AI-Native Networking: Juniper's Innovations Revolutionize Data Infrastructure

AI-native networking: Juniper Networks' innovations - SiliconANGLE

What to expect at the Juniper 'AI-Native NOW': Join theCUBE August 20 Juniper Networks Inc. is leveraging artificial intelligence to transform network management and operations, an approach that it has described as AI-native networking. The company's networking strategy in this area was recently highlighted by the unveiling in July of its Ops4AI Lab, a multi-vendor lab for validating end-to-end automated AI data center solutions and operations. It was part of a series of AI-native networking initiatives implemented by Juniper over the past year. Juniper has been actively engaged in adding new enhancements to its AI-Native Networking Platform while extending its reach into the growing enterprise AI community. "With AI-native networking, Juniper introduced two concepts: AI for networking, networking for AI, delivering AI ops and AI data center networking to assure the best end-to-end operator and end-user experiences," said Praveen Jain, senior vice president and general manager of AI clusters and cloud-ready data center at Juniper, in an exclusive interview with SiliconANGLE. "We want to broaden that conversation, engaging with our ecosystem partners, industry leaders and customers to discuss [the] biggest challenges and opportunities organizations are facing with deploying their AI data centers." To continue that conversation, Juniper will hold a one-day "AI-Native NOW" event at the New York Stock Exchange on August 20, and theCUBE, SiliconANGLE Media's livestreaming studio, will be at the gathering to provide insights on how Juniper is transforming network management and operations through AI-native innovation. Research analysts from theCUBE will interview key executives and industry experts to hear the latest news on Wi-Fi, wired switching, AIOps, network access control, SD-WAN and location-based services. (* Disclosure below.) Juniper's concept of Ops4AI encompasses many of the key technologies that have powered its AI strategy. This includes leveraging Mist AI to automate client infrastructure and manage networks, and the deployment of its network assistant Marvis. The release of Marvis Minis this spring offered an AI-native digital experience tool that works with wireless and wired networks to diagnose problems. Mist has also been integrated with Unified Endpoint solutions to automatically check for corporate devices security compliance. "Juniper's approach is intriguing, focused on optimization, load balancing and visibility -- hallmarks of the company for the past quarter of a century," said Zeus Kerravala, principal analyst at ZK Research, a division of Kerravala Consulting, in a recent analysis for SiliconANGLE. Juniper's pursuit of networking for AI and AI for networking will be a central focus of the firm's event next week. The growth of AI deployment has transformed data center and cloud computing, a reality that is driving Juniper's interest in the market opportunity this presents. "The pace of advancement and growth in the critical ingredients of AI are simply breathtaking," said Juniper's Chief Executive Officer Rami Rahim, during an appearance on theCUBE. "We're seeing massive scale in AI models, in the data that's being used to train AI, and in the computing infrastructure that makes it all possible. AI has the potential of being as big, if not much bigger, than the internet. The market opportunity is immense." During the "Juniper Networks AI-Native NOW" event, theCUBE Research's Executive Analyst John Furrier will provide insights on the latest technology innovations driven by Mist AI, as well as how Juniper Networks is revolutionizing network management and operations with AI-native innovation. Expert guests include Juniper Networks' Manoj COO Leelanivas, CMO Jean English and SVP of Product Management Sudheer Matta.

How AI infrastructure is transforming networking and data flow in 2024 - SiliconANGLE

Transforming AI infrastructure: The role of networking in high-performance computing AI infrastructure is rapidly transforming industries, especially in networking and data transfer. Networking is an exciting and necessary part of building high-performance infrastructure for data transfer and machine learning training, with a shift toward on-premises infrastructure due to cost and data privacy concerns, according to Raj Yavatkar (pictured), chief technology officer of Juniper Networks Inc.. "It is an exciting place to be because everybody is building huge GPU clusters," he said. "All of those clusters need to be fed data from storage networks. Then once you have data in, you need to have data transferred between these GPUs. So, networking is needed no matter what -- very high performance, a high throughput, low latency kind of network. That's being built. More exciting part is that a lot of the infrastructure is not just being built in hyperscalers, it's also being built by enterprises on-prem, which is a big shift in the market." Yavatkar spoke with theCUBE Research's John Furrier at the AI Infrastructure Silicon Valley - Executive Series event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the integration of AI into networking, the shift toward AI-native infrastructure and the challenges of scaling networks to support large GPU clusters in enterprise environments. AI is being used to automate network configurations based on workload, with the Ultra Ethernet Consortium working to evolve the Ethernet standard to match training and inferencing requirements, according to Yavatkar. "What's happening with GPU-based clusters, these things are coming together. The standard Ethernet, we are being able to show it's not just good enough. It performs as well or better than InfiniBand-based networks," he said. "That is being recognized by industry now by creating this Ultra Ethernet Consortium, which is a consortium of all the vendors trying to evolve Ethernet standard to add some new capabilities to match the training workloads and inferencing workloads." Networking has evolved to intelligence-based, integrating telemetry and machine learning for application-aware assurance, automating root cause diagnostics and potentially remediation, Yavatkar added. "You start collecting telemetry from application, from compute, GPU, networking, operating systems, and you start correlating using machine learning model," he said. "If you do that, now you can start finding out where the problem is because you can find the anomalies, you find the switch buffers are running out of space, you find the packet loss has increased, latency has spiked. Based on that, you can point out where the problem is, and then you can do automated root cause diagnostics."