AI in the Pacific Ocean
AI in the Pacific Ocean: Server Configuration
This document details the server configuration for the “AI in the Pacific Ocean” project, a research initiative focused on real-time data analysis of oceanic conditions using advanced Artificial Intelligence. This guide is intended for new system administrators and engineers joining the team. It covers hardware specifications, software stack, network topology, and security considerations.
Overview
The “AI in the Pacific Ocean” project relies on a distributed server network deployed across three primary locations: Hawaii, Guam, and American Samoa. Each location hosts a cluster of servers responsible for data ingestion, processing, model training, and data dissemination. Redundancy is a key design principle, with each location capable of independently performing all core functions. The project leverages a hybrid cloud approach, utilizing on-premise hardware for low-latency data processing and cloud services for long-term storage and complex model training. See also Data Flow Diagram for a visual representation of the system.
Hardware Specifications
The core compute nodes at each location are based on a standardized hardware configuration. The following table details the specifications for a single compute node:
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 Cores/64 Threads) |
| RAM | 256 GB DDR4 ECC Registered 3200 MHz |
| Storage (OS) | 1 TB NVMe PCIe Gen4 SSD |
| Storage (Data) | 8 x 16 TB SAS HDD in RAID 6 |
| GPU | 2 x NVIDIA A100 80GB |
| Network Interface | Dual 100 Gbps Ethernet |
| Power Supply | 2 x 1600W Redundant Power Supplies |
In addition to the compute nodes, each location also features dedicated storage servers and network appliances. A summary of the storage server specifications is provided below:
| Component | Specification |
|---|---|
| CPU | Intel Xeon Silver 4310 (12 Cores/24 Threads) |
| RAM | 128 GB DDR4 ECC Registered 3200 MHz |
| Storage | 64 x 16 TB SAS HDD in RAID Z2 |
| Network Interface | Quad 25 Gbps Ethernet |
| Capacity | 1 PB usable storage |
Finally, network infrastructure is standardized across all locations. See Network Configuration Details for more information.
Software Stack
The software stack is built around a Linux operating system and utilizes a containerized environment for application deployment.
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker and Kubernetes
- Programming Languages: Python 3.9, C++
- AI Frameworks: TensorFlow 2.10, PyTorch 1.12
- Database: PostgreSQL 14 with TimescaleDB extension for time-series data. See Database Schema Documentation
- Message Queue: Kafka for asynchronous communication between services. Refer to Kafka Cluster Setup
- Monitoring: Prometheus and Grafana for system monitoring and alerting. More information is available at Monitoring Dashboard Guide.
- Data Ingestion: Apache NiFi for automated data pipeline management. NiFi Flow Configuration details the current flows.
- Version Control: Git with GitLab for code management and collaboration.
Network Topology
Each location is connected to the internet via a dedicated 10 Gbps fiber optic link. Internal network communication utilizes a flat network topology with VLANs to segment traffic. A high-level overview of the network topology is shown below:
| Location | Internal Network | External Connection |
|---|---|---|
| Hawaii | 10.0.1.0/24 | 10 Gbps Fiber |
| Guam | 10.0.2.0/24 | 10 Gbps Fiber |
| American Samoa | 10.0.3.0/24 | 10 Gbps Fiber |
Inter-location communication is secured via IPsec VPN tunnels. See VPN Configuration Guide for detailed instructions. DNS is managed internally using Bind9. Firewall rules are managed using iptables.
Security Considerations
Security is paramount for the “AI in the Pacific Ocean” project. The following security measures are in place:
- Firewall: Strict firewall rules are enforced to restrict network access.
- Intrusion Detection/Prevention: Suricata is used for intrusion detection and prevention. See Suricata Ruleset for current rule definitions.
- Access Control: Role-Based Access Control (RBAC) is implemented for all systems.
- Data Encryption: Data is encrypted in transit and at rest using AES-256.
- Regular Security Audits: Regular security audits are conducted to identify and address vulnerabilities.
- Multi-Factor Authentication: MFA is enforced for all administrative access.
- Vulnerability Scanning: Nessus is used for regular vulnerability scanning. See Vulnerability Scan Reports.
Future Expansion
Planned future expansion includes increasing the number of compute nodes at each location, integrating additional data sources (e.g., satellite imagery), and deploying edge computing devices to further reduce latency. See Project Roadmap for details.
Related Pages
- Data Flow Diagram
- Network Configuration Details
- Database Schema Documentation
- Kafka Cluster Setup
- Monitoring Dashboard Guide
- NiFi Flow Configuration
- VPN Configuration Guide
- Suricata Ruleset
- Vulnerability Scan Reports
- Project Roadmap
- Server Maintenance Schedule
- Troubleshooting Guide
- Disaster Recovery Plan
- Contact Information
- Software License Information
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.* ⚠️