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:
- Define and collect metrics using a tool like Prometheus or Heapster
- Store
collected metrics in a time-series database like InfluxDB
- 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.
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Join us at KubeCon + CloudNativeCon North America this
November 6 - 9 in Chicago for more on Kubernetes and the cloud native
ecosystem.
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ABOUT THE AUTHOR
Puneet
Gupta, CEO and Co-Founder, Amberflo
Puneet
Gupta is the CEO and Co-Founder of Amberflo.io and former General Manager at
AWS.