAN is used to isolate the AI in the Scotland Rainforest project from other network traffic.

{| class="wikitable"
! Parameter
! Value
|-
| VLAN ID
| 1000
|-
| Subnet Mask
| 255.255.255.0
|-
| Gateway
| 192.168.1000.1
|-
| DNS Servers
| 8.8.8.8, 8.8.4.4
|}

Firewall rules are configured using `iptables` to restrict access to the servers and protect against unauthorized access. See [[Firewall Management]] for details. The servers are also accessible via SSH for remote administration.

== Security Considerations ==

Security is a paramount concern for the AI in the Scotland Rainforest project. Several measures are in place to protect the data and infrastructure:

* Regular security audits are conducted to identify and address vulnerabilities.
* Strong passwords and multi-factor authentication are enforced for all user accounts.
* Data is encrypted both in transit and at rest.
* Firewall rules are regularly reviewed and updated.
* Intrusion detection and prevention systems are deployed. See [[Security Best Practices]].

== Future Expansion ==

As the project evolves, the server infrastructure will need to be expanded to accommodate increasing data volumes and more complex AI models. Future plans include:

* Adding more servers to the cluster.
* Upgrading the network infrastructure to 200Gbps Ethernet.
* Implementing a distributed file system (e.g., Ceph) for improved storage scalability.
* Exploring the use of GPU acceleration for machine learning tasks. See [[GPU Cluster Configuration]].

{| class="wikitable"
! Future Upgrade
! Estimated Timeline
! Cost (Approximate)
|-
| Additional Server Nodes (x3)
| Q1 2024
| £20,000
|-
| Network Upgrade (200Gbps)
| Q2 2024
| £10,000
|-
| Distributed File System (Ceph)
| Q3 2024
| £15,000
|}

This document provides a comprehensive overview of the server configuration for the "AI in the Scotland Rainforest" project. For more detailed information, please refer to the linked documentation. [[Project Documentation Hub]]

[[Server Maintenance Schedule]]
[[Data Backup Procedures]]
[[Disaster Recovery Plan]]
[[User Account Management]]
[[Network Topology Diagram]]
[[Software License Management]]
[[Environmental Monitoring Data]]
[[Sensor Calibration Procedures]]
[[AI Model Training Data]]
[[Data Privacy Policy]]
[[Incident Response Plan]]
[[Regular System Updates]]
[[Security Audit Reports]]
[[Contact Information]]
[[Project Team Members]]
[[Glossary of Terms]]

[[Category:Server Hardware]]

== Intel-Based Server Configurations ==
{| class="wikitable"
! 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 ==
{| class="wikitable"
! 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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Admin: Automated server configuration article