AI in the Aegean Sea

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AI in the Aegean Sea: Server Configuration

This document details the server configuration for the "AI in the Aegean Sea" project, a research initiative focused on real-time marine data analysis using artificial intelligence. This article is intended for new team members and system administrators responsible for maintaining the project's infrastructure. We will cover hardware specifications, software stack, network topology, and security considerations. Familiarity with Linux server administration and MediaWiki syntax is recommended.

Hardware Overview

The project utilizes a distributed server architecture, consisting of three primary server types: Data Acquisition Servers (DAS), Processing Servers (PS), and a central Management Server (MS). Each server type is tailored to its specific role. All servers are hosted in a climate-controlled facility in Athens, Greece, ensuring optimal operating conditions. Power redundancy is provided via UPS and a backup generator. Detailed specifications are provided below.

Server Type CPU RAM Storage Network Interface
Data Acquisition Server (DAS) Intel Xeon Silver 4310 (8 Cores) 64 GB DDR4 ECC 4TB NVMe SSD (RAID 1) 10 Gbps Ethernet
Processing Server (PS) AMD EPYC 7763 (64 Cores) 256 GB DDR4 ECC 8TB NVMe SSD (RAID 0) + 64TB HDD (RAID 6) 100 Gbps Ethernet
Management Server (MS) Intel Core i7-12700K (12 Cores) 32 GB DDR5 ECC 1TB NVMe SSD 1 Gbps Ethernet

Software Stack

Each server runs a customized build of Ubuntu Server 22.04 LTS. The core software stack is detailed below. We utilize Docker for containerization and Kubernetes for orchestration of the AI models on the Processing Servers. The Data Acquisition Servers utilize ROS 2 for data ingestion and initial processing. The Management Server employs Prometheus and Grafana for system monitoring.

Server Type Operating System Core Software Additional Software
Data Acquisition Server (DAS) Ubuntu Server 22.04 LTS ROS 2 Foxy Fitzroy, Python 3.8 MQTT Broker (Mosquitto), PostgreSQL
Processing Server (PS) Ubuntu Server 22.04 LTS CUDA Toolkit 11.8, TensorFlow 2.10, PyTorch 1.12, Kubernetes 1.24 NVIDIA drivers, Docker 20.10
Management Server (MS) Ubuntu Server 22.04 LTS Prometheus, Grafana, Ansible Git, SSH Server

Network Topology and Security

The servers are interconnected via a dedicated VLAN. The Data Acquisition Servers communicate with the Processing Servers via a high-bandwidth, low-latency network connection. The Management Server has restricted access to all other servers via SSH and a dedicated VPN connection for remote administration. A firewall, configured with iptables, protects the network from external threats. All data transmission is encrypted using TLS/SSL. Regular security audits are conducted to ensure system integrity. Internal DNS is managed by a dedicated server using BIND9.

Component IP Address Range Subnet Mask Gateway
Data Acquisition Servers 192.168.10.0/24 255.255.255.0 192.168.10.1
Processing Servers 192.168.20.0/24 255.255.255.0 192.168.20.1
Management Server 192.168.30.10 255.255.255.0 192.168.30.1

Data Flow

Data is collected by the DAS from various sensors deployed in the Aegean Sea. This data, including temperature, salinity, and current velocity, is pre-processed locally and then transmitted to the PS via MQTT. The PS utilizes AI models, trained on historical data, to identify anomalies and predict future trends. The results are stored in a time-series database (InfluxDB) and visualized using Grafana on the Management Server. Automated alerts are generated via Prometheus based on predefined thresholds. The system leverages message queues (RabbitMQ) for asynchronous communication between components.

Future Considerations

Planned upgrades include increasing the storage capacity of the Processing Servers and implementing a more robust security infrastructure utilizing intrusion detection systems. We are also evaluating the feasibility of utilizing GPU virtualization to improve resource utilization and reduce hardware costs. Further research is being conducted on the application of federated learning to enhance the AI models without compromising data privacy.

Special:Search for related articles. See also Server maintenance procedures and Troubleshooting guide.


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Core i9-13900 Server (128GB) 128 GB RAM, 2x2 TB NVMe SSD
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Core i5-13500 Server (128GB) 128 GB RAM, 2x500 GB NVMe SSD
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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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