AI in the American Samoan Rainforest

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AI in the American Samoan Rainforest: Server Configuration

This article details the server configuration deployed to support the "AI in the American Samoan Rainforest" project. This project utilizes machine learning to analyze audio and visual data collected from remote sensors within the rainforest ecosystem, aiding in biodiversity monitoring and conservation efforts. This guide is aimed at new contributors to the MediaWiki site and provides a detailed overview of the server infrastructure.

Project Overview

The "AI in the American Samoan Rainforest" project involves continuous data collection from a network of sensor nodes deployed throughout the National Park of American Samoa. These nodes capture audio recordings of animal vocalizations, and low-resolution images for species identification. The data is transmitted via satellite link to a central server cluster for processing. The key goals of the project include:

  • Automated species identification
  • Real-time monitoring of endangered species
  • Detection of invasive species
  • Tracking rainforest health indicators

Server Architecture

The server infrastructure is based on a distributed architecture designed for scalability and reliability. The system is segmented into three primary tiers: Data Ingestion, Processing, and Storage/Presentation. This allows for independent scaling of each component based on demand. Detailed information about Network Topology is available on a separate page. We use Linux as our operating system of choice.

Data Ingestion Tier

This tier is responsible for receiving data from the sensor nodes. It consists of a load balancer and several ingestion servers. The load balancer distributes incoming traffic evenly across the ingestion servers, ensuring high availability. Data is received via a secure satellite connection utilizing the X.509 protocol for authentication.

Server Component Quantity Specifications
Load Balancer 1 HAProxy, 8GB RAM, 4 Core CPU, 100Mbps Network Interface
Ingestion Server 3 Ubuntu Server 22.04, 16GB RAM, 8 Core CPU, 1Gbps Network Interface, 1TB SSD
Firewall 1 pfSense, dedicated hardware appliance

Processing Tier

The Processing Tier is where the core machine learning algorithms are executed. This tier utilizes a cluster of GPU-accelerated servers to handle the computationally intensive tasks of audio analysis and image recognition. We leverage TensorFlow and PyTorch for model training and inference. The servers are managed by a Kubernetes cluster for orchestration and scaling. See the Machine Learning Models page for details on the models being used.

Server Component Quantity Specifications
GPU Server 4 Ubuntu Server 22.04, 64GB RAM, 16 Core CPU, 2x NVIDIA RTX 3090 GPUs, 2TB NVMe SSD, 10Gbps Network Interface
Kubernetes Master Node 1 Ubuntu Server 22.04, 32GB RAM, 8 Core CPU, 1TB SSD, 1Gbps Network Interface
Kubernetes Worker Node 2 Ubuntu Server 22.04, 32GB RAM, 8 Core CPU, 1TB SSD, 1Gbps Network Interface

Storage & Presentation Tier

This tier handles the long-term storage of processed data and provides a web interface for researchers to access and analyze the results. We utilize a PostgreSQL database to store metadata and analysis results. The web interface is built using Python and the Flask framework. Data backups are performed daily to a geographically separate location using rsync.

Server Component Quantity Specifications
Database Server 1 Ubuntu Server 22.04, 64GB RAM, 16 Core CPU, 4TB RAID 10 HDD, 1Gbps Network Interface
Web Server 2 Ubuntu Server 22.04, 16GB RAM, 4 Core CPU, 500GB SSD, 1Gbps Network Interface
Backup Server 1 Ubuntu Server 22.04, 8TB HDD, 1Gbps Network Interface

Software Stack

The following software components are integral to the server configuration:

  • Operating System: Ubuntu Server 22.04 LTS
  • Web Server: Nginx
  • Database: PostgreSQL 14
  • Programming Languages: Python 3.9, Bash
  • Machine Learning Frameworks: TensorFlow, PyTorch
  • Containerization: Docker
  • Orchestration: Kubernetes
  • Monitoring: Prometheus, Grafana
  • Logging: Elasticsearch, Kibana
  • Version Control: Git

Security Considerations

Security is paramount. All servers are protected by a firewall. Regular security audits are conducted, and all software is kept up to date. Access to the servers is restricted to authorized personnel only, utilizing SSH keys and multi-factor authentication. We also implement intrusion detection systems (IDS) to monitor for malicious activity. Refer to the Security Policy document for more detailed information.

Future Enhancements

Planned future enhancements include:

  • Implementing a more robust data pipeline using Apache Kafka.
  • Exploring the use of edge computing to reduce latency and bandwidth requirements.
  • Integrating with real-time alert systems to notify researchers of significant events.
  • Expanding the database capacity to accommodate increasing data volumes.

Server Maintenance procedures are documented separately. Please consult the Troubleshooting Guide for assistance with common issues. For information on Data Privacy practices, see the dedicated page.


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.* ⚠️

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