In the race to incorporate AI into the business, leaders are focusing on cleaning up data, setting up new high-bandwidth cloud connections, beefing up security and boosting compute scalability. But what about the network? Is the business network ready for AI?
It is an important question since 92% of enterprises actively invest in AI to leverage value from their data.
Follow the Data
The entire AI journey relies on the lifecycle of data. It starts with the large datalakes, repositories of business data from which models will learn. These datalakes are often at the edges of the network, close to where business operations lay.
However, the compute for the AI models is built in the cloud. Few enterprises have the GPU farms AI requires. Instead, most enterprises use the massive GPU farms available in the major public clouds.
Once the models are built, they need to be moved throughout the enterprise – to the edge, data centers, branch offices and even in various clouds.
The challenge for the network is supporting the rapid movement and access to data AI requires. This means a network that has:
- High performance between every node
- SLA-grade reliability
- Low latency
- The ability to comply with data sovereignty laws from every corner of the globe
- Deployment agility
- Cost-effectiveness
Traditional Network Limitations
Enterprises find existing networking solutions fall short of these requirements:
- Traditional Private Networks – Private networks with dedicated physical assets are known for problems in lengthy deployment cycles and extremely high costs.
- Overlay Networks – Overlays run in part (or completely) on top of the public internet and have lower costs but struggle in three key areas:
- Delivering high-performance, SLA-grade reliability consistently.
- Latency issues from inefficient public internet traffic routing.
- Data sovereignty, because the enterprise cannot control where traffic gets routed, increasing the risk for the business to comply with data laws and regulations.
AI Requires a New Network
A network to support rapid movement and access to data AI requires high-performance, reliability and low latency with affordability and deployment agility.
The network must be private, as anything based on the public internet will fail to deliver performance, reliability, low latency and data sovereignty requirements. The network must also use a modern, disaggregate architecture to deliver affordability and deployment agility to meet the demands of the business.
Actionable Insights for AI-Ready Networking
To ensure the network can meet the demands of AI, start by assessing and modernizing the current infrastructure with these actionable steps:
- Adopt a Hybrid Network Architecture: Integrate a private network backbone with a disaggregated, modular architecture. This combines the high-performance and reliability of private networks with the flexibility and cost-effectiveness of modern technologies. By leveraging this hybrid model, you ensure high-speed data transfer, while maintaining agility and reducing overall costs.
- Leverage Network-as-a-Service (NaaS): Consider implementing NaaS solutions to optimize network management. NaaS allows enterprises to access advanced networking capabilities on demand. With NaaS, you can quickly scale resources, enhance network agility, and incorporate sophisticated features such as AI-driven traffic management and automated provisioning. This model aligns with the dynamic requirements of AI and provides cost savings through a pay-as-you-go structure.
- Implement Network Automation and Orchestration: Use network automation to quickly adapt to changes. Automation helps dynamically adjust resources based on AI workloads, which is crucial for handling the unpredictable demands of AI operations.
- Enhance Security and Compliance: Invest in advanced security solutions that offer real-time threat detection and compliance management. Ensure your network architecture meets global data sovereignty requirements with robust encryption and access controls in place. Regularly review and update security policies to address emerging threats and regulatory changes.
- Optimize for Low Latency: Focus on reducing latency by deploying edge computing solutions where AI processing can occur closer to data sources. This minimizes time required for data to travel, thereby improving response times and overall performance. Consider edge data centers and local caching strategies to further enhance speed.
- Monitor and Adjust Continuously: Regularly monitor network performance with AI-driven analytics tools. These tools provide insights into traffic patterns, potential bottlenecks, and areas for improvement. Use this data to make informed adjustments, ensuring your network remains agile and capable of handling evolving AI demands.
By integrating these strategies, you can build a network infrastructure that excels in the AI-driven future. Your network will be better equipped to handle increasing data volumes and complex operations, positioning your enterprise for growth and innovation.
