AI in the Ligurian Sea

# AI in the Ligurian Sea: Server Configuration

This article details the server infrastructure supporting the “AI in the Ligurian Sea” project, a research initiative focused on real-time marine data analysis using artificial intelligence. This guide is intended for new system administrators and engineers joining the project. It covers hardware specifications, software stack, networking, and security considerations.

Project Overview

The “AI in the Ligurian Sea” project utilizes a network of underwater sensors and surface buoys to collect data on water temperature, salinity, currents, marine life, and pollution levels. This data is streamed to a central server cluster for processing by machine learning algorithms. The goal is to provide early warnings for environmental events, track marine species migration patterns, and improve understanding of the marine ecosystem. The project relies heavily on High Availability and Fault Tolerance.

Hardware Infrastructure

The server cluster is housed in a secure, climate-controlled data center in Genoa, Italy. It comprises three primary server types: data ingestion servers, processing servers, and storage servers. Each server type is built to specific specifications to optimize performance and reliability. We use a Redundant Power Supply setup for all servers.

Data Ingestion Servers

These servers are responsible for receiving data streams from the sensors and buoys. They perform initial data validation and buffering before sending the data to the processing servers.

Processing Servers

These servers perform the core AI processing tasks, including data cleaning, feature extraction, model training, and inference. They utilize GPUs for accelerated computation. These servers are crucial for Machine Learning Model Deployment.

Storage Servers

These servers provide high-capacity storage for raw sensor data, processed data, and model artifacts. They are configured for data redundancy and long-term preservation. Data Backup is performed nightly.

Software Stack

The software stack is designed for scalability, reliability, and ease of maintenance.

• Operating Systems: Ubuntu Server 22.04 LTS, CentOS Stream 9, Red Hat Enterprise Linux 8 (as detailed above)
• Programming Languages: Python 3.9, C++
• Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn
• Data Storage: PostgreSQL 14 with TimescaleDB extension. We use Database Replication for redundancy.
• Message Queue: RabbitMQ
• Containerization: Docker, Kubernetes
• Monitoring: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana)
• Version Control: Git, GitLab
• Configuration Management: Ansible
• Networking: Virtual LANs (VLANs) are used to segment network traffic.

Networking Configuration

The server cluster is connected to the internet via a dedicated 100 Gbps fiber optic link. Internal networking is based on a redundant spine-leaf architecture.

• Network Topology: Spine-Leaf
• Firewall: pfSense
• Load Balancing: HAProxy
• DNS: Bind9
• DHCP: ISC DHCP Server

Security Considerations

Security is paramount in the “AI in the Ligurian Sea” project.

• Firewall Rules: Strict firewall rules are in place to restrict access to the servers.
• Intrusion Detection System (IDS): Snort
• Intrusion Prevention System (IPS): Suricata
• Regular Security Audits: Performed quarterly by an external security firm.
• Data Encryption: All data is encrypted at rest and in transit. We use TLS/SSL for all network communication.
• Access Control: Role-based access control (RBAC) is implemented to limit access to sensitive data and resources. Two-Factor Authentication is enforced for all administrative accounts.
• Vulnerability Scanning: Nessus is used for regular vulnerability scanning.

Future Expansion

Planned future expansion includes adding additional processing servers with more powerful GPUs, increasing storage capacity, and implementing a distributed database architecture. We also plan to integrate with Cloud Computing services for scalability and disaster recovery.

Server Maintenance is scheduled weekly. Please refer to the Troubleshooting Guide for common issues. Contact the System Administrators group for assistance.

Category:Server Hardware

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.* ⚠️