AI in the Anguilla Rainforest
- AI in the Anguilla Rainforest: Server Configuration
This document details the server configuration supporting the "AI in the Anguilla Rainforest" project. This project utilizes artificial intelligence to analyze real-time data collected from sensors deployed throughout the Anguilla Rainforest, focusing on biodiversity monitoring and anomaly detection. This guide is intended for new members of the server administration team.
Project Overview
The “AI in the Anguilla Rainforest” project aims to provide real-time insights into the health and biodiversity of the rainforest ecosystem. Data streams from various sensor types (acoustic, thermal, visual, and atmospheric) are processed by machine learning models to identify species, detect unusual activity (e.g., deforestation, poaching), and track environmental changes. The system relies on a robust and scalable server infrastructure to handle the high volume of data and computational demands. See Data Acquisition and Machine Learning Models for more details on these aspects.
Server Architecture
The server infrastructure is composed of three primary tiers: data ingestion, processing, and storage. Each tier is designed for scalability and redundancy. We utilize a distributed architecture leveraging multiple servers to ensure high availability and fault tolerance. The entire system is monitored via Server Monitoring Dashboard and Alerting System.
Data Ingestion Tier
This tier is responsible for receiving data from the sensors in the rainforest. It consists of load balancers and ingestion servers. The load balancers distribute the incoming data stream across the ingestion servers, ensuring no single server is overwhelmed. Ingestion servers perform initial data validation and formatting before passing the data to the processing tier. Refer to Sensor Network Configuration for specifics on the sensors.
Processing Tier
This tier houses the machine learning models and performs the core data analysis. It consists of powerful GPU-accelerated servers optimized for deep learning tasks. The processing tier receives data from the ingestion tier, runs the models, and generates insights. We use Kubernetes Cluster Management to orchestrate the deployment and scaling of these models.
Storage Tier
This tier provides persistent storage for the raw sensor data, processed data, and model outputs. It utilizes a distributed file system to ensure scalability and data durability. Data is archived according to the Data Retention Policy.
Server Specifications
The following tables detail the specifications for each server type within the infrastructure.
Ingestion Servers
| Server Role | CPU | Memory | Storage | Network Interface |
|---|---|---|---|---|
| Ingestion Server | Intel Xeon Silver 4310 (12 cores) | 64 GB DDR4 ECC RAM | 2 x 1TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
Processing Servers
| Server Role | CPU | Memory | GPU | Storage | Network Interface |
|---|---|---|---|---|---|
| Processing Server | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC RAM | NVIDIA A100 (80GB) x 2 | 4 x 2TB NVMe SSD (RAID 0) | 100 Gbps InfiniBand |
Storage Servers
| Server Role | CPU | Memory | Storage | Network Interface |
|---|---|---|---|---|
| Storage Server | Intel Xeon Gold 6338 (32 cores) | 128 GB DDR4 ECC RAM | 32 x 16TB SAS HDDs (RAID 6) | 40 Gbps Ethernet |
Software Stack
The software stack is carefully chosen to provide a robust and efficient platform for AI processing.
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker
- Orchestration: Kubernetes
- Programming Languages: Python, C++
- Machine Learning Frameworks: TensorFlow, PyTorch
- Database: PostgreSQL with TimescaleDB extension for time-series data
- Message Queue: Kafka
- Monitoring: Prometheus, Grafana – see Monitoring and Alerting Documentation
Network Configuration
The servers are connected via a high-speed private network. The network is segmented into different zones (data ingestion, processing, storage) to enhance security. Firewall rules are configured to restrict access between zones based on the principle of least privilege. See Network Topology Diagram for a visual representation.
Security Considerations
Security is paramount. All servers are hardened according to the Server Hardening Guidelines. Regular security audits are conducted to identify and address vulnerabilities. Data is encrypted both in transit and at rest. Access control is strictly enforced using role-based access control (RBAC). We follow the Incident Response Plan in case of security breaches.
Future Expansion
As the project evolves, the server infrastructure will need to be expanded to accommodate increasing data volumes and computational demands. We plan to add more processing servers with more powerful GPUs and increase the storage capacity of the storage tier. We are also exploring the use of serverless computing to further optimize resource utilization. Please refer to Capacity Planning for details.
Main Page Server Maintenance Schedule Troubleshooting Guide Contact Support Data Security Policy
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.* ⚠️