AI in the Arabian Sea

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  1. 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.* ⚠️

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