AI in the Micronesian Rainforest
AI in the Micronesian Rainforest: Server Configuration
This article details the server configuration for the "AI in the Micronesian Rainforest" project, a research initiative focused on biodiversity monitoring and species identification using artificial intelligence. This documentation is intended for new system administrators and developers joining the project. Understanding these configurations is crucial for maintaining system stability and contributing to the research. We utilize a distributed server architecture to handle the large datasets generated by the sensor network deployed within the rainforest.
Project Overview
The "AI in the Micronesian Rainforest" project employs a network of acoustic sensors, camera traps, and environmental sensors to collect data on the rainforest ecosystem. This data is processed using machine learning algorithms to identify species, monitor population trends, and detect potential threats to biodiversity. The server infrastructure supports data ingestion, storage, processing, and visualization. Data privacy is handled according to the data governance policy.
Server Architecture
The system utilizes a three-tier architecture:
- **Ingestion Tier:** Responsible for receiving data from the sensors and initial processing.
- **Processing Tier:** Handles the computationally intensive machine learning tasks.
- **Presentation Tier:** Provides a web interface for researchers to access and visualize the data.
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Each tier is composed of multiple servers for redundancy and scalability. The network is secured by a firewall configuration and monitored by a system monitoring dashboard.
Ingestion Tier Configuration
The Ingestion Tier consists of three servers, each running a custom data ingestion script written in Python. These servers are responsible for receiving data from the sensor network and storing it in a PostgreSQL database.
| Server Name | Operating System | CPU | RAM | Storage |
|---|---|---|---|---|
| ingestion-01 | Ubuntu Server 22.04 LTS | Intel Xeon Silver 4210 | 64 GB | 4 TB RAID 1 |
| ingestion-02 | Ubuntu Server 22.04 LTS | Intel Xeon Silver 4210 | 64 GB | 4 TB RAID 1 |
| ingestion-03 | Ubuntu Server 22.04 LTS | Intel Xeon Silver 4210 | 64 GB | 4 TB RAID 1 |
These servers utilize a message queue system, RabbitMQ, to handle data bursts from the sensors. The data is initially stored in a raw format before being processed and normalized. Regular database backups are performed to prevent data loss.
Processing Tier Configuration
The Processing Tier is the core of the AI system. It houses the machine learning models and performs the data analysis. This tier consists of four servers, each equipped with high-performance GPUs. The primary software used here is TensorFlow.
| Server Name | Operating System | CPU | RAM | GPU | Storage |
|---|---|---|---|---|---|
| processing-01 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6248R | 128 GB | NVIDIA Tesla V100 | 8 TB RAID 0 |
| processing-02 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6248R | 128 GB | NVIDIA Tesla V100 | 8 TB RAID 0 |
| processing-03 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6248R | 128 GB | NVIDIA Tesla V100 | 8 TB RAID 0 |
| processing-04 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6248R | 128 GB | NVIDIA Tesla V100 | 8 TB RAID 0 |
These servers communicate with the Ingestion Tier via the PostgreSQL database and utilize a distributed computing framework, Apache Spark, to parallelize the machine learning tasks. Model training is performed nightly, and the updated models are deployed automatically using a continuous integration/continuous deployment (CI/CD) pipeline.
Presentation Tier Configuration
The Presentation Tier provides a web interface for researchers to access and visualize the data. It consists of two servers running a web application built with Flask and a front-end developed using React.
| Server Name | Operating System | CPU | RAM | Storage |
|---|---|---|---|---|
| presentation-01 | Ubuntu Server 22.04 LTS | Intel Core i7-12700 | 32 GB | 1 TB SSD |
| presentation-02 | Ubuntu Server 22.04 LTS | Intel Core i7-12700 | 32 GB | 1 TB SSD |
These servers are load balanced using Nginx to ensure high availability and responsiveness. The web application connects to the PostgreSQL database to retrieve and display the data. Access to the web interface is controlled by a user authentication system.
Networking Considerations
All servers are connected via a dedicated 10 Gigabit Ethernet network. A virtual private network (VPN) is used to provide secure remote access to the servers. The network is segmented to isolate the different tiers and enhance security. Detailed network diagrams are available in the network documentation.
Future Expansion
Future expansion plans include adding more servers to the Processing Tier to handle increased data volumes and more complex machine learning models. We are also evaluating the use of Kubernetes to orchestrate the containerized applications.
Special:Search/Sensor Network Special:Search/Data Governance Policy Special:Search/Firewall Configuration Special:Search/System Monitoring Dashboard Special:Search/Python Special:Search/PostgreSQL Special:Search/RabbitMQ Special:Search/TensorFlow Special:Search/Apache Spark Special:Search/Flask Special:Search/React Special:Search/Nginx Special:Search/User Authentication System Special:Search/Database Backups Special:Search/Network Documentation Special:Search/Continuous Integration Special:Search/Virtual Private Network
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.* ⚠️