AI in the Bay of Bengal

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AI in the Bay of Bengal: Server Configuration

This document details the server configuration supporting the "AI in the Bay of Bengal" project, a real-time marine environmental monitoring and prediction system utilizing artificial intelligence. This guide is aimed at new contributors to the project and provides a comprehensive overview of the server infrastructure. It covers hardware specifications, software stack, networking, and security considerations.

Project Overview

The "AI in the Bay of Bengal" project aims to leverage data from a network of buoys, satellites, and coastal sensors to predict phenomena like cyclone formation, sea level rise, algal blooms, and changes in marine biodiversity. The system employs machine learning models for forecasting and anomaly detection, requiring significant computational resources and a robust server infrastructure. This documentation outlines that infrastructure. Data is processed using Python and TensorFlow, with results accessible via a web interface built with PHP and JavaScript. The system relies heavily on PostgreSQL for data storage and Redis for caching. All code is version controlled via Git.

Hardware Specifications

The server infrastructure is distributed across two primary locations: a primary data center in Chennai, India, and a secondary disaster recovery site in Singapore. The Chennai site houses the primary processing and storage servers, while the Singapore site provides redundancy and failover capabilities.

The Chennai Data Center consists of the following servers:

Server Role CPU RAM Storage Network Interface
Primary Processing (x4) 2 x Intel Xeon Gold 6338 (32 cores/64 threads) 256 GB DDR4 ECC REG 8 x 4TB NVMe SSD (RAID 0) 100 Gbps Ethernet
Data Storage (x3) 2 x AMD EPYC 7763 (64 cores/128 threads) 512 GB DDR4 ECC REG 24 x 16TB SAS HDD (RAID 6) 40 Gbps Ethernet
Database Server (x2) 2 x Intel Xeon Silver 4310 (12 cores/24 threads) 128 GB DDR4 ECC REG 2 x 1TB NVMe SSD (RAID 1) 25 Gbps Ethernet
Caching Server (x2) 2 x Intel Xeon E-2388G (8 cores/16 threads) 64 GB DDR4 ECC REG 2 x 960GB NVMe SSD (RAID 1) 10 Gbps Ethernet

The Singapore Disaster Recovery site mirrors the database and caching servers:

Server Role CPU RAM Storage Network Interface
Database Server (x2) 2 x Intel Xeon Silver 4310 (12 cores/24 threads) 128 GB DDR4 ECC REG 2 x 1TB NVMe SSD (RAID 1) 25 Gbps Ethernet
Caching Server (x2) 2 x Intel Xeon E-2388G (8 cores/16 threads) 64 GB DDR4 ECC REG 2 x 960GB NVMe SSD (RAID 1) 10 Gbps Ethernet

Software Stack

The following software components are utilized:

Networking Configuration

The servers are interconnected using a dedicated 100Gbps fiber optic network within the Chennai data center. Connectivity to the internet is provided through multiple Tier-1 providers for redundancy. The Singapore site is connected to the Chennai site via a dedicated encrypted VPN tunnel. A firewall is implemented at the network perimeter to protect against unauthorized access. Internal network segmentation is used to isolate different server roles. DNS is managed using BIND9.

Component Configuration Details
Primary Network 10.0.0.0/8 (Chennai), 192.168.1.0/24 (Singapore)
Firewall pfSense 2.5.2 with intrusion detection and prevention systems
Load Balancer HAProxy for distributing traffic to processing servers.
VPN IPsec tunnel between Chennai and Singapore

Security Considerations

Security is paramount due to the sensitive nature of the data. The following measures are in place:

  • Regular security audits and vulnerability scans.
  • Intrusion detection and prevention systems (IDS/IPS).
  • Strong password policies and multi-factor authentication.
  • Data encryption at rest and in transit.
  • Regular backups and disaster recovery procedures.
  • Access control lists (ACLs) to restrict access to sensitive resources.
  • Up-to-date software patches and security updates.
  • Implementation of SELinux for enhanced security.

Future Enhancements

Planned future enhancements include:

  • Implementation of a Kubernetes cluster for improved scalability and orchestration.
  • Migration to a cloud-based infrastructure for increased flexibility and cost-effectiveness.
  • Integration of additional data sources, such as weather radar data.
  • Development of more sophisticated machine learning models.


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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