AI in the Arabian Sea
- AI in the Arabian Sea: Server Configuration
This article details the server configuration powering the "AI in the Arabian Sea" project, a research initiative focused on real-time environmental monitoring and predictive modeling using artificial intelligence. This documentation is intended for new engineers joining the project or those seeking to understand the system's architecture.
Overview
The "AI in the Arabian Sea" project utilizes a distributed server architecture to process data collected from a network of sensors deployed throughout the Arabian Sea. These sensors capture data related to temperature, salinity, currents, marine life activity, and weather patterns. The system employs machine learning algorithms to analyze this data, predict potential environmental changes, and provide actionable insights. The core infrastructure consists of three primary tiers: Data Acquisition, Processing, and Prediction. Each tier leverages specialized server hardware and software configurations optimized for its specific task. This document focuses on the server configurations within each tier.
Data Acquisition Tier
The Data Acquisition Tier is responsible for receiving and initially processing data from the sensor network. This tier emphasizes reliability and low latency. Servers in this tier are geographically distributed to minimize the impact of localized outages.
Server Specifications
| Server Role | Hardware Specification | Software Configuration |
|---|---|---|
| Edge Servers (x12) | CPU: Intel Xeon Silver 4210R RAM: 64GB DDR4 ECC Storage: 1TB NVMe SSD Network: Dual 10GbE NICs |
Operating System: Ubuntu Server 22.04 LTS Data Collection Agent: Custom Python script utilizing MQTT Database: SQLite (local caching) |
| Aggregation Server (x2) | CPU: Intel Xeon Gold 6248R RAM: 128GB DDR4 ECC Storage: 2TB NVMe SSD (RAID 1) Network: Quad 10GbE NICs |
Operating System: CentOS Stream 9 Message Queue: RabbitMQ Database: PostgreSQL 14 |
These Edge Servers use a lightweight data collection agent to filter and pre-process the sensor data before forwarding it to the Aggregation Servers. The Aggregation Servers collect data from multiple Edge Servers, perform initial validation, and queue it for further processing. See Data Pipelines for more details on data flow. The choice of PostgreSQL is detailed in Database Selection Rationale. Maintaining high availability is covered in High Availability Design.
Processing Tier
The Processing Tier is the core of the AI system. It's responsible for cleaning, transforming, and enriching the data received from the Data Acquisition Tier. This tier utilizes high-performance computing (HPC) resources to handle the computationally intensive tasks of data processing.
Server Specifications
| Server Role | Hardware Specification | Software Configuration |
|---|---|---|
| Data Processing Nodes (x8) | CPU: AMD EPYC 7763 RAM: 256GB DDR4 ECC Storage: 4TB NVMe SSD (RAID 0) Network: 100GbE NICs GPU: NVIDIA A100 (80GB) x 4 |
Operating System: Rocky Linux 9 Data Processing Framework: Apache Spark 3.4 Programming Language: Python with PyTorch and TensorFlow |
| Feature Store Server (x1) | CPU: Intel Xeon Platinum 8380 RAM: 512GB DDR4 ECC Storage: 8TB NVMe SSD (RAID 6) Network: 40GbE NICs |
Operating System: Ubuntu Server 22.04 LTS Feature Store: Feast Database: Cassandra |
The Data Processing Nodes employ Apache Spark to distribute the processing workload across multiple cores and GPUs. The Feature Store Server manages the curated features used by the machine learning models. We utilize Feast due to its scalability and integration with Spark. See Spark Configuration for details on Spark tuning. Understanding GPU utilization is explained in GPU Performance Monitoring.
Prediction Tier
The Prediction Tier is responsible for running the trained machine learning models and generating predictions based on the processed data. This tier requires low latency and high throughput to deliver real-time insights.
Server Specifications
| Server Role | Hardware Specification | Software Configuration |
|---|---|---|
| Model Serving Nodes (x6) | CPU: Intel Xeon Gold 6338 RAM: 128GB DDR4 ECC Storage: 1TB NVMe SSD Network: 25GbE NICs GPU: NVIDIA T4 x 2 |
Operating System: Debian 11 Model Serving Framework: TensorFlow Serving Containerization: Docker Orchestration: Kubernetes |
| API Gateway Server (x2) | CPU: Intel Xeon Silver 4210 RAM: 32GB DDR4 ECC Storage: 500GB SATA SSD Network: 10GbE NICs |
Operating System: Ubuntu Server 22.04 LTS API Gateway: Kong Load Balancing: Nginx |
The Model Serving Nodes leverage TensorFlow Serving to efficiently deploy and serve the trained models. Kubernetes is used to orchestrate the deployment and scaling of these nodes. The API Gateway Server provides a unified interface for accessing the predictions. See Kubernetes Deployment Guide for details on deployment. Understanding latency requirements is vital, see Latency Optimization. Security considerations are described in Security Best Practices.
Network Infrastructure
The entire system is interconnected via a dedicated 100GbE fiber optic network. Network segmentation is implemented to isolate the different tiers and enhance security. Detailed network diagrams are available in Network Topology.
Future Considerations
Future upgrades will include exploring the use of specialized AI accelerators (e.g., Google TPUs) and expanding the distributed storage capacity. We are also investigating the integration of federated learning techniques to improve model accuracy and privacy. See Future Development Roadmap for more details.
Data Security Monitoring and Alerting Disaster Recovery Plan Version Control Strategy Change Management Process
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.* ⚠️