Wavefront by VMware Expands Kubernetes Monitoring with Root-Cause Analysis and Zero-Config Deployment

Originally reported by Container Journal. Announcement made at AWS re:Invent 2019.

Overview

At AWS re:Invent 2019, Wavefront by VMware announced a set of enhancements to its Kubernetes monitoring platform. The updates focus on three areas: predefined alert-driven root-cause analysis, zero-configuration deployment of monitoring agents, and an OpenShift 4 Operator available through OperatorHub. Together, these changes aim to lower the operational barrier for IT teams that need full-stack observability across Kubernetes clusters and the applications running on them.

Root-Cause Analysis via Predefined Kubernetes Alerts

One of the most time-consuming tasks in Kubernetes operations is tracing an incident back to its origin across a distributed, dynamic environment. Wavefront addressed this by introducing predefined alerts based on Kubernetes events. These alerts are surfaced through Wavefront Alert Viewer, a centralized landing page that exposes machine learning algorithms and advanced analytics to help teams detect and resolve incidents faster.

Alert Viewer automatically highlights point tags shared by affected time-series events. This makes it easier to correlate alerts generated by application dependencies across the full stack — from the Kubernetes cluster itself down to its underlying host infrastructure. Alerts can also be streamed in real time, enabling teams to trigger automated remediation scripts without manual intervention.

Specific conditions that IT teams can monitor out of the box include:

• Cluster utilization extremes
• SLA violations caused by increased latency or elevated error rates per service
• Excessive pod restarts
• Paused or stuck deployments

Zero-Configuration Agent Deployment

Deploying monitoring tooling into Kubernetes clusters has historically been nearly as complex as standing up the clusters themselves. Wavefront addresses this friction with a zero-configuration installation path for both its Kubernetes Collector and Java Tracing Agent.

The Wavefront Kubernetes Collector now automatically discovers and instruments Java-based services using the Wavefront Java Tracing Agent, which is built on the open-source Java Special Agent tracing project. This means teams no longer need to manually annotate or configure each service — the collector handles service discovery and instrumentation automatically on deployment.

According to Rob Markovich, Senior Director at Wavefront, reducing deployment complexity is critical to adoption. Monitoring tools that are difficult to configure rarely make it into production environments managed by IT operations teams, regardless of their technical merit.

Intelligent Trace Sampling

High-volume distributed tracing environments face a practical problem: retaining every trace is expensive, but discarding traces indiscriminately means losing diagnostic signal. Wavefront’s answer is Wavefront Intelligent Trace Sampling.

The system samples traces that are statistically likely to be the most diagnostically relevant while discarding redundant ones. Crucially, it derives and retains RED metrics (Rate, Errors, Duration) for all traces — including those not retained after sampling. This preserves 100% visibility into application trace metrics even when individual trace payloads are dropped, striking a balance between storage efficiency and observability completeness.

Certified OpenShift 4 Operator on OperatorHub

For organizations running Red Hat OpenShift, Wavefront announced that a certified OpenShift 4 Operator is now available on OperatorHub. Kubernetes Operators codify operational knowledge into software, automating the lifecycle management — installation, upgrading, and scaling — of applications running on Kubernetes. Distributing Wavefront as a certified Operator simplifies its deployment and ongoing management within OpenShift environments, aligning it with the Red Hat ecosystem’s standard tooling patterns.

Why This Matters for DevOps Teams

Observability is foundational to mature DevOps practice, and Kubernetes environments amplify that requirement because of their inherently dynamic, distributed nature. Pod scheduling, service mesh dependencies, autoscaling events, and rolling deployments all create transient state that traditional monitoring tools were not designed to handle.

Wavefront’s positioning targets a specific gap: providing a unified monitoring surface that IT administrators — not just platform engineers — can operate effectively, covering both microservices-based applications on Kubernetes and legacy monolithic workloads that may exist alongside them. By reducing deployment complexity and providing predefined, actionable alert definitions, Wavefront attempts to make Kubernetes observability accessible without requiring teams to build and maintain a bespoke monitoring stack.

The broader industry trend reinforces this: the more operational tooling that organizations can apply consistently across new and existing platforms, the lower the barrier to Kubernetes adoption at scale.

Key Announcements at a Glance