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AI in the Catalonia Rainforest

# AI in the Catalonia Rainforest: Server Configuration

This article details the server configuration utilized for the "AI in the Catalonia Rainforest" project, a research initiative focusing on biodiversity monitoring and anomaly detection using machine learning. This guide is intended for newcomers to our MediaWiki site and provides a detailed overview of the hardware and software components.

Project Overview

The "AI in the Catalonia Rainforest" project employs a network of sensors collecting data on various environmental factors, including temperature, humidity, soundscapes, and camera imagery. This data is processed locally at the edge and then transmitted to a central server farm for deeper analysis using advanced AI models. The project aims to identify and alert researchers to unusual events, such as illegal logging or the presence of invasive species. See Project Goals for more information.

Server Infrastructure

The core infrastructure consists of three primary server types: Edge Servers, Processing Servers, and Storage Servers. Each server type has a specific role and configuration, detailed below. Understanding Server Roles is crucial.

Edge Servers

Edge servers are deployed directly within the Catalonia Rainforest, close to the sensor networks. They perform initial data processing, filtering, and aggregation to reduce bandwidth requirements. They also run basic anomaly detection models for immediate alerts.

Specification Value
CPU Intel Xeon E-2388G
RAM 32 GB DDR4 ECC
Storage 1 TB NVMe SSD
Operating System Ubuntu Server 22.04 LTS
Network Connectivity 4G LTE with failover to satellite

These servers utilize a lightweight containerization solution, Docker, for deploying the edge-based AI models. Further details on Edge Server Software Stack are available.

Processing Servers

Processing servers are located in a secure data center and are responsible for running the complex AI models used for in-depth data analysis. They handle tasks such as species identification from camera imagery and soundscape classification. Data Analysis Pipeline documents the process.

Specification Value
CPU Dual Intel Xeon Gold 6338
RAM 256 GB DDR4 ECC
Storage 4 x 4 TB NVMe SSD (RAID 0)
GPU 4 x NVIDIA A100 (40GB)
Operating System CentOS Stream 9
Networking 100 Gbps Ethernet

The processing servers utilize Kubernetes for container orchestration, enabling scalability and resilience. See Kubernetes Configuration for specifics.

Storage Servers

Storage servers provide a centralized repository for all collected data, including raw sensor readings, processed data, and AI model outputs. Data is stored redundantly to ensure data integrity. Refer to the Data Backup Strategy document.

Specification Value
CPU Intel Xeon Silver 4310
RAM 128 GB DDR4 ECC
Storage 60 x 16 TB SAS HDD (RAID 6)
Operating System Red Hat Enterprise Linux 8
Network Connectivity 40 Gbps Ethernet

We employ Ceph as our distributed storage system, providing scalability and high availability. See Ceph Cluster Details for further configuration information.

Software Stack

The project relies on a diverse software stack, including:

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

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