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Automated server configuration article

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# AI in the England Rainforest: Server Configuration

This article details the server configuration supporting the “AI in the England Rainforest” project. This project utilizes artificial intelligence to monitor and analyze data collected from a simulated rainforest environment within England, focusing on biodiversity, climate patterns, and ecosystem health. This document is intended for new system administrators and developers contributing to the project.

== Project Overview ==

The “AI in the England Rainforest” project involves a network of sensors deployed throughout a large, climate-controlled facility mimicking a rainforest environment. These sensors collect data on temperature, humidity, light levels, soil moisture, audio recordings (for animal identification), and video feeds. This data is processed in real-time by AI models to identify species, detect anomalies, and predict potential ecological shifts. The entire system is underpinned by a robust server infrastructure, described below. For information on the [[Data Collection Pipeline]], see the dedicated documentation.

== Server Infrastructure ==

The server infrastructure is divided into three tiers: Data Acquisition, Processing & AI, and Storage & Archiving. Each tier has specific hardware and software requirements. We utilize a hybrid cloud approach, with critical processing occurring on-premise for latency reasons, and long-term archival in a secure cloud environment. Details about [[Security Protocols]] are available on the security wiki.

=== Data Acquisition Servers ===

These servers are responsible for receiving data directly from the sensors. They perform initial data validation and preprocessing before forwarding the data to the Processing & AI tier. The servers are located close to the sensor network to minimize latency.

{| class="wikitable"
! Server Name
! Role
! Operating System
! CPU
! RAM
! Network Interface
|-
| aq-server-01
| Primary Data Receiver
| Ubuntu Server 22.04 LTS
| Intel Xeon Silver 4310 (12 cores)
| 64 GB DDR4 ECC
| 10 Gbps Ethernet
|-
| aq-server-02
| Secondary Data Receiver (Failover)
| Ubuntu Server 22.04 LTS
| Intel Xeon Silver 4310 (12 cores)
| 64 GB DDR4 ECC
| 10 Gbps Ethernet
|}

Software running on these servers includes: [[MQTT Broker]], [[Node-RED]], and custom Python scripts for data validation. See [[Data Acquisition Software]] for configuration details.

=== Processing & AI Servers ===

These servers are the heart of the project, running the AI models and performing real-time data analysis. They require significant computational resources, particularly GPUs.

{| class="wikitable"
! Server Name
! Role
! Operating System
! CPU
! GPU
! RAM
! Storage
|-
| ai-server-01
| Primary AI Processing
| Ubuntu Server 22.04 LTS
| Intel Xeon Gold 6338 (32 cores)
| NVIDIA A100 (80 GB)
| 256 GB DDR4 ECC
| 4 TB NVMe SSD
|-
| ai-server-02
| Secondary AI Processing (Model Training)
| Ubuntu Server 22.04 LTS
| Intel Xeon Gold 6338 (32 cores)
| NVIDIA A100 (80 GB)
| 256 GB DDR4 ECC
| 4 TB NVMe SSD
|-
| ai-server-03
| Real-time Anomaly Detection
| Ubuntu Server 22.04 LTS
| Intel Xeon Silver 4310 (12 cores)
| NVIDIA RTX 3090 (24 GB)
| 128 GB DDR4 ECC
| 2 TB NVMe SSD
|}

Key software components include: [[TensorFlow]], [[PyTorch]], [[Kubernetes]] for container orchestration, and various custom AI models developed in Python. Refer to the [[AI Model Documentation]] for details on the models themselves.

=== Storage & Archiving Servers ===

These servers are responsible for storing the raw sensor data and the processed results. Long-term archiving is done in a cloud-based object storage service.

{| class="wikitable"
! Server Name
! Role
! Operating System
! Storage Capacity
! RAID Level
! Network Interface
|-
| st-server-01
| Primary Data Storage
| CentOS 7
| 100 TB
| RAID 6
| 40 Gbps Infiniband
|-
| st-server-02
| Backup & Replication
| CentOS 7
| 100 TB
| RAID 6
| 40 Gbps Infiniband
|}

We utilize [[Ceph]] for distributed file storage and replication. Cloud archival is managed through [[Amazon S3]]. Detailed information about the [[Data Retention Policy]] can be found on the policy wiki.

== Networking ==

The servers are connected via a high-speed network infrastructure. A dedicated VLAN is used for the sensor data traffic. The network topology is a star configuration with a central core switch. See the [[Network Diagram]] for a visual representation.

== Monitoring & Alerting ==

The entire server infrastructure is monitored using [[Prometheus]] and [[Grafana]]. Alerts are configured for critical metrics such as CPU usage, memory usage, disk space, and network latency. Alerts are delivered via [[PagerDuty]]. A detailed guide to [[Server Monitoring]] is available for new administrators.

[[Help:Contents]]
[[Main Page]]
[[Project:AI Rainforest]]
[[Data Collection Pipeline]]
[[Security Protocols]]
[[Data Acquisition Software]]
[[AI Model Documentation]]
[[TensorFlow]]
[[PyTorch]]
[[Kubernetes]]
[[Ceph]]
[[Amazon S3]]
[[Data Retention Policy]]
[[Network Diagram]]
[[Server Monitoring]]
[[PagerDuty]]
[[Help:Editing]]

[[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
|
|}

== Order Your Dedicated Server ==
[https://powervps.net/?from=32 Configure and order] your ideal server configuration

=== Need Assistance? ===
* Telegram: [https://t.me/powervps @powervps Servers at a discounted price]

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

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