AI in the Atlantic Ocean
- AI in the Atlantic Ocean: Server Configuration
This article details the server configuration powering the "AI in the Atlantic Ocean" project, a long-term initiative focused on real-time data analysis of marine ecosystems using artificial intelligence. This guide is intended for new contributors to the project’s infrastructure and provides a comprehensive overview of the hardware and software components. Understanding this configuration is crucial for system maintenance, troubleshooting, and future scalability efforts.
Project Overview
The "AI in the Atlantic Ocean" project relies on a network of underwater sensors, surface buoys, and a central server cluster. Data collected from these sources is transmitted via satellite links to our primary server farm located in Newfoundland, Canada. The server cluster processes this data, running complex machine learning algorithms to identify patterns, predict changes, and provide insights into the health of the Atlantic Ocean. The primary goal is to provide early warnings for events like harmful algal blooms, track marine mammal migration patterns, and assess the impact of climate change on the marine environment. Detailed data regarding sensor types is available on the Sensor Network Details page. We also maintain a Data Pipeline Documentation page.
Server Hardware
The server cluster is composed of three main tiers: ingestion, processing, and storage. Each tier utilizes specialized hardware optimized for its respective tasks. Network infrastructure details are found on the Network Topology page.
Ingestion Tier
This tier is responsible for receiving data from the sensors and preparing it for processing. It prioritizes high bandwidth and low latency.
| Component | Specification | Quantity |
|---|---|---|
| Server Model | Dell PowerEdge R750 | 4 |
| CPU | Intel Xeon Gold 6338 (32 cores/64 threads) | 4 |
| RAM | 256 GB DDR4 ECC Registered | 4 |
| Network Interface | Dual 100GbE QSFP28 | 4 |
| Storage (Temporary) | 2 x 1TB NVMe SSD (RAID 1) | 4 |
Processing Tier
This tier performs the core AI computations. It requires significant processing power and often utilizes GPU acceleration. Information on algorithm optimization is important here.
| Component | Specification | Quantity |
|---|---|---|
| Server Model | Supermicro SYS-220M-360 | 8 |
| CPU | AMD EPYC 7763 (64 cores/128 threads) | 8 |
| RAM | 512 GB DDR4 ECC Registered | 8 |
| GPU | NVIDIA A100 (80GB) | 8 |
| Network Interface | Dual 100GbE InfiniBand | 8 |
| Storage (Local) | 4 x 2TB NVMe SSD (RAID 0) | 8 |
Storage Tier
This tier provides long-term storage for the processed data. It emphasizes capacity and data redundancy. Details about data archiving policies are available.
| Component | Specification | Quantity |
|---|---|---|
| Storage Array | NetApp FAS8200 | 2 |
| Drive Type | 16TB SAS 7.2K RPM | 256 |
| Total Capacity (Raw) | 4PB | - |
| RAID Level | RAID-6 | - |
| Network Interface | Dual 40GbE | 2 |
Software Configuration
The software stack is built around a Linux distribution, specifically Ubuntu Server 22.04 LTS.
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker and Kubernetes are used for deploying and managing the AI algorithms and supporting services. The Kubernetes cluster configuration is hosted on GitLab.
- Programming Languages: Python is the primary language for developing and deploying the AI models. R is used for statistical analysis.
- Machine Learning Frameworks: TensorFlow and PyTorch are used for building and training the AI models.
- Database: PostgreSQL is used for storing metadata and processed data.
- Message Queue: RabbitMQ is used for asynchronous communication between different components.
- Monitoring: Prometheus and Grafana are used for monitoring the performance of the server cluster.
- Data Visualization: Jupyter Notebooks are used for interactive data exploration and visualization.
Security Considerations
Security is paramount for this project. Several measures are in place to protect the data and infrastructure:
- Firewall: A robust firewall configuration protects the server cluster from unauthorized access.
- Intrusion Detection System (IDS): An IDS monitors network traffic for malicious activity.
- Data Encryption: Data is encrypted both in transit and at rest.
- Regular Security Audits: Regular security audits are conducted to identify and address vulnerabilities. See the Security Audit Reports for details.
- Access Control: Strict access control policies are enforced to limit access to sensitive data and resources.
Future Scalability
As the project grows and the volume of data increases, the server cluster will need to be scaled. Future scalability plans include:
- Adding more servers to the processing tier.
- Upgrading the network infrastructure to support higher bandwidth.
- Implementing a distributed storage system.
- Exploring the use of cloud computing resources. Details of the Cloud Migration Plan are available.
Internal Links to Relevant Pages Data Management Procedures Deployment Guidelines Troubleshooting Guide System Architecture Overview Code Repository Access API Documentation Sensor Calibration Procedures Emergency Contact List Backup and Recovery Plan Change Management Policy Server Monitoring Dashboard Security Incident Response Plan Networking Configuration Details Disaster Recovery Plan Version Control System
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.* ⚠️