AI-Native Networking: Juniper Networks Leads the Charge in Network Automation and Optimization

AI-native networking: Juniper's path to autonomous networks - SiliconANGLE

Juniper Networks aims to simplify complexity with AI-native networking AI-native networking is revolutionizing how enterprises and service providers manage their infrastructure, enabling them to prioritize core business objectives by automating complex network operations. Integrating artificial intelligence into network management promises to streamline processes, enhance efficiency and minimize disruptions. For example, the advancements in AI-native networking underscore AI's potential to optimize network performance and resource utilization, according to Neil McRae (pictured), chief network strategist at Juniper Networks Inc. "What [network performance optimization] allows our customers to do is to focus on their business, not focus on running a network, not focus on dealing with network issues. Marvis, our AI agent, takes care of all of the network[s] for them," McRae said. McRae spoke with theCUBE's Dave Vellante and Savannah Peterson at MWC25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed AI-driven network automation, operational efficiency and the future of network modernization. (* Disclosure below.) AI-native networking represents a significant leap forward in network technology, enabling networks to dynamically adapt and optimize traffic flow, according to McRae. Networks are increasingly complex, and AI offers the capability to analyze vast amounts of data to determine the most efficient and effective routing paths. "The data model is all the routing information. It's already in the network today, but typically, it's been hidden away from service providers," McRae said. "We're exposing that data and allowing the routing protocols to take AI to either the best path on an experience point of view or taking the best path that's actually the most energy efficient. Trying to do that manually when you have a network of thousands of developers in it is impossible; this is where the AI really brings in its capability and allows us to benefit the service providers." Automating network housekeeping tasks is another critical benefit of AI-native networking. This automation ensures that routine maintenance and configurations are consistently performed, reducing the likelihood of human error, according to McRae. "Most network issues actually happen when operators forget to do something, and we're human; we forget stuff," he said. "The AI takes that away from you, and it automates the initial configuration, the setup, the policies -- everything that a service provider needs to run the network well and also do that on a continuous and sustainable basis." The application of AI extends beyond routing to encompass a wide range of operational tasks and contributes to broader network modernization efforts. This is extremely valuable in software-defined wide-area network environments, where AI allows for rapid identification and resolution of network issues, minimizing downtime and business disruptions, according to McRae. "One of the biggest benefits I hear from customers is, 'We're able to pinpoint the issue within seconds,'" he said. "Historically, that could take hours. You might have to coordinate with multiple providers or multiple service agents, and you've got multiple stakeholders. They just want to know what is it and when's it going to be fixed. Using Marvis and Paragon, our AI tools zero in on that [and] takes so much pain out of service providers' day-to-day operations, but also allows them to reduce costs on things like service-level payments where you've had an outage." Modernizing networks to meet the demands of today's digital landscape involves a multifaceted approach, encompassing not only AI-driven operational efficiency, but also a focus on monetization, security, sustainability and automation. Service providers are increasingly adopting network-as-a-service models, enabling them to build on-demand networks for their customers, according to McRae. "You can't do any of those pillars without that," he said, adding that AI is crucial in achieving these goals, particularly in enhancing sustainability. "With our WAN-enabled AI and ... some of the other tools that we've got, not only can we reduce the energy, but we can actually display it." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of MWC25:

AI-driven network automation is transforming enterprise IT - SiliconANGLE

AI-driven networking takes center stage as data demands soar AI-driven network automation is reshaping the way enterprises scale their infrastructure to meet the demands of artificial intelligence. As AI workloads surge, networks must evolve with smarter, autonomous solutions to handle rising data demands. Technologies such as liquid cooling, co-packaged optics and high-performance fabrics are now essential for AI-driven data centers. To stay competitive, organizations must enhance efficiency, scalability and resilience by adopting AI for networking to streamline operations and networks for AI to support massive GPU clusters, according to Rami Rahim (pictured), chief executive officer of Juniper Networks Inc. "I think there are three really critical ingredients of an AI native network," Rahim said. "The first is you must have access to the right data. Second, it's about having a proven cloud that can scale from the smallest to the largest of customers as we have done with incredible wins around the world." Rahim spoke with theCUBE's Dave Vellante and Bob Laliberte at MWC25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how AI-driven network automation is transforming enterprise infrastructure, optimizing network operations and enabling scalable, high-performance connectivity to support the rapid growth of AI workloads. (* Disclosure below.) The integration of AI in networking is fundamentally reshaping how organizations manage their infrastructure. Traditional network operations have long been burdened by complex troubleshooting and maintenance requirements. However, AI-driven solutions are now making network management more autonomous, allowing IT teams to focus on strategic initiatives rather than daily firefighting. "Starting with AI for networks, any CTO of a big company, CIO, is going to be typically struggling just keeping up with maintaining the network to provide a great experience for their end users. Keeping the lights on typically is an arduous task in and of itself," Rahim said. "Providing artificial intelligence and moving much of that operation to robots, basically software that's doing that work that would otherwise have to be done by humans is what this opportunity is all about." As AI continues to permeate network operations, enterprises are seeing tangible benefits such as reduced downtime, lower operational costs and improved end-user experiences. Companies such as Juniper are leading the way with AI-powered platforms that provide real-time insights, automate troubleshooting and enhance overall network performance, according to Rahim. "Our solution, which is driven by Mist AI, is truly unique in the industry today in giving operators that freedom to focus on much more consequential, important things like advancing their strategies, keeping the disruptors out, generating new revenue streams," he explained. AI applications require more than just traditional data center networking. The computational power necessary for large-scale AI models, such as generative AI and large language models, demands ultra-fast and high-capacity networks. Enterprises and cloud providers alike are investing heavily in building a strong networking infrastructure that can efficiently connect thousands -- or even millions -- of GPUs. "The pace of investment, tens of billions, hundreds of billions of dollars going into learning. In time, all of that learning has to translate to inference and value generation, that's necessarily going to happen closer to where the data is at the edge or even in the customer premises," Rahim noted. Networking for AI also requires innovations in power efficiency and congestion management. Companies are increasingly turning to purpose-built silicon and software-driven optimizations to ensure seamless AI model training and inference. The ability to detect and mitigate network congestion in real time is now a competitive differentiator in the industry, Rahim said. "We have built into our automation capabilities for the data center, the ability to detect congestion and to proactively alleviate it before it starts to reduce the utilization of those precious GPU resources. That has resulted in some of the wins that we're achieving now in that space," he added. Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of MWC25: