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Automated server configuration article

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# AI in Aruba: Server Configuration & Deployment

This article details the server configuration used to support Artificial Intelligence (AI) workloads within the Aruba Networks environment. This is intended as a guide for new systems administrators and engineers deploying or maintaining AI-related infrastructure.

== Overview ==

The “AI in Aruba” initiative leverages a hybrid server architecture, combining on-premise hardware for low-latency processing with cloud-based resources for model training and large dataset storage. This allows for real-time insights from network data while maintaining scalability and cost-effectiveness. This document focuses on the on-premise server configuration, which forms the core of the real-time analytics pipeline. We utilize a combination of high-performance compute servers and dedicated storage arrays. Understanding the interplay between [[Network Infrastructure]] and server performance is crucial.

== Hardware Specifications ==

The core processing is handled by dedicated GPU servers. The following table outlines the key specifications for these servers:

{| class="wikitable"
! Component
! Specification
! Quantity per Server
|-
| CPU
| Dual Intel Xeon Gold 6338 (32 cores/64 threads per CPU)
| 2
|-
| RAM
| 512 GB DDR4 ECC Registered 3200MHz
| 1
|-
| GPU
| NVIDIA A100 80GB PCIe 4.0
| 4
|-
| Storage (OS)
| 500GB NVMe PCIe Gen4 SSD
| 1
|-
| Storage (Data)
| 4TB NVMe PCIe Gen4 SSD (RAID 0)
| 1
|-
| Network Interface
| Dual 100GbE QSFP28
| 1
|-
| Power Supply
| 2000W Redundant Platinum
| 2
|}

These servers are housed in a dedicated, climate-controlled server room with redundant power and cooling. Proper [[Server Room Management]] is essential for maintaining stability. See also [[Power Distribution Units]] for details on power infrastructure.

== Storage Configuration ==

Data storage is a critical component, requiring both high capacity and low latency. We employ a tiered storage approach. Raw data is stored in a cloud-based object storage solution (AWS S3, Google Cloud Storage), while frequently accessed data is cached on-premise.

The on-premise caching layer utilizes a dedicated NVMe storage array. Details are below:

{| class="wikitable"
! Parameter
! Value
|-
| Vendor
| Pure Storage
|-
| Model
| FlashArray//X70
|-
| Capacity
| 100TB Raw
|-
| Usable Capacity
| 50TB (after RAID and Deduplication)
|-
| RAID Level
| RAID-TP (Triple Parity)
|-
| Connectivity
| 100GbE iSCSI
|-
| Data Reduction
| Inline Deduplication & Compression
|}

This array provides the necessary I/O performance for real-time data analysis. Understanding [[Storage Area Networks]] is crucial for managing this infrastructure. We also employ [[Data Backup Strategies]] to protect against data loss.

== Software Stack ==

The software stack is built on a Linux foundation, providing flexibility and control.

{| class="wikitable"
! Component
! Version
|-
| Operating System
| Ubuntu Server 22.04 LTS
|
|-
| Containerization
| Docker 24.0.5
|
|-
| Orchestration
| Kubernetes 1.27
|
|-
| AI Framework
| TensorFlow 2.13.0
|
|-
| Programming Language
| Python 3.10
|
|-
| Data Processing
| Apache Spark 3.4.1
|
|-
| Monitoring
| Prometheus & Grafana
|}

All AI models are containerized using Docker and orchestrated with Kubernetes for scalability and resilience. [[Kubernetes Deployment]] procedures should be followed carefully. We utilize [[Continuous Integration/Continuous Deployment]] (CI/CD) pipelines for automated model updates. Refer to [[Security Best Practices]] for securing the software stack. The [[Networking Configuration]] must allow for communication between components.

== Network Considerations ==

High bandwidth and low latency are essential for AI workloads. The servers are connected to the Aruba Networks core network via dual 100GbE uplinks. [[VLAN Segmentation]] is used to isolate AI traffic from other network traffic. Quality of Service (QoS) policies are implemented to prioritize AI-related data flows. The [[Firewall Configuration]] is configured to allow only necessary traffic to and from the AI servers. [[Load Balancing]] is employed to distribute traffic across multiple servers.

== Future Expansion ==

Planned future expansion includes:

* Adding more GPU servers to increase processing capacity.
* Implementing a dedicated NVMe-oF (NVMe over Fabrics) network for even lower latency storage access.
* Integrating with additional data sources.
* Exploring the use of more advanced AI frameworks like PyTorch.

Refer to [[Scalability Planning]] for long-term infrastructure growth.

[[Server Maintenance]] is critical to long-term stability.

[[Category:Server Hardware]]

== Intel-Based Server Configurations ==
{| class="wikitable"
! 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 ==
{| class="wikitable"
! 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 ==
[https://powervps.net/?from=32 Configure and order] your ideal server configuration

=== Need Assistance? ===
* Telegram: [https://t.me/powervps @powervps Servers at a discounted price]

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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Admin: Automated server configuration article