How AI is rewriting the rules of enterprise edge computing

The rapid adoption of artificial intelligence applications in enterprise environments is fundamentally changing the requirements for edge network infrastructure, particularly in how organizations handle data traffic and network resources.

The AI networking challenge: Enterprise networks are facing unprecedented demands from AI applications that introduce radically different traffic patterns and resource requirements compared to traditional web applications.

• AI workloads generate bursty, unpredictable traffic that requires symmetrical upload and download capabilities
• Traditional edge networks, designed primarily for downstream-heavy web traffic, struggle to handle AI’s unique demands
• Applications like generative AI and video inferencing require significantly higher bandwidth and lower latency than conventional enterprise applications

Technical requirements: The shift to AI-driven operations demands specific networking capabilities that many current edge infrastructures cannot support.

• Traffic ratios are moving from the traditional 85:1 download-to-upload ratio toward 50:50 or even reversed ratios for some AI applications
• Real-time AI applications require ultra-low latency for optimal performance
• Encrypted AI traffic flows make traditional network optimization techniques less effective
• Peer-to-peer architectures of AI applications create complex traffic patterns between multiple network endpoints

VeloRAIN solution: Broadcom’s VeloCloud has developed VeloRAIN (Robust AI Networking) to address these emerging challenges.

• The platform builds on VeloCloud’s SD-WAN technology to optimize AI workload performance across distributed networks
• Machine learning capabilities help identify and prioritize encrypted AI traffic
• Dynamic application-based slicing enables customized quality of experience for different applications and users
• AI-powered operations automation adjusts network policies in real-time based on performance data

Service provider opportunities: Communication service providers (CSPs) are positioned to play a crucial role in this network transformation.

• Enterprises need increased bandwidth and expert guidance to navigate AI implementation
• CSPs can provide consultative services to help organizations select appropriate connectivity solutions
• The shift creates opportunities for higher-margin services like managed security and SD-WAN

Looking ahead to implementation challenges: The ongoing evolution of AI applications will continue to reshape network requirements and create new demands.

• Organizations must carefully evaluate their existing infrastructure against future AI needs
• Network security remains a critical concern as AI applications proliferate
• Success will require close collaboration between enterprises, technology providers, and CSPs to ensure optimal network performance