AI in the Tyrrhenian Sea

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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.* ⚠️

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