Verizon extends observability to the network edge with Pixie
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Verizon 5G Edge is a mobile-edge computing platform that helps developers and businesses build applications with ultra-high reliability and low latency. Behind the scenes, Verizon 5G Edge supports Kubernetes clusters for developers to use in the 19 Amazon Web Services (AWS) Wavelength Zones nationwide. The more clusters and zones, the more pathways phones and other devices have to achieve optimal performance.
Challenge: Innovating and improving the developer experience on Verizon 5G Edge.
Developers could not debug their workloads deployed on Verizon 5G Edge nodes. Edge computing environments are distinct enough to create architectural constraints for traditional application performance monitoring (APM) and Kubernetes monitoring. Specifically, edge computing nodes:
- Are resource-constrained with limited memory and network capacity
- Require a three-level architecture with edge agents connecting to centralized cluster agents that connect back to a cloud monitoring service. Traditional APM tools have a two-level architecture with cluster agents connecting back to the cloud monitoring service.
Without enough visibility, the developer experience on the Verizon 5G Edge proved prohibitively difficult. This was a serious problem that made the value of investing in the edge unclear.
Solution: A new solution at the network edge using auto-telemetry with Pixie.
Verizon chose New Relic auto-telemetry with Pixie because of how it addresses the shortcomings of traditional APM and Kubernetes monitoring solutions:
- Pixie PEM (agents) are lightweight
- Pixie PEM (agents) can be installed in a three-level architecture where agents on edge nodes relay data to central Kubernetes clusters, which send data back to the New Relic cloud monitoring service
As a result, developers on Verizon 5G Edge get instant observability for their Kubernetes workloads and can drive greater adoption by businesses looking to build applications on new platforms.
Out-of-the-box instrumentation: On the path toward full automation.
The Pixie integration with New Relic provides automatic observability of Kubernetes applications’ performance. Pixie, an open-source tool, has earned its popularity for good reason. As noted in its documentation, there’s no need for manual instrumentation; instead, Pixie uses extended Berkeley Packet Filter (eBPF) to capture telemetry data automatically. Developers can use Pixie to view the high-level state of their clusters and also drill down to more detailed views.
“Imagine a scenario where you notice that gigabytes on gigabytes of data are being exchanged from the edge back to the parent region. You could actually use these insights to re-architect your edge application and introduce efficiencies in your end-to-end workflows,” says Robert Belson, Developer Relations Lead, Corporate Strategy at Verizon. “In this way, Pixie on Verizon 5G Edge can be used not only as an observability tool, but also as an architectural asset.”
In addition, Verizon and New Relic collaborated on a Terraform module where developers simply needed to provide their Pixie deploy key, Pixie API key, and Elastic Kubernetes Service (EKS) cluster name. Everything else was built-in. With the EKS cluster, developers could select which AWS Wavelength Zones they wanted their node groups to live in. From there, they could move quickly to auto-instrumentation.
Additionally, the integration with Pixie has made network flow monitoring with the Pixie query language (PxL scripts) simpler. The flexibility of PxL scripts makes it easier for Verizon developers to create, customize, and adopt new views.
Performance monitoring is optimized with New Relic.
By using the Pixie integration with New Relic on Verizon 5G Edge, developers can leverage a single dashboard across edge and non-edge workloads, leading to a more holistic visualization of performance and availability. Additionally, the Verizon team has been working to make the Pixie integration deployment even easier and more automated. Developers can automate the agent install, alerts setup, and dashboard configuration—all with a single line of code.
Looking forward, New Relic performance monitoring capabilities can be integrated deeply with the Verizon 5G Edge Discovery Service (EDS), an API that determines the optimal edge workload for a given mobile client.
“This is how we start to think about network intelligence in the context of Verizon 5G Edge,” says Robert. “It's no longer just about application metrics, CPU, or memory use. It's about how network intelligence can deliver unprecedented workload orchestration capabilities.”
Impact: Making it easier for developers to experiment on the edge.
Today’s customers want immersive, hassle-free mobile experiences. With the New Relic observability platform and the integration with Pixie, Verizon can deliver on this demand more easily. Auto-instrumentation in the Pixie integration speeds the implementation of Verizon solutions, while New Relic alerts provide enhanced observability into Verizon software.
Essentially, the combined capabilities of Verizon and New Relic are a guide for building best practices in deploying APM to the edge. They create a reference architecture that guides future developers to the right metrics and the best ways of configuring dashboards and alerts. Together, Verizon and the Pixie integration with New Relic transform how people, businesses, and things connect with each other—and how developers innovate on the edge.
- Headquarters: New York, New York
- Fortune rank: 20
- Retail locations: Nearly 1,500
- Fortune 500 customers served: 99%
- Countries served: 150+
- Environment: AWS
- Team overview: Developer relations for Verizon 5G Edge
- Service used in automation template: Pixie integration with New Relic
- In a multi-edge deployment, determine which AWS Wavelength Zone is the most optimal at all times.
- Prevent degradation in performance and poor device and user experiences that the Verizon API, EDS, might not surface.
- Leverage telemetry data gathered from Pixie on Kubernetes and application services infrastructure as a part of the routing algorithm to determine the optimal network path of a multi-access edge computing (MEC) device.