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WHAT IS KUBERNETES? HOW K8S MANAGES CONTAINERIZED APPS

Steve Caron Infrastructure August 31, 2021

In this blog post

 * What is Kubernetes?
 * Orchestrating the world: from pipe dream to mainstream
 * What are containers, and why are they so hot?
 * Kubernetes: Container orchestration for the cloud-native era
 * Kubernetes forged by the rise of Google
 * Kubernetes design principles
 * Kubernetes architecture: a primer
 * What is Kubernetes used for? Kubernetes use cases
 * How does Kubernetes enable DevOps?
 * The challenges of Kubernetes at scale: The service mesh question>
 * Observability challenges with Kubernetes
 * Monitoring the full Kubernetes stack
 * Further your Kubernetes knowledge


SHARE BLOG POST



Kubernetes is a popular solution for scaling, managing, and automating the
deployments of containerized applications in distributed environments. But these
highly dynamic and distributed environments require a new approach to
monitoring.

More applications now rely on containers and microservices than ever before.
According to the 2020 Cloud Native Computing Foundation (CNCF) survey, 92
percent of organizations are using containers in production, and 83 percent of
these use Kubernetes as their preferred container management solution. With apps
growing larger and more complex by the day, IT teams will require tools to help
manage these deployments.

Since Kubernetes emerged in 2014, it has become a popular solution for scaling,
managing, and automating the deployments of containerized applications in
distributed environments. There’s no doubt it will be the orchestration platform
of choice for many enterprises as they grow their apps over the coming years.
Although Kubernetes simplifies application development while increasing resource
utilization, it is a complex system that presents its own challenges. In
particular, achieving observability across all containers controlled by
Kubernetes can be laborious for even the most experienced DevOps teams.

But what is Kubernetes exactly? Where does it come from? What problem is it
trying to solve, and how does it work? What challenges does it present, and how
can you overcome them?


WHAT IS KUBERNETES?

Kubernetes (aka K8s) is an open-source platform used to run and manage
containerized applications and services on clusters of physical or virtual
machines across on-premises, public, private, and hybrid clouds. It automates
complex tasks during the container’s life cycle, such as provisioning,
deployment, networking, scaling, load balancing, and more. This simplifies
orchestration in cloud-native environments.

However, these highly dynamic and distributed environments require a new
approach to monitoring Kubernetes infrastructure and applications.






ORCHESTRATING THE WORLD: FROM PIPE DREAM TO MAINSTREAM

When I first started working at Dynatrace in 2011, our customers were using the
Dynatrace solution to get deep end-to-end visibility into environments we now
refer to as monolithic. The bold organizations were building distributed
environments using service-oriented architecture (SOA) and trying to implement
enterprise service busses (ESBs) to facilitate application-to-application
communication. Although it all looked good on paper, it ended up being difficult
to implement.

But a perfect storm was brewing on the horizon. Three revolutions were just
beginning and have been feeding on each other since, as commented by John
Arundel and Justin Domingus in their 2019 book Cloud Native DevOps with
Kubernetes:

 * Cloud computing: A revolution in the automation of
   infrastructure-as-a-service (IaaS) in an on-demand, pay-as-you-use model
 * DevOps and continuous delivery: A revolution in processes, and the way people
   and software delivery teams work
 * Containers and microservices: A revolution in the architecture of distributed
   systems

Cloud-native refers to cloud-based, containerized, distributed systems, made up
of cooperating microservices, dynamically managed by automated
infrastructure-as-code.
The change was happening, and it was happening fast; more organizations were
adopting containerized deployment methods (such as Docker) and DevOps practices
and CI/CD pipelines to confidently deliver business-differentiating features
quickly in an increasingly competitive market. At its start in 2013, Docker was
mainly used by developers as a sandbox for testing purposes. The challenge at
the time was to manage containers at scale in real-world production
environments.


WHAT ARE CONTAINERS, AND WHY ARE THEY SO HOT?

A container is a unit of software that packages application code and its
dependencies together, creating a small, self-contained, and fully functional
environment to run a workload (app, service), isolated from the other
applications running on the same machine. These packages, known as container
images, are immutable, and they are abstracted from the environment on which
they run. Their immutability and abstraction make them portable across
environments, whether it’s a physical or virtual machine, on-premises, in a data
center, or in the public cloud, regardless of the underlying platform or OS.
This distributed approach to developing and running apps and services is also
known as microservice architecture.

Container runtime engines, such as Docker’s runC, leverage OS-level
virtualization capabilities offered from the kernel to create isolated spaces
called “containers.” This virtualization makes it possible to efficiently deploy
and securely run a container independently of the hosting infrastructure.
Because the concern of environmental conflicts is removed, you can run multiple
containers on the same node and achieve higher resource utilization, which can
reduce infrastructure costs.

But on their own, containers are not sufficient.

What’s missing here? Well, many things can happen with containers.

As containers are the vehicle of choice for microservices, you wouldn’t expect
to run a full-fledged enterprise application in a single container; instead, you
would have multiple containers running on different machines to make up a
distributed system.

But how will you set up the communication? Who manages the networking aspects?
How do you make this system resilient and fault-tolerant? How do you make it
scalable?

Containers cannot be used at their full potential on their own. Enter the
orchestration platform.




KUBERNETES: CONTAINER ORCHESTRATION FOR THE CLOUD-NATIVE ERA

Think of Kubernetes as a classical orchestra. Replace the composer with a
software architect, the conductor with a container platform, the score with a
workload, the musicians with containers, the hand gestures with API-based
messages, performance with current system state, and vision with desired system
state.

Just like a classical orchestra is a framework for integrating and coordinating
all the elements of a beautiful music performance, Kubernetes is a framework for
integrating and coordinating all the elements for running dynamic
microservice-based applications. Without orchestration, running these
applications in production would be impossible.


KUBERNETES FORGED BY THE RISE OF GOOGLE

If there was any company positioned to understand the problems and limitations
of containers before anyone else, it was Google.

Google has been running production workloads in containers longer than any other
organization. To operate their infrastructure at high utilization, Google moved
their most intensive services into containers. To overcome the challenges of
efficiently managing such deployments at a massive scale, they invented a
platform to enable container orchestration, known as Borg, which had been
Google’s secret weapon for a decade until 2014 when it announced Kubernetes, an
open-source project based on the experience and lessons learned from Borg and
its successor Omega.

Since then, Kubernetes has taken the container world by storm, becoming the de
facto standard for container orchestration, leaving Docker Swarm and Apache
Mesos far behind. Google eventually donated the project to the CNCF, while
remaining its largest contributor, although companies such as Microsoft, Intel,
and Red Hat also contribute and develop their own Kubernetes distributions.


KUBERNETES DESIGN PRINCIPLES

To understand how Kubernetes works and how to best use it, it’s good to
understand the motivations behind its design.


DECLARATIVE

Kubernetes manages its resources in a declarative way, which means you specify
the desired state and Kubernetes will continuously reconcile the actual state
with the desired state. This frees you from having to tell it what to do or how
to do it (the imperative way), so you can spend your time doing other things.


EPHEMERAL

Kubernetes is designed to deal with failures, which can and will happen: servers
can go down, processes run out of memory and crash, network becomes unreliable,
and so on. So instead of assuming the platform will ensure the application
resources are always up and running, architects should design them to be fault
tolerant and make containers disposable and replaceable.


IMMUTABLE

Immutable means unchangeable. In the Kubernetes context, that means if you need
to make a change to a container workload, you create a new version (image) of
it. Deployments are then executed by provisioning based on validated
version-controlled images, so they are more consistent and reliable.


DISTRIBUTED

Kubernetes architecture is distributed, which means each platform component has
a well-defined role and clear mechanism of communication (via API). It is can
run on multiple machines, which makes it more resilient and fault-tolerant.


AUTOSCALABLE

To adapt quickly in dynamic, cloud-native environments, Kubernetes provides
resource autoscaling to respond to changes in demand. Horizontal Pod Autoscaler
(HPA) adjusts the number of instances or replicas based on observed metrics.
Vertical pod autoscaling, an add-on, adjusts the resource requests and limits
pod CPU or memory usage as needed.

For clusters that run on a public cloud, cluster autoscaling adjusts the number
of nodes in the cluster to help control the cost.


PORTABLE

Kubernetes can run anywhere: in public or private clouds, on-premises, on
virtual machines, bare-metal servers, or even mainframes, and is portable across
OS distributions.

Immutable infrastructure allows to move your workloads without having to
redesign your applications, thus avoiding vendor lock-in.


SELF-HEALING

Because of the ephemeral nature of its containerized workload, Kubernetes
provides control mechanisms to repair applications – or even the platform itself
– in case of failures. It implements multiple control loops, continuously
monitoring the components running on the platform and acting if something is
wrong or does not correspond to the desired state. If a container fails,
Kubernetes will restart it. If a pod encapsulating a container has a problem,
Kubernetes will kill it and spin up a new one. If a node becomes unhealthy,
Kubernetes will reschedule the workload to run on a healthy node; if a healthy
node is not available, Kubernetes can spin up a new machine using cluster
autoscaling.


RESOURCE OPTIMIZATION

Because Kubernetes decouples the application workload from the infrastructure,
it can choose the most appropriate server to run your application based on the
resource requirements defined in your object manifest file. Its immutable
infrastructure enables Kubernetes to move those around freely on the platform
infrastructure, making sure resources are utilized as efficiently as possible
and achieve much better results than with manual human intervention.


KUBERNETES ARCHITECTURE: A PRIMER

Kubernetes provides a framework to orchestrate containers, for example, to run
them securely, create cross-node virtual networks, recreate a container if one
fails, manage scaling and load balancing, execute rollouts and rollbacks, and
manage secrets, including OAuth tokens, passwords, and SSH keys.

A Kubernetes environment is called a cluster. A Kubernetes cluster is made up of
node components, which manage individual containers and their workloads, and
control plane components, which manage global functions. A cluster can host
multiple nodes.

Image Source


NODE COMPONENTS:

 * Image: A container image is a file that encapsulates the application,
   including its dependencies and configurations
 * Node: A virtual or physical worker machine with services to run a pod
 * Pods: A group of containers that run the application workload deployed to a
   single node
 * Kubelet: An agent running on each node responsible for communication between
   the cluster and nodes

With Kubernetes, pods — groups of application containers that share an operating
system — run across clusters of services, called nodes, independently of
compatibility or location.


CONTROL PLANE COMPONENTS:

 * Kube-scheduler: The default scheduler that selects an optimal node for every
   pod
 * Kubernetes API: The flexible REST API that manages all interactions with
   Kubernetes
 * Kube controller manager: The component that handles all control processes
 * Cloud controller manager: The interface with a cloud provider’s API
 * Etcd: A fault-tolerant distributed key-value data store that keeps the
   cluster configuration

The Kube-scheduler schedules the pods, allocating available resources based on
the CPU and memory requirements of each node. Web server instances are
automatically scaled up or degraded based on demand for the software
application, which can be millions of users simultaneously.


WHAT IS KUBERNETES USED FOR? KUBERNETES USE CASES

The primary advantage of using containers over virtual machines (VMs) for
microservice architecture is their small size and performance. They can be spun
up and down a lot faster, and have instant access to system resources. This
frees up processing power and makes them more portable. Other benefits include
shortened software CI/CD cycles, efficient resource utilization, high
availability, seamless performance regardless of computing environment, and
system self-healing by automatically restarting or replicating containers.

Kubernetes is useful if your organization is experiencing any of the following
pain points :

 * Slow, siloed development hindering release schedules
 * Inability to achieve the scalability required to meet growing customer demand
 * Lack of in-house talent specializing in the management of containerized
   applications
 * High costs when optimizing existing infrastructure resources

Kubernetes helps overcome these scaling limitations, coding shortfalls, and
development delays. Managed service providers supply the infrastructure and
technical expertise to run Kubernetes for your organization. Examples include:

 * Azure Kubernetes Service (AKS)
 * Amazon Elastic Kubernetes Service (EKS)
 * IBM Cloud Kubernetes Service
 * Red Hat OpenShift
 * Google Cloud Kubernetes Engine (GKE)

Managed service providers make the benefits of the Kubernetes platform
accessible for all shapes and sizes of enterprises struggling to meet a variety
of business objectives.

Kubernetes enterprise distributions give organizations the option to host their
own Kubernetes infrastructure. Examples include:

 * Red Hat OpenShift Container Platform
 * Rancher Kubernetes Engine
 * Mirantis Docker Kubernetes Service (formerly Docker EE)
 * VMWare Tanzu Kubernetes Grid (formerly Pivotal Container Service–PKS)
 * D2iQ Konvoy

Despite the flexibility and portability of containers, it’s important to know
that splitting up monolithic applications into small, loosely coupled
microservices that span multiple containers and environments makes it a
challenge for DevOps teams to maintain visibility into the apps and where they
run.


HOW DOES KUBERNETES ENABLE DEVOPS?

DevOps teams use agile processes to quickly and efficiently deliver new
applications and features, and they typically rely on microservice architecture
to accomplish their goals.

For developers, containers align well with the distributed nature of
microservice architectures and agile development, which speeds up release cycles
from months to days, sometimes hours. Because containers include everything
needed to run an application and are abstracted away from the underlying
infrastructure, DevOps teams can use them to build, test, and run new
applications or features without impacting any other aspects of the application
environment.

For operations, Kubernetes and containers make deployment easier by eliminating
dependencies on the underlying technology stack, like operating systems and
middleware. This independence from the infrastructure makes it easier to manage
and automate rolling deployments (blue-green, canary), dynamic scaling, and
resource allocation.

The flexibility of Kubernetes also makes it easier to scale applications and
make development pipelines more resilient. This enables DevOps teams to tap the
benefits of containerization at scale without running into the operational and
management challenges that would otherwise drain their productivity and internal
resources.<


THE CHALLENGES OF KUBERNETES AT SCALE: THE SERVICE MESH QUESTION>

Organizations with large, mature Kubernetes environments eventually graduate to
another problem — whether to use a service mesh. A service mesh controls
service-to-service communications for large-scale applications to improve
application performance and resiliency. These applications comprising many
microservices can experience performance challenges as application traffic grows
and requests between the various microservices exponentially increase. When this
happens, a service mesh provides an effective solution for routing requests
between these microservices and optimizing the flow of data between them.

Service meshes can effectively and securely manage the connections between
services so applications can continue to perform at a high level and meet
service-level agreements. While orchestration platforms, such as Kubernetes,
help developers use containers at scale without having to worry about the
underlying infrastructure or advanced container management techniques, service
meshes let them focus on adding business value with each new service they build
rather than having to worry about “utility code” to ensure secure and resilient
communication between services.

Service meshes are best suited for large, mature K8s infrastructures. For a
closer look at service meshes and whether your organization would benefit from
using one, see the blog What is a service mesh? Service mesh benefits and how to
overcome their challenges.


OBSERVABILITY CHALLENGES WITH KUBERNETES

The CNCF 2020 survey revealed that complexity is one of the top challenges in
using and deploying containers. This complexity presents unique observability
challenges when running Kubernetes applications and services on highly dynamic
distributed systems.

Foremost among these problems is that while Kubernetes orchestrates your
containers, it doesn’t offer any insight on the internal state of your
applications or issues that might be causing slowdowns or stoppages. That’s why
IT teams rely on telemetry data to gain a better understanding of the behavior
of their code during runtime. But while collecting logs, metrics, and
distributed traces is supported by many protocols like Prometheus and
OpenTelemetry, the real value comes from understanding how these constantly
changing data points relate to each other. It’s in these hard-to-see
relationships that performance issues reveal themselves.

Containerized application instances can come and go rapidly. For example, a pod
can be scheduled then terminated in a matter of milliseconds. They can also have
billions of dependencies. It’s true that monitoring agents on nodes can track
the state of the cluster and alert DevOps teams when anomalies occur, but what
if the issue is with the virtualization infrastructure?

DevOps teams need an automated, full-stack observability solution to stay on top
of their Kubernetes orchestration platforms. That’s where Dynatrace comes in.


MONITORING THE FULL KUBERNETES STACK

The Dynatrace platform — powered by the advanced AI engine, Davis — is the only
Kubernetes monitoring system with continuous automation that identifies and
prioritizes alerts from applications and infrastructure without changing code,
container images, or deployments.

For full mastery of Kubernetes, simply deploy the OneAgent Operator, and
Dynatrace can:

 * Track the availability, health, and resource utilization of Kubernetes
   infrastructure
 * Get an intuitive view on your workloads and quickly identify unexpected
   replica counts or excessive pod-level resource limits
 * Prioritize anomalies and automatically determine the exact root-cause
 * Automatically discover and instrument thousands of pods with no manual
   configuration changes.

With this critical information in one centralized interface, all teams within
the software development life cycle will be able to operate from a single source
of truth so they can resolve issues faster, and focus on innovations.

While DevOps and SREs will be happy to learn about these powerful capabilities,
Dynatrace’s value extends far beyond just Kubernetes observability. Dynatrace
leverages its powerful AI to provide end-to-end visibility into the entire
software stack, mapping and analyzing dependencies in near real time to
determine both the root cause of any disruption and the impact of slowdowns as
they pertain to business KPIs.

Regardless of your cloud platform, container runtime, service mesh layer, or the
number of nodes you are running, Dynatrace makes monitoring your Kubernetes
infrastructure — and everything else in your cloud environment — simple.


FURTHER YOUR KUBERNETES KNOWLEDGE

Kubernetes is a hard, complex implementation, and operations at the enterprise
level is not a walk in the park, requiring adequate monitoring and a different
approach than with classic stacks.

 * Take a deeper dive into Monitoring Kubernetes Infrastructure for day 2
   operations
 * Learn more about Kubernetes: Challenges for observability platforms
 * Ready to learn more about how Dynatrace can make you a Kubernetes monitoring
   pro:See Mastering Kubernetes with Dynatrace
 * See how Dynatrace Expands application and infrastructure observability with
   operational insights into Kubernetes pods
 * Successful Kubernetes Monitoring – Three Pitfalls to Avoid
 * How AI Solves the Kubernetes Complexity Conundrum
 * Hands-on workshop: Dynatrace with Kubernetes
 * Need help getting started? Monitor your Kubernetes clusters with Dynatrace






k8s kubernetes

--------------------------------------------------------------------------------

You liked this article? Feel free to share:


Steve Caron



The Author

As a Senior Sales Engineer for the Global Center of Excellence at Dynatrace,
Steve Caron focused on helping customers solve the challenges of providing
observability into cloud-native platforms and applicative workloads. Prior to
joining Dynatrace in 2011, Steve occupied various positions in software
development, testing, architecture, implementation, and training. Rest assured,
aside from the professional world, Steve doesn’t think or speak in the third
person; he prefers spending time with his family, traveling or reading about
pretty much anything.


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