AI in Nottingham

```wiki

REDIRECT AI in Nottingham

AI in Nottingham: Server Configuration Overview

This document details the server configuration supporting the "AI in Nottingham" project. This project aims to provide a centralised resource for information about Artificial Intelligence initiatives, research, and businesses within the Nottingham area. This guide is intended for new system administrators and developers contributing to the platform. Understanding these configurations is crucial for maintenance, scaling, and troubleshooting. This wiki page details the core infrastructure. For details on software deployment, see the separate Deployment Guide.

Core Infrastructure

The AI in Nottingham platform is hosted on a cluster of dedicated servers located within the University of Nottingham’s High Performance Computing facility. The cluster is designed for high availability and scalability. We utilise a three-tier architecture: web servers, application servers, and database servers. Network connectivity is provided by a dedicated 10Gbps fibre link. A detailed network diagram can be found at Network Diagram. For information on security protocols, see the security documentation.

Web Server Configuration

The web servers handle incoming HTTP/HTTPS requests and serve static content. They are also responsible for reverse proxying requests to the application servers.

Parameter	Value
Operating System	Ubuntu Server 22.04 LTS
Web Server	Nginx 1.22.0
SSL Certificate	Let's Encrypt (Renewed Automatically)
Reverse Proxy	Enabled (to Application Servers)
Load Balancing	Nginx with upstream module
PHP Version	PHP 8.2

These servers are monitored using Nagios for uptime, CPU usage, memory usage, and disk space. Alerts are configured to notify the on-call team of any issues. Regular backups are performed using rsync to a remote storage location. See Backup Procedures for more details. Web server logs are rotated daily and archived for one month. For details on Nginx configuration, see the Nginx documentation.

Application Server Configuration

The application servers execute the core logic of the AI in Nottingham platform, including handling user authentication, data processing, and API requests. They interact with the database servers to retrieve and store data.

Parameter	Value
Operating System	Ubuntu Server 22.04 LTS
Application Framework	Python 3.10 with Django 4.1
WSGI Server	Gunicorn 20.1.0
Database Driver	psycopg2 (PostgreSQL)
Caching	Redis 6.2
Task Queue	Celery 5.2

Application server performance is critical. We use New Relic for application performance monitoring (APM). Code deployments are automated using Jenkins. Error logging is handled by Sentry. For information on Django settings, see the settings documentation.

Database Server Configuration

The database servers store all persistent data for the AI in Nottingham platform, including user accounts, AI initiative details, and research papers.

Parameter	Value
Operating System	Ubuntu Server 22.04 LTS
Database System	PostgreSQL 14.5
Replication	Streaming Replication (Primary-Standby)
Backup Strategy	Daily Full Backups, Hourly Transaction Log Backups
Storage	SSD RAID 10
Connection Pooling	PgBouncer

Database performance is monitored using pgAdmin. Database backups are stored offsite for disaster recovery. Regular database maintenance tasks, such as vacuuming and indexing, are performed automatically. See Database schema for details on the data model. For information on PostgreSQL configuration, see the PostgreSQL documentation.

Scaling Considerations

The platform is designed to scale horizontally. Additional web and application servers can be easily added to the cluster to handle increased traffic. Database scaling is more complex and requires careful planning. We are currently investigating the use of database sharding to improve database scalability. Load testing is performed regularly to identify performance bottlenecks.

Future Enhancements

Future enhancements to the server configuration include:

Implementation of a content delivery network (CDN) to improve website performance.
Migration to a containerized deployment using Docker and Kubernetes.
Integration with a machine learning platform for automated anomaly detection.
Further optimization of database queries.
Improved monitoring and alerting capabilities.

Related Pages

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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.* ⚠️