Maximize AI Cluster Performance Using Juniper Self-Optimizing Ethernet

Learn how to maximize AI cluster performance through Juniper's self-optimizing Ethernet fabric in this technical presentation. Explore how Juniper's advanced load balancing innovations dynamically adapt to congestion, keeping AI clusters running at peak efficiency. Vikram Singh, Sr. Product Manager for AI Data Center Solutions, addresses the unique challenges of AI/ML traffic, including UDP-based low entropy flows, bursty traffic patterns, and the synchronous compute requirements of data parallelism. Understand why traditional Ethernet struggles with AI workloads and how Juniper's open, standards-based approach offers solutions through AI load balancing technologies: Dynamic Load Balancing (DLB), Global Load Balancing (GLB), and RDMA-aware Load Balancing (RLB). Discover how these technologies work at microsecond granularity to make informed forwarding decisions, exchange link quality data between network components, and ensure consistent high performance without expensive proprietary hardware.