This project presents the design and implementation of an enterprise-grade Wide Area Network (WAN).
This network will undergo regular updates to accommodate continuous improvements and evolving requirements, ensuring its capabilities remain adaptable and limitless.
Detailed explanation follows below:
Figure 3.11: Main Topology
Figure 3.12: VLAN Segmentation - High Performance Computing VLANs
Figure 3.13: VLAN Segmentation - General Access VLANs
At the core of this network design is a robust, scalable, and resilient architecture designed to support high-performance AI workloads, ensuring that research, data processing, and operational tasks can continue seamlessly even during periods of failure or network disruption. Inspired by the best practices in AI infrastructure, this network is built to scale efficiently and provide the necessary reliability for mission-critical operations.
High Availability and Redundancy for Seamless AI Research
This network is structured to provide continuous connectivity between the branch office (NYC) and central office (SJ) through a redundant and highly available Wide Area Network (WAN). By utilizing NAT (Network Address Translation), uninterrupted connectivity is ensured even during network disruptions. The WAN is designed with multiple failover paths to guarantee that the infrastructure remains operational, supporting the large-scale computations required for AI model training and optimization.
At the heart of the architecture, there is a primary and secondary Layer 3 switch setup, ensuring that routing capabilities remain uninterrupted, while providing high availability using HSRP (Hot Standby Router Protocol) for automatic failover. To prevent loops and ensure fault-tolerant pathways within the Layer 2 domains, Spanning Tree Protocol (STP) is implemented across all switches. This design ensures that AI models can run continuously without network interruptions, even during failures or planned maintenance.
Optimized Network Segmentation for AI and Data Management
The network features dedicated Layer 2 switches for each major VLAN to optimize traffic flow and isolate critical workloads:
- Core VLAN (VLAN 10), handles essential network functions, ensuring robust data flow between other VLANs.
- Management VLAN (VLAN 20), is reserved for administrative tasks, securely managing network configuration and monitoring activities.
- Compute VLAN (VLAN 30), where AI traffic is prioritized, ensures that high-bandwidth, low-latency data traffic associated with AI model training receives optimal network resources.
Quality of Service (QoS) policies are implemented to prioritize AI workloads, optimizing throughput and minimizing delays for real-time data processing.
For data-intensive AI tasks such as training large language models and reinforcement learning, the Compute VLAN ensures that these workloads receive guaranteed bandwidth and low-latency routing, ensuring that computations are handled without delay, thus accelerating research and results.
In addition, the network incorporates a fourth Layer 2 switch supporting three critical VLANs related to research and development:
- R&D VLAN (VLAN 100), is dedicated to research and development traffic, providing the necessary bandwidth for data-driven research tasks.
- Employee (VLAN 200), supports internal employee network traffic, ensuring secure and reliable access to internal resources.
- Guest VLAN (VLAN 300), handles guest and external access, facilitating connectivity for wireless devices such as phones and laptops through access points.
This segmentation ensures the proper isolation of different traffic types, preventing congestion and enhancing security by keeping R&D, employee, and guest traffic separate.
Port security is applied where appropriate on access interfaces connecting end devices, limiting unauthorized connections and enhancing the overall security of the network. This helps ensure that only authorized devices, such as workstations or research servers, can communicate over these VLANs.
Comprehensive Automation for Seamless Management
Leveraging Ansible for automation, the network's configuration and provisioning processes are streamlined, reducing the potential for human error and enhancing scalability. Automation ensures that network devices are quickly and efficiently configured, updated, and monitored, providing the agility needed for rapidly evolving AI research and infrastructure demands.
From configuring Trunking between switches to implementing ACLs (Access Control Lists) and OSPF (Open Shortest Path First) for dynamic routing, Ansible allows for maintaining consistency and ease of management across the infrastructure. This automation also extends to DHCP (Dynamic Host Configuration Protocol) and NAT configuration on the router, enabling centralized management and rapid deployment of new devices or configurations as the network grows to accommodate new AI models and services.
Robust Security and Flexible Network Management
The use of STP, ACLs, VTP (VLAN Trunking Protocol), and SSH (Secure Shell) ensures that both security and management capabilities are tightly controlled. These technologies help secure internal traffic, prevent unauthorized access, and maintain strict controls on configuration management. By incorporating NAT and firewall protections, the security of critical AI model data is ensured while allowing for controlled external access where necessary.
Port security complements these measures by preventing unauthorized devices from connecting to access interfaces in critical areas, such as the Compute VLAN or Management VLAN, without needing configuration for every VLAN individually.
The dynamic routing protocols, including OSPF and RIP, ensure that routing information is automatically updated, supporting the redundancy and fault tolerance required for a globally distributed infrastructure. The network is resilient, capable of adjusting to topology changes and handling high-availability scenarios seamlessly, ensuring AI workloads continue to function without disruption.
Future-Proofed and Scalable Architecture
Designed with scalability in mind, this network is prepared for future growth, ensuring that as the demand for AI research and computational resources grows, the infrastructure can grow seamlessly. By designing the network with VLAN segmentation, distributed routing, loop-prevention mechanisms, and security policies like port security, the network can evolve to handle even larger datasets and AI models without compromising performance.
The infrastructure is adaptable to the rapidly changing landscape of AI, ensuring it is always ready to meet the demands of new projects, additional research teams, and larger-scale models.