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AI in Dorset

```wiki #REDIRECT AI in Dorset

AI in Dorset: Server Configuration & Technical Overview

This article details the server configuration supporting the "AI in Dorset" project, a local initiative focused on applying artificial intelligence to issues facing Dorset County. This documentation is intended for new system administrators and developers contributing to the project. It outlines the hardware, software, and network configuration.

Project Overview

The "AI in Dorset" project utilizes machine learning models to analyze local data sources, including weather patterns, traffic flow, and demographic information. The goal is to provide insights for improved resource allocation, disaster preparedness, and economic development. The server infrastructure is designed for scalability, reliability, and security. See Data Security Policy for more details on data handling.

Hardware Specification

The core server infrastructure consists of three primary servers, each with a specific role. The following table details the hardware configuration for each:

Server Role CPU RAM Storage Network Interface
Application Server | Intel Xeon Gold 6248R (24 cores) | 128GB DDR4 ECC | 2 x 2TB NVMe SSD (RAID 1) | 10Gbps Ethernet
Database Server | AMD EPYC 7763 (64 cores) | 256GB DDR4 ECC | 4 x 4TB SAS HDD (RAID 10) | 10Gbps Ethernet
Model Training Server | 2 x NVIDIA Tesla A100 GPUs | 512GB DDR4 ECC | 2 x 8TB NVMe SSD (RAID 0) | 25Gbps Ethernet & 10Gbps Ethernet

All servers are housed in a dedicated, climate-controlled server room at the Dorset County Council IT facility. Power redundancy is provided by dual power supplies and a UPS system. Refer to the Server Room Access Policy for physical access procedures.

Software Stack

The software stack is built around a Linux operating system, providing a stable and flexible platform.

Component Version Function
Operating System | Ubuntu Server 22.04 LTS | Server OS Web Server | Apache 2.4 | Hosting the web application Database | PostgreSQL 14 | Data storage and management Programming Language | Python 3.10 | Backend logic and model integration Machine Learning Framework | TensorFlow 2.12 | Model training and inference Containerization | Docker 24.0 | Application deployment & isolation Orchestration | Kubernetes 1.27 | Container management and scaling

The application server hosts the web-based user interface and API endpoints. The database server manages all project data. The model training server is responsible for training and updating the machine learning models. See Software Licensing Information for details on software licenses.

Network Configuration

The servers are connected to the Dorset County Council network via a dedicated VLAN. Security is paramount, and access is restricted based on the principle of least privilege.

Parameter Value Description
VLAN ID | 100 | Dedicated VLAN for the AI in Dorset project Subnet Mask | 255.255.255.0 | Network subnet Gateway | 192.168.100.1 | Default gateway DNS Servers | 8.8.8.8, 8.8.4.4 | Public DNS servers Firewall | iptables | Network firewall

The firewall is configured to allow only necessary traffic to and from the servers. Regular security audits are conducted to identify and address potential vulnerabilities. Review the Network Security Guidelines for detailed network security information. Connectivity to external data sources is managed through a secure VPN connection as defined in the VPN Configuration Guide.

Security Considerations

Security is a critical aspect of the "AI in Dorset" project. All data is encrypted at rest and in transit. Access control is enforced through strong authentication and authorization mechanisms. Regular vulnerability scans are performed to identify and mitigate potential security risks. See the Incident Response Plan for procedures to follow in the event of a security incident.

Future Scalability

The infrastructure is designed to be scalable to accommodate future growth. Kubernetes allows for easy scaling of the application and model training components. Additional servers can be added to the cluster as needed. We anticipate utilizing Cloud Computing Resources for burst capacity during peak demand. See the Capacity Planning Document for long-term scalability projections.

Related Documentation

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

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