Kentik Blog: Data Center Networking
VXLAN overlays bring flexibility to modern data centers, but they also hide what operators most need to see: true host-to-host and service-to-service traffic. Kentik restores that visibility by decoding VXLAN from sFlow, exposing both overlay endpoints and underlay paths in a single view without the cost and complexity of pervasive packet capture — the result: faster troubleshooting, smarter capacity planning, and confident operations at scale.
In large-scale AI model training, network performance is no longer a supporting actor — it’s center stage. Job Completion Time (JCT), the key metric for measuring training efficiency, is heavily influenced by the network interconnecting thousands of GPUs. In this post, learn why JCT matters, how microbursts and GPU synchronization delays inflate it, and how platforms like Kentik give network engineers the visibility and intelligence they need to keep training jobs on schedule.
Kentik transforms real-time network telemetry into actionable alerts for AI-optimized data centers. By converting database queries into custom alerts, engineers can detect issues like elephant flows, idle links, and packet loss before performance suffers and triggers alerts in systems like ServiceNow or PagerDuty.
Elephant flows are no longer rare. They’re foundational to AI workloads. In today’s GPU-heavy data centers, long-lived, high-volume flows can distort ECMP, overflow buffers, and rack up unexpected cloud bills. Kentik helps you see and tame these elephants with real-time flow analytics, automated alerting, and predictive capacity planning.
In leaf-spine data center networks, traffic often becomes imbalanced, leaving some uplinks idle and resulting in wasted bandwidth. Kentik helps engineers identify underutilized paths, diagnose the causes, and take corrective action using enriched telemetry, visual topology maps, and intelligent alerts, turning hidden inefficiencies into actionable insights.
Traditional data center networking can’t meet the needs of today’s AI workload communication. We need a different networking paradigm to meet these new challenges. In this blog post, learn about the technical changes happening in data center networking from the silicon to the hardware to the cables in between.