AI in Adaptive Learning
- AI in Adaptive Learning: Server Configuration
This article details the server configuration required to support an Adaptive Learning system powered by Artificial Intelligence (AI). This setup assumes a moderate-sized implementation serving approximately 500 concurrent users. Scalability considerations are included where appropriate. This guide is aimed at system administrators new to deploying AI-driven educational platforms on our MediaWiki infrastructure.
Overview
Adaptive Learning utilizes AI algorithms to personalize the learning experience for each student. This requires significant computational resources for model training, inference, and data storage. The server infrastructure must be robust, scalable, and capable of handling large datasets. We'll focus on the key components: Application Servers, Database Servers, AI/ML Processing Servers, and Storage. Understanding the interplay between these is crucial for optimal performance. See also System Architecture for a broader overview of our platform.
Application Servers
Application Servers handle user requests, manage sessions, and interact with the database and AI/ML processing servers. They are the primary interface for students and instructors.
| Specification | Value |
|---|---|
| Number of Servers | 3 (Load balanced) |
| CPU | Intel Xeon Gold 6248R (24 cores/server) |
| RAM | 128GB DDR4 ECC Registered |
| Operating System | Ubuntu Server 22.04 LTS |
| Web Server | Apache 2.4 |
| Application Framework | PHP 8.2 with Symfony framework |
We utilize a load balancer (e.g., HAProxy or Nginx) to distribute traffic across these servers, ensuring high availability and responsiveness. Consider using a Content Delivery Network (CDN) for static assets to further reduce load. Regular monitoring using Nagios or Zabbix is essential.
Database Servers
The database stores student data, learning materials, and AI model metadata. A robust and scalable database solution is paramount.
| Specification | Value |
|---|---|
| Number of Servers | 2 (Primary/Replica) |
| Database System | PostgreSQL 15 |
| CPU | Intel Xeon Silver 4310 (12 cores/server) |
| RAM | 64GB DDR4 ECC Registered |
| Storage | 2TB NVMe SSD (RAID 1) |
| Replication | Asynchronous Replication |
PostgreSQL is chosen for its reliability, data integrity features, and support for complex queries. Regular backups are performed using pg_dump. Database performance is monitored using pgAdmin. Consider using database sharding for extremely large datasets. Proper database indexing is crucial for query performance.
AI/ML Processing Servers
These servers are responsible for running the AI/ML algorithms that power the adaptive learning system. This includes model training, inference, and data preprocessing.
| Specification | Value |
|---|---|
| Number of Servers | 4 (Dedicated to AI/ML) |
| CPU | AMD EPYC 7763 (64 cores/server) |
| RAM | 256GB DDR4 ECC Registered |
| GPU | 4x NVIDIA A100 (40GB VRAM/GPU) |
| Operating System | Ubuntu Server 22.04 LTS |
| AI/ML Frameworks | TensorFlow, PyTorch, Scikit-learn |
These servers are equipped with high-performance GPUs to accelerate AI/ML computations. We use Kubernetes to orchestrate the deployment and scaling of AI/ML models. Model versioning is managed using MLflow. Monitoring GPU utilization is critical, using tools like nvidia-smi. Consider using dedicated message queues like RabbitMQ or Kafka for asynchronous task processing. See also GPU Configuration Guide.
Storage Infrastructure
A scalable and reliable storage infrastructure is required to store large datasets of student data, learning materials, and AI model artifacts.
- Object Storage: We utilize MinIO for storing large files (videos, images, datasets).
- Network File System (NFS): Used for sharing files between servers.
- Backup Storage: Tape backups are used for long-term archival.
Networking
A high-bandwidth, low-latency network is essential for connecting all the servers.
- Network Topology: Star topology with a central core switch.
- Network Speed: 10 Gigabit Ethernet.
- Firewall: iptables is used to secure the network.
Security Considerations
Security is paramount. We implement the following measures:
- Regular Security Audits: Performed by our Security Team.
- Intrusion Detection System (IDS): Using Snort.
- Access Control: Role-Based Access Control (RBAC) is implemented.
- Data Encryption: Data is encrypted at rest and in transit.
Scalability
The system is designed to be scalable. We can add more application servers, database servers, and AI/ML processing servers as needed. We also utilize autoscaling features in Kubernetes to automatically scale AI/ML models based on demand. Horizontal scaling is preferred over vertical scaling.
Server Monitoring Deployment Process Troubleshooting Guide System Documentation Backup and Recovery Disaster Recovery Plan Performance Tuning Security Policy Capacity Planning Network Configuration Database Administration AI Model Management User Account Management Change Management
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 |
Order Your Dedicated Server
Configure and order your ideal server configuration
Need Assistance?
- Telegram: @powervps Servers at a discounted price
⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️