Virtualization Technology News and Information
Article
RSS
Why you need Metering for Kubernetes infrastructure

An increasing number of organizations are adopting Kubernetes (K8s) as their go-to container orchestration platform. This powerful platform is used for automating deployment, scaling, and management of containerized applications. According to CNCF's 2020 survey, Kubernetes adoption grew to 83% in 2020, up from 78% in 2019, and has continued to rise in the years since.

However, as powerful as Kubernetes is, managing complex clusters and container workloads constantly scaling up and down, potentially across multiple locations, is a challenging task. Modern businesses require on-demand visibility to system usage by user or application and health down to the container-level. This is where metering comes in.

Metering is the practice of defining and collecting real-time metrics on the usage of resources within a Kubernetes cluster. Metering differs from traditional observability solutions by providing access to real-time usage data filtered by individual user, application or resource, with additional design principles to ensure accuracy and completeness. Unlike monitoring or other observability, metering data is consumed by all departments and organizations across a business to inform decision-making and provide real-time visibility to system usage, adoption, and health.

Reason #1: Kubernetes Monitoring: Complex and Costly to Build and Maintain

Industry standard solutions for Kubernetes monitoring and visibility require complex infrastructure builds that integrate multiple tools together or purchasing costly third-party software solutions. Either approach is both resource- and time-consuming, requiring specialized knowledge and continuous maintenance.

Typically, a Kubernetes monitoring infrastructure configuration consists of multiple technologies:

  1. Define and collect metrics using a tool like Prometheus or Heapster
  2. Store collected metrics in a time-series database like InfluxDB
  3. Aggregate and visualize the data with a tool like Grafana

An engineering team is then required to integrate the chosen technologies and services to create a bespoke data pipeline for Kubernetes monitoring. Changes or additions to the metrics being collected, or the visualizations being shown requires engineering resources to scope and implement the changes. Some organizations can afford this overhead, but for most the most feasible and cost-effective solution is to leverage a metering platform solution.

A metering platform streamlines this workflow into a single tool, eliminating the need for patching multiple disparate tools together and reducing engineering overhead. Metering, by design, includes event definition and ingestion, persistence in a time-series database, aggregation according to the selected aggregation functions (sum, max, average, etc.) and time intervals, and visualization in the form of charts, tables, dashboards, and reports.

Reason #2: Access to Real-time Usage Data and Custom Metrics

Metering provides access to real-time usage data that is crucial for understanding and making informed decisions regarding Kubernetes usage within your system. A metering platform provides a single pane of glass to view all Kubernetes cluster usage and performance metrics in one place, creating a single-source-of-truth for system usage and consumption data.

Traditional monitoring and observability solutions are developer-centric and not user-friendly for a non-technical audience. This limits the reach and viability of the tool (and its associated data) across the wider business.

With metering, you can define any metric to track along with the ingestion format and frequency. It also allows you to add custom metadata to each event record for advanced analytics and filtering, such as user or customer name, resource, system, or application name and ID, and more. This means functional teams from IT to product to marketing can define relevant metrics and event data structures for their respective functions, all using the same tool and underlying usage data.

As businesses shift more towards data-driven, product-led strategies, having access to this timely usage data will only become more critical.

Reason #3: Metering is fundamental to ephemeral cloud infrastructure. It also enables Usage-Based Pricing and Billing

Docker clusters running on Kubernetes are ephemeral by nature, scaling in and out of existence on-demand as the workloads require. It is incumbent on any business operating such a complex and ever-changing infrastructure to track this variable usage in order to understand costs and build a transparent customer-facing billing model. With elastic back-end resources and variable usage, the pricing should scale and accommodate this variability, in other words, it should also be elastic.

Usage-based pricing is increasingly becoming the preferred business model  with over half of SaaS companies offering some form of usage-based pricing by the end of 2023 (OpenView Partners). Adopting a usage-based pricing model based on metered usage aligns well with  customers where they pay only for what is used. It has shown to drive organic revenue growth and product adoption.

Reason #4: Actionable Insights with Proactive Alerting

In any production system there will be errors, outages, and bugs. The key is having real-time visibility to system statuses and health, in order to react proactively and solve issues before they reach a critical level. Alerting is built into a metering platform as a core capability. Users must be able to easily define alerts as-needed and ensure they are delivered to the proper stakeholders for prompt resolution.

Using a metering platform streamlines operations and removes integration points (or failure points) from the workflow. Having alerting built-in to the platform simplifies alert creation and management, and eliminates the need for engineering resources to create, deploy, and manage a disparate alerting system across multiple tools . Real-time data and granular visibility into cluster usage and health allows for proactive alerting and faster issue resolution compared to the reactive approach of only addressing system health after being notified of an outage. Overall a metering-centric approach allows for higher net uptime and a more proactive operating model.

Conclusion

Implementing metering for Kubernetes offers tangible advantages that can increase operational efficiency and customer satisfaction.It provides real-time visibility into system health, and enables proactive management of issues-leading to higher uptime. By leveraging the granular data and alerting capabilities of a metering platform, organizations can effectively balance customer usage, the corresponding costs, and robust operational insights creating a more proactive and efficient operating model for Kubernetes based infrastructure and applications.

++

Join us at KubeCon + CloudNativeCon North America this November 6 - 9 in Chicago for more on Kubernetes and the cloud native ecosystem. 

##

ABOUT THE AUTHOR

Puneet Gupta, CEO and Co-Founder, Amberflo

Puneet Gupta 

Puneet Gupta is the CEO and Co-Founder of Amberflo.io and former General Manager at AWS.

Published Wednesday, October 11, 2023 7:42 AM by David Marshall
Comments
There are no comments for this post.
To post a comment, you must be a registered user. Registration is free and easy! Sign up now!
Calendar
<October 2023>
SuMoTuWeThFrSa
24252627282930
1234567
891011121314
15161718192021
22232425262728
2930311234