AI in the Sri Lankan Rainforest
# AI in the Sri Lankan Rainforest: Server Configuration
This article details the server configuration supporting the "AI in the Sri Lankan Rainforest" project. This project utilizes artificial intelligence to analyze data collected from sensor networks deployed within several rainforest regions of Sri Lanka, focusing on biodiversity monitoring and early warning systems for deforestation. This document is geared towards new contributors and system administrators who will be maintaining this infrastructure.
Project Overview
The "AI in the Sri Lankan Rainforest" project involves the real-time analysis of data streams from a network of acoustic sensors, camera traps, and environmental sensors. The data is processed using machine learning models to identify species, detect illegal logging activity, and monitor ecosystem health. The server infrastructure is designed for high availability, scalability, and data security. Data is collected via Data Acquisition Systems and transmitted to the central servers for processing.
Server Architecture
The system follows a tiered architecture:
- **Ingestion Tier:** Receives data from the sensor network.
- **Processing Tier:** Performs data cleaning, feature extraction, and model inference.
- **Storage Tier:** Stores raw data, processed data, and model artifacts.
- **API Tier:** Provides access to processed data and model outputs.
- **Operating System:** Ubuntu Server 22.04 LTS
- **Containerization:** Docker and Kubernetes
- **Message Queue:** RabbitMQ
- **Database:** PostgreSQL 14 with PostGIS extension
- **Programming Languages:** Python 3.9, Go
- **Machine Learning Frameworks:** TensorFlow, PyTorch
- **API Framework:** Flask
- **Monitoring:** Prometheus and Grafana. System monitoring is implemented extensively.
- All servers are hardened using CIS benchmarks.
- Regular security updates are applied.
- Intrusion detection and prevention systems are in place.
- Access control is strictly enforced. Least privilege is a key principle.
- Data is encrypted at rest and in transit.
- Regular penetration testing is conducted.
- Implementing a more sophisticated data pipeline using Apache Kafka.
- Scaling the Processing Tier to handle increased data volumes.
- Exploring the use of GPU acceleration for machine learning tasks.
- Improving the monitoring and alerting system.
- Implementing automated scaling via Kubernetes autoscaling.
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These tiers are distributed across multiple physical servers to ensure redundancy and scalability. We utilize a Microservices architecture to allow independent scaling and updating of components. Load balancing is crucial for distributing traffic. Database replication is implemented for data durability.
Hardware Specifications
The following table summarizes the hardware specifications for each server tier.
| Server Tier | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Ingestion Tier (x3) | Intel Xeon Silver 4210R (10 Cores) | 64GB DDR4 ECC | 2 x 1TB NVMe SSD (RAID 1) | 10Gbps Ethernet |
| Processing Tier (x5) | AMD EPYC 7763 (64 Cores) | 256GB DDR4 ECC | 4 x 2TB NVMe SSD (RAID 10) + 1 x 8TB HDD (Archive) | 25Gbps Ethernet |
| Storage Tier (x2) | Intel Xeon Gold 6248R (24 Cores) | 128GB DDR4 ECC | 8 x 16TB SAS HDD (RAID 6) | 40Gbps Ethernet |
| API Tier (x2) | Intel Xeon E-2288G (8 Cores) | 32GB DDR4 ECC | 1 x 500GB NVMe SSD | 1Gbps Ethernet |
These specifications are subject to change based on project needs and available resources. Regular capacity planning exercises are essential.
Software Stack
The software stack is based on open-source technologies.
Network Configuration
The server network is segmented into three zones: public, DMZ, and private. The ingestion tier servers reside in the DMZ, while the processing, storage, and API tiers are located in the private network. A firewall is configured to restrict traffic between zones.
| Zone | IP Range | Access Control |
|---|---|---|
| Public | 203.0.113.0/24 | Limited access to API Tier via reverse proxy. |
| DMZ | 192.168.1.0/24 | Access to Ingestion Tier only from Sensor Network. |
| Private | 10.0.0.0/16 | Restricted access between tiers based on service requirements. |
Detailed network diagrams are available on the Internal Wiki. Network security audits are conducted quarterly.
Data Storage and Backup
The Storage Tier utilizes a RAID 6 configuration for data redundancy. Data is backed up daily to an offsite location using rsync. A disaster recovery plan has been developed and tested.
| Data Type | Storage Location | Backup Frequency |
|---|---|---|
| Raw Sensor Data | Storage Tier (SAS HDD) | Daily (Offsite) |
| Processed Data | Storage Tier (SAS HDD) | Daily (Offsite) |
| Model Artifacts | Storage Tier (SAS HDD) | Weekly (Offsite) |
| Database (PostgreSQL) | Storage Tier (SAS HDD) | Daily (Offsite) |
Security Considerations
Security is a top priority.
Future Enhancements
Planned enhancements include:
Sensor Networks Data Analysis Machine Learning PostgreSQL Kubernetes Docker Linux Server Administration Network Administration Security Best Practices Disaster Recovery System Architecture Data Backup Load Balancing Database Replication Data Acquisition Systems
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.* ⚠️