AI in the Mediterranean Sea

AI in the Mediterranean Sea: Server Configuration

This article details the server configuration supporting the "AI in the Mediterranean Sea" project, a research initiative utilizing artificial intelligence to monitor and analyze marine ecosystems. This guide is aimed at newcomers to our MediaWiki site and provides a detailed overview of the hardware and software infrastructure. It assumes a basic understanding of server administration and networking concepts. Please refer to Help:Contents for general MediaWiki help.

Project Overview

The "AI in the Mediterranean Sea" project involves deploying a network of underwater sensors collecting data on temperature, salinity, marine life, and pollution levels. This data is transmitted to our central server cluster for processing and analysis using machine learning algorithms. The goal is to provide real-time insights into the health of the Mediterranean Sea and predict potential ecological changes. See Project Goals for more details.

Server Infrastructure

The project relies on a distributed server infrastructure composed of three primary tiers: Data Acquisition, Data Processing, and Data Storage. Each tier utilizes specialized hardware and software components. Refer to the Network Diagram for a visual representation of the server architecture.

Data Acquisition Servers

These servers are located near the coastal monitoring stations. They receive data from the underwater sensors and perform initial data validation and pre-processing. They use a lightweight operating system and minimal processing power.

Data Acquisition Server Specifications	Value
Ubuntu Server 22.04 LTS	Intel Celeron J4125	8 GB DDR4	256 GB SSD	1 Gbps Ethernet	MQTT over TLS

These servers employ MQTT broker software for secure data transmission. See Data Acquisition Protocol for specifics.

Data Processing Servers

This tier constitutes the core of the AI system. These servers are responsible for running the machine learning algorithms, analyzing the data, and generating insights. They require significant processing power and memory. These servers also use Docker containers to isolate and manage different AI models.

Data Processing Server Specifications	Value
CentOS Stream 9	2 x AMD EPYC 7763 (64 cores each)	256 GB DDR4 ECC	2 x 2 TB NVMe SSD (RAID 1)	10 Gbps Ethernet	4 x NVIDIA A100 (80GB)

The machine learning framework used is TensorFlow, with models trained on Large Datasets. We also use Kubernetes for orchestration of the AI workloads.

Data Storage Servers

These servers are dedicated to storing the raw and processed data. They require high storage capacity and reliability. Data is backed up daily to an offsite location using rsync.

Data Storage Server Specifications	Value
Debian 11	Intel Xeon Silver 4310	64 GB DDR4 ECC	8 x 16 TB SAS HDD (RAID 6)	10 Gbps Ethernet	ZFS

The database system used is PostgreSQL with a focus on time-series data. See Database Schema for detailed information.

Software Stack

The software stack consists of various open-source tools and libraries.

Operating Systems: Ubuntu Server, CentOS Stream, Debian
Programming Languages: Python, C++
Machine Learning Frameworks: TensorFlow, PyTorch
Database: PostgreSQL
Message Broker: MQTT
Containerization: Docker, Kubernetes
Monitoring: Prometheus, Grafana – see Monitoring Dashboard
Version Control: Git
Continuous Integration/Continuous Deployment (CI/CD): Jenkins

Networking Considerations

The servers are connected via a high-speed private network. Firewall rules are configured using iptables to restrict access to authorized personnel and services. Virtual Private Networks (VPNs) are used for remote access. See Network Security Policy for detailed configuration.

Future Expansion

We plan to expand the server infrastructure as the project grows. This will involve adding more Data Processing servers to handle the increasing data volume and complexity of the AI models. We are also exploring the use of Edge Computing to perform some of the data processing closer to the sensors, reducing latency and bandwidth requirements.

Main Page Data Analysis Techniques Sensor Calibration Troubleshooting Guide Security Protocols Data Privacy Policy Performance Optimization System Documentation Contact Information Frequently Asked Questions Glossary of Terms Hardware Maintenance Software Updates Backup and Recovery Disaster Recovery Plan API Documentation Environmental Monitoring

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.* ⚠️