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AI in Greater Manchester

AI in Greater Manchester: Server Configuration Overview

This article details the server configuration supporting the “AI in Greater Manchester” initiative. It is designed for newcomers to our MediaWiki site and provides a technical overview of the infrastructure. The goal of this project is to provide a centralized resource for information regarding Artificial Intelligence research, development, and deployment across the Greater Manchester region. This document outlines the server specifications, software stack, and network topology. It assumes a basic understanding of server administration and networking concepts. See Server Administration Basics for a refresher.

Overview

The "AI in Greater Manchester" project utilizes a hybrid server infrastructure consisting of on-premise hardware at the University of Manchester and cloud-based resources from Amazon Web Services (AWS). This hybrid approach allows for flexibility, scalability, and cost optimization. Data sensitivity and regulatory compliance requirements dictate that certain datasets remain on-premise, while others are processed in the cloud. The entire system is managed using Configuration Management Systems like Ansible. Regular Server Backups are performed to ensure data integrity.

On-Premise Server Specifications

The core on-premise infrastructure resides within a dedicated, secure server room at the University of Manchester. These servers are responsible for hosting the primary MediaWiki instance, data storage for sensitive datasets, and initial data processing pipelines.

Server Name Role CPU RAM Storage Network Interface
gm-mediawiki-01 MediaWiki Frontend Intel Xeon Gold 6248R (24 cores) 128 GB DDR4 ECC 2 x 1TB NVMe SSD (RAID 1) 10 Gbps Ethernet
gm-data-01 Data Storage (Sensitive Data) Intel Xeon Silver 4210 (10 cores) 64 GB DDR4 ECC 8 x 4TB SAS HDD (RAID 6) 1 Gbps Ethernet
gm-compute-01 Initial Data Processing AMD EPYC 7763 (64 cores) 256 GB DDR4 ECC 2 x 2TB NVMe SSD (RAID 0) 10 Gbps Ethernet

These servers run Ubuntu Server 22.04 LTS and are interconnected via a dedicated VLAN. Access is strictly controlled via Firewall Configuration and multi-factor authentication. Monitoring is performed using Nagios Monitoring.

AWS Cloud Infrastructure

The AWS component of the infrastructure provides scalable compute resources for large-scale data processing, machine learning model training, and hosting of web applications. We primarily utilize the following AWS services:

Service Instance Type (Example) Purpose Region
EC2 p3.8xlarge Machine Learning Model Training eu-west-1 (London)
S3 Standard Data Storage (Non-Sensitive) eu-west-1 (London)
Lambda Python 3.9 Serverless Functions (API endpoints) eu-west-1 (London)
RDS (PostgreSQL) db.r5.large Metadata Database eu-west-1 (London)

The AWS infrastructure is managed using Infrastructure as Code with Terraform. Security is enforced through AWS Identity and Access Management (IAM) policies and VPC configurations. Data transfer between on-premise and AWS is secured using VPN Connections.

Software Stack

The following software components are crucial to the operation of the "AI in Greater Manchester" platform:

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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