AI in the Tyrrhenian Sea
AI in the Tyrrhenian Sea: Server Configuration
This document details the server configuration supporting the "AI in the Tyrrhenian Sea" project, a real-time data analysis and predictive modeling initiative focused on marine ecosystems within the Tyrrhenian Sea. This article is intended for new engineers joining the project and provides a comprehensive overview of the hardware, software, and networking infrastructure. Understanding this configuration is crucial for maintenance, troubleshooting, and future scaling. Please refer to the Server Administration Guide for general server policies.
Project Overview
The "AI in the Tyrrhenian Sea" project relies on data collected from a network of submerged sensors, satellite feeds, and research vessels. This data includes temperature readings, salinity levels, marine life detection (using Acoustic Monitoring, Optical Sensors, and DNA Sequencing, current patterns, and pollution levels. The project uses machine learning algorithms, specifically Deep Learning and Time Series Analysis, to predict algal blooms, track marine animal migration patterns, and identify potential environmental threats. Data processing occurs in near real-time, requiring a robust and scalable server infrastructure. The project is heavily reliant on Data Integrity Checks to ensure reliable results.
Hardware Configuration
The core server infrastructure consists of three primary server clusters: Ingestion, Processing, and Storage. Each cluster is designed for redundancy and high availability. A separate Monitoring System provides constant oversight.
| Server Role | Server Count | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|---|
| Ingestion (Data Acquisition) | 3 | Intel Xeon Gold 6338 (32 cores) | 128 GB DDR4 ECC | 2 x 1 TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Processing (AI/ML) | 6 | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC | 4 x 2 TB NVMe SSD (RAID 0) + 2 x 80 GB NVMe SSD (Caching) | 40 Gbps InfiniBand |
| Storage (Data Archival) | 5 | Intel Xeon Silver 4310 (12 cores) | 64 GB DDR4 ECC | 16 x 8 TB SAS HDD (RAID 6) | 25 Gbps Ethernet |
All servers are housed in a dedicated, climate-controlled data center with redundant power supplies and backup generators. The power distribution is handled by a Smart Power Distribution Unit for efficient energy management.
Software Stack
The software stack is built around a Linux distribution (Ubuntu Server 22.04 LTS) and utilizes containerization technology (Docker) for application deployment and management. The project leverages Kubernetes for orchestration of the Docker containers. Below are the key software components:
| Component | Version | Purpose | Notes |
|---|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers | Security patches applied automatically. |
| Docker | 20.10.14 | Containerization platform | Used for packaging and deploying applications. |
| Kubernetes | 1.24.0 | Container orchestration | Manages the deployment, scaling, and operation of containerized applications. |
| Python | 3.10 | Programming language for AI/ML scripts | Utilizes virtual environments for dependency management. |
| TensorFlow | 2.9.1 | Machine learning framework | Primary framework for training and deploying models. |
| PostgreSQL | 14.5 | Database | Stores metadata, sensor configurations, and historical data summaries. |
| Grafana | 8.4.2 | Data visualization | Displays real-time data and performance metrics. |
We also utilize a Version Control System (Git) hosted on a private GitLab instance for all code management. Regular Backup Procedures are in place to prevent data loss.
Networking Configuration
The server clusters are interconnected via a high-speed network infrastructure. The network topology is a full mesh, providing multiple paths for data transmission. Network security is a high priority, with firewalls and intrusion detection systems in place. Access to the server infrastructure is restricted to authorized personnel only, using Multi-Factor Authentication.
| Network Segment | IP Range | Purpose | Security Level |
|---|---|---|---|
| Ingestion Cluster | 192.168.1.0/24 | Data acquisition and pre-processing | High |
| Processing Cluster | 192.168.2.0/24 | AI/ML model training and inference | Highest |
| Storage Cluster | 192.168.3.0/24 | Data archival and retrieval | Medium |
| Management Network | 10.0.0.0/24 | Server administration and monitoring | Highest |
DNS resolution is handled by a local BIND DNS Server. All network traffic is monitored using a Network Intrusion Detection System.
Future Scaling
As the data volume and complexity of the AI models increase, the server infrastructure will need to be scaled. Planned upgrades include adding more processing nodes to the Processing Cluster and expanding the storage capacity of the Storage Cluster. We are also investigating the use of GPU Acceleration to further improve the performance of the machine learning algorithms. The current architecture is designed to facilitate horizontal scaling.
Server Administration Guide
Acoustic Monitoring
Optical Sensors
DNA Sequencing
Deep Learning
Time Series Analysis
Data Integrity Checks
Monitoring System
Smart Power Distribution Unit
Version Control System
Backup Procedures
Kubernetes
BIND DNS Server
Network Intrusion Detection System
Multi-Factor Authentication
GPU Acceleration
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.* ⚠️