Kubernetes: Difference between revisions

Latest revision as of 17:44, 6 July 2025

Kubernetes is an open-source container orchestration platform designed to automate the deployment, scaling, and operation of application containers across clusters of hosts. Initially developed by Google, Kubernetes has become one of the leading technologies in cloud-native computing, enabling IT organizations to manage containerized applications in a more efficient and resilient manner. The platform abstracts away the underlying infrastructure, allowing developers to focus on their applications while providing operators with tools to manage and maintain them.

Background

Kubernetes originated from Google's experience in managing large-scale containerized applications. The system is based on Google's Borg system, which was built to handle large workloads in the cloud. In 2014, Google released Kubernetes as an open-source project. Since then, the platform has gained wide adoption and has been supported by a robust community. The project is now maintained by the Cloud Native Computing Foundation (CNCF), which fosters its growth and supports its ecosystem.

The name "Kubernetes" is derived from the Greek word for helmsman or pilot, reflecting its role in navigating complex containerized environments. The adoption of Kubernetes corresponds with the rise of microservices architecture, where applications are composed of multiple loosely-coupled services. Kubernetes provides the necessary infrastructure to deploy, scale, and manage these services efficiently.

Architecture

Kubernetes' architecture consists of several key components that collectively manage containerized applications. Understanding this architecture is crucial for anyone looking to implement Kubernetes effectively.

Control Plane

The control plane is the brain of a Kubernetes cluster. It is responsible for managing the state of the cluster by maintaining the desired state specified by users. The main components of the control plane include the following:

kube-apiserver: This component acts as the entry point for all REST commands used to control the cluster. It serves as the interface between the user and the cluster, allowing users and components to communicate with the control plane.
etcd: This is a distributed key-value store that stores all cluster data, including the configuration and the current state of various objects. It is designed to be reliable and consistent, ensuring that data is available across all nodes in the cluster.
kube-scheduler: The kube-scheduler watches for newly created Pods (the smallest deployable units in Kubernetes) that do not have a node assigned. It selects a suitable node for them to run based on resource availability and other constraints.
kube-controller-manager: This component is responsible for regulating the state of the system. It runs controller processes, which monitor the state of cluster resources and make necessary changes to maintain the desired state.

Node Components

Each node in a Kubernetes cluster has its own set of components that manage the containers running on that node. The main node components include:

kubelet: This is the primary agent running on each node and is responsible for ensuring that containers are running in a Pod. The kubelet receives commands from the control plane, reports the state of the node, and manages local container lifecycles.
kube-proxy: This component manages network routing for services within the cluster. It automatically routes traffic to active Pods, handling load balancing and ensuring smooth communication between services.
Container Runtime: Kubernetes supports different container runtimes, which are responsible for running the containers. Common examples are Docker, containerd, and cri-o. The runtime pulls the necessary images and manages their lifecycle on the node.

Add-ons

Kubernetes also supports various add-ons to extend its capabilities. Some commonly used add-ons include:

CoreDNS: A DNS server that provides name resolution services for services and Pods within the cluster.
Dashboard: A web-based user interface that provides visibility into the cluster, allowing users to manage application deployments and monitor resources.
Metrics Server: This is a cluster-wide aggregator of monitoring data that is essential for scaling and performance practices.

Implementation

Implementing Kubernetes can be achieved through various deployment options tailored to different workloads and organizational needs. Organizations can choose from cloud-based, on-premises, or hybrid solutions depending on their architecture and compliance requirements.

Cloud Providers

Most major cloud providers offer managed Kubernetes services, simplifying the installation and maintenance effort required to get a cluster up and running. Services like Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS), and Amazon Elastic Kubernetes Service (EKS) provide users with tools for provisioning, scaling, and managing Kubernetes clusters without having to manage the underlying hardware or infrastructure explicitly.

These services often include additional features such as automatic updates, integrated monitoring, and built-in security measures, which further ease the operational challenges of running Kubernetes in production.

On-Premises Deployments

For organizations that need to maintain control over their infrastructure, Kubernetes can also be installed on-premises. Several tools exist for deploying Kubernetes in such environments, including:

Kubeadm: A tool designed to simplify the process of setting up a Kubernetes cluster. It provides straightforward commands for initializing the control plane and joining worker nodes.
Rancher: A complete management platform for Kubernetes that also allows users to deploy and manage multiple clusters across various environments.
OpenShift: An enterprise Kubernetes distribution that provides additional features like developer tools, integrated CI/CD pipelines, and security enhancements out of the box.

Hybrid and Multi-Cloud Environments

Kubernetes is also well-suited for hybrid and multi-cloud deployments. Organizations can leverage Kubernetes to manage workloads that span across on-premises infrastructure and various cloud environments, providing a consistent operational model. Either by building custom solutions or utilizing platforms such as Red Hat OpenShift, organizations can maintain flexibility and optimize resource usage across diverse infrastructures.

Applications

Kubernetes has found applications across various industries and for numerous use cases. Its orchestration abilities make it suitable for numerous scenarios, including:

Microservices Architecture

Kubernetes is often adopted by organizations transitioning to a microservices architecture. In such environments, applications are broken down into smaller, more manageable services that can be deployed and scaled independently. Kubernetes provides native support for managing these services, ensuring they can seamlessly communicate and scale based on demand.

Continuous Integration and Continuous Deployment (CI/CD)

Kubernetes plays a significant role in modern CI/CD pipelines, automating the deployment of applications across multiple environments. Developers can create and test their applications in isolated environments before deploying them to production. Kubernetes' robust set of APIs and native capabilities for rolling updates and rollbacks enable organizations to implement robust CI/CD practices that enhance release velocity while minimizing downtime.

Big Data and Machine Learning

Organizations using Kubernetes for big data and machine learning workloads benefit from its ability to scale resources dynamically. Data-intensive applications such as Apache Spark and TensorFlow can be deployed on Kubernetes, enabling organizations to optimize resource usage and facilitate the processing of large datasets efficiently. With Kubernetes, data scientists and engineers can configure clusters that automatically adjust resource allocation based on workload demands.

Edge Computing

The rise of edge computing has also seen adoption of Kubernetes as organizations look to deploy containers in remote locations. Kubernetes can manage distributed workloads across edge devices, providing consistent configurations and toolsets regardless of deployment location. This capability is essential for managing IoT applications where processing needs to occur closer to data sources for real-time analysis.

Real-world Examples

Numerous organizations and technology companies have adopted Kubernetes to leverage its capabilities. Some notable examples include:

Google

As the original creator of Kubernetes, Google employs the platform extensively within its data centers to manage containerized applications. Google Cloud Platform utilizes Kubernetes for its container solutions, proving both scalability and reliability while serving its vast customer base.

Spotify

Spotify has adopted Kubernetes to power its Backend as a Service (BaaS) platform, which delivers personalized content and recommendations to its millions of users worldwide. By utilizing Kubernetes, Spotify can easily manage its containerized microservices architecture, leading to improvements in developer productivity and faster deployment cycles.

The New York Times

The New York Times implemented Kubernetes to modernize its content delivery platform, enabling it to serve millions of readers with high availability and reduced latency. By leveraging Kubernetes, the news organization can efficiently deploy resources to accommodate spikes in traffic during breaking news events while effectively managing its infrastructure costs.

Criticism and Limitations

Despite its widespread adoption, Kubernetes is not without its challenges and criticisms. Understanding these limitations is essential for organizations considering the platform.

Complexity

One of the primary criticisms of Kubernetes is its complexity. With many components, configurations, and APIs to understand, new users may find the learning curve steep. For smaller organizations or teams without dedicated DevOps resources, managing Kubernetes can prove challenging. Organizations may invest significant time and effort into training and tooling to ensure effective utilization.

Resource Consumption

Kubernetes itself can introduce overhead, particularly for smaller applications. Running Kubernetes clusters involves provisioning infrastructure to support the control plane and cluster components, which can be resource-intensive. This overhead can be a consideration for teams with lean engineering efforts or smaller workloads, as the resources consumed by Kubernetes may detract from application performance.

Debugging Challenges

Debugging applications running on Kubernetes can also be complex compared to traditional deployments. The containerized environment obscures traditional methods of troubleshooting. Developers often need to rely on tools such as logging and tracing frameworks to diagnose issues. Addressing performance bottlenecks may also require in-depth knowledge of Kubernetes networking and storage mechanisms, further challenging debugging efforts.

Ecosystem Fragmentation

The Kubernetes ecosystem is vast and rapidly evolving, which can lead to fragmentation. Various projects and tools are created to enhance functionalities, but this also means that selecting the right tools and ensuring compatibility can be overwhelming. Organizations must stay informed and perform thorough evaluations of third-party integrations to maintain a stable environment.

References

@@ Line 1: / Line 1: @@
-'''Kubernetes''' is an open-source container orchestration platform designed to automate the deployment, scaling, and management of containerized applications. Originally developed by Google, Kubernetes has become a cornerstone of modern application architecture, providing a robust framework that allows developers to run and manage large-scale applications efficiently across clusters of hosts. The system works with a wide range of container tools and is known for its flexibility, extensibility, and strong community support.
+'''Kubernetes''' is an open-source container orchestration platform designed to automate the deployment, scaling, and operation of application containers across clusters of hosts. Initially developed by Google, Kubernetes has become one of the leading technologies in cloud-native computing, enabling IT organizations to manage containerized applications in a more efficient and resilient manner. The platform abstracts away the underlying infrastructure, allowing developers to focus on their applications while providing operators with tools to manage and maintain them.
 == Background ==
-Kubernetes was announced by Google in 2014 and is built upon years of experience the company gained while running applications in containers at scale. The architecture is derived from a system called Borg, which was instrumental in helping Google manage its massive workloads. Initially, Kubernetes started as an academic project known as "Project Seven" before being released as an open-source project. It quickly gained popularity and was adopted by many organizations, becoming one of the leading platforms for managing containerized applications.
+Kubernetes originated from Google's experience in managing large-scale containerized applications. The system is based on Google's Borg system, which was built to handle large workloads in the cloud. In 2014, Google released Kubernetes as an open-source project. Since then, the platform has gained wide adoption and has been supported by a robust community. The project is now maintained by the Cloud Native Computing Foundation (CNCF), which fosters its growth and supports its ecosystem.
-The term "Kubernetes" comes from the Greek word κυβερνήτης (kubernētēs), meaning "helmsman" or "pilot," implying that the platform steers the operational aspects of containerized applications. Kubernetes supports a wide variety of workloads, making it a highly versatile tool for developers and organizations.
+The name "Kubernetes" is derived from the Greek word for helmsman or pilot, reflecting its role in navigating complex containerized environments. The adoption of Kubernetes corresponds with the rise of microservices architecture, where applications are composed of multiple loosely-coupled services. Kubernetes provides the necessary infrastructure to deploy, scale, and manage these services efficiently.
 == Architecture ==
-Kubernetes architecture is primarily composed of a master node and multiple worker nodes, forming a cluster. Each component of the architecture plays a specific role in managing the lifecycle of applications and ensuring high availability and fault tolerance.
+Kubernetes' architecture consists of several key components that collectively manage containerized applications. Understanding this architecture is crucial for anyone looking to implement Kubernetes effectively.
-=== Master Node ===
+=== Control Plane ===
-The master node is the control plane of the Kubernetes cluster. It manages the cluster and provides the API for users and developers to interact with. The key components of the master node include:
+The control plane is the brain of a Kubernetes cluster. It is responsible for managing the state of the cluster by maintaining the desired state specified by users. The main components of the control plane include the following:
-* '''API Server''': The API server is the face of the Kubernetes control plane and provides the APIs that can be used by clients. It processes REST requests, validates them, and updates the corresponding objects in the etcd datastore.
+* '''kube-apiserver''': This component acts as the entry point for all REST commands used to control the cluster. It serves as the interface between the user and the cluster, allowing users and components to communicate with the control plane.
-* '''etcd''': etcd is a distributed key-value store that holds the configuration data and the state of the Kubernetes cluster. It serves as the single source of truth for the cluster.
+* '''etcd''': This is a distributed key-value store that stores all cluster data, including the configuration and the current state of various objects. It is designed to be reliable and consistent, ensuring that data is available across all nodes in the cluster.
-* '''Controller Manager''': This component manages various controllers that are responsible for regulating the state of the cluster. For instance, it ensures that the desired number of replicas of a pod are running or managing the deployment of updates.
+* '''kube-scheduler''': The kube-scheduler watches for newly created Pods (the smallest deployable units in Kubernetes) that do not have a node assigned. It selects a suitable node for them to run based on resource availability and other constraints.
-* '''Scheduler''': The scheduler assigns tasks to worker nodes based on resource availability and constraints defined by the user. It optimizes the placement of pods for efficient resource utilization.
+* '''kube-controller-manager''': This component is responsible for regulating the state of the system. It runs controller processes, which monitor the state of cluster resources and make necessary changes to maintain the desired state.
-=== Worker Nodes ===
+=== Node Components ===
-Worker nodes are where the actual containerized applications run. Each worker node contains several components that facilitate the running of applications:
+Each node in a Kubernetes cluster has its own set of components that manage the containers running on that node. The main node components include:
-* '''Kubelet''': The kubelet is an agent that runs on each worker node, ensuring that containers are running in pods as described in the Kubernetes API. It communicates with the API server to receive instructions and report the status of the containers.
+* '''kubelet''': This is the primary agent running on each node and is responsible for ensuring that containers are running in a Pod. The kubelet receives commands from the control plane, reports the state of the node, and manages local container lifecycles.
-* '''Kube Proxy''': This network proxy runs on each worker node and manages network communication between pods and services. It helps facilitate service discovery and load balancing.
+* '''kube-proxy''': This component manages network routing for services within the cluster. It automatically routes traffic to active Pods, handling load balancing and ensuring smooth communication between services.
-* '''Container Runtime''': The container runtime is responsible for running containers. Kubernetes supports various container runtimes, including Docker, containerd, and CRI-O, allowing flexibility in container management.
+* '''Container Runtime''': Kubernetes supports different container runtimes, which are responsible for running the containers. Common examples are Docker, containerd, and cri-o. The runtime pulls the necessary images and manages their lifecycle on the node.
-== Core Concepts ==
+=== Add-ons ===
-Understanding the core concepts of Kubernetes is crucial for deploying and managing applications effectively. The platform uses several abstractions that help organize and manage resources efficiently.
+Kubernetes also supports various add-ons to extend its capabilities. Some commonly used add-ons include:
+* '''CoreDNS''': A DNS server that provides name resolution services for services and Pods within the cluster.
+* '''Dashboard''': A web-based user interface that provides visibility into the cluster, allowing users to manage application deployments and monitor resources.
+* '''Metrics Server''': This is a cluster-wide aggregator of monitoring data that is essential for scaling and performance practices.
-=== Pods ===
+== Implementation ==
+Implementing Kubernetes can be achieved through various deployment options tailored to different workloads and organizational needs. Organizations can choose from cloud-based, on-premises, or hybrid solutions depending on their architecture and compliance requirements.
+=== Cloud Providers ===
-A pod is the smallest deployable unit in Kubernetes, representing a single instance of a running process in a cluster. Each pod can contain one or more containers that share the same network namespace and filesystem. Pods are designed to work together and can communicate freely with each other, simplifying the development of microservices architectures.
+Most major cloud providers offer managed Kubernetes services, simplifying the installation and maintenance effort required to get a cluster up and running. Services like Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS), and Amazon Elastic Kubernetes Service (EKS) provide users with tools for provisioning, scaling, and managing Kubernetes clusters without having to manage the underlying hardware or infrastructure explicitly.
-=== Deployments ===
+These services often include additional features such as automatic updates, integrated monitoring, and built-in security measures, which further ease the operational challenges of running Kubernetes in production.
-A deployment is a higher-level abstraction that manages the lifecycle of pods. It provides declarative updates to pods and replica sets, allowing users to define the desired state of an application. When changes are made to the deployment, the controller will automatically adjust the pods to match the user-defined state, managing rollouts and rollbacks efficiently.
+=== On-Premises Deployments ===
-=== Services ===
+For organizations that need to maintain control over their infrastructure, Kubernetes can also be installed on-premises. Several tools exist for deploying Kubernetes in such environments, including:
+* '''Kubeadm''': A tool designed to simplify the process of setting up a Kubernetes cluster. It provides straightforward commands for initializing the control plane and joining worker nodes.
+* '''Rancher''': A complete management platform for Kubernetes that also allows users to deploy and manage multiple clusters across various environments.
+* '''OpenShift''': An enterprise Kubernetes distribution that provides additional features like developer tools, integrated CI/CD pipelines, and security enhancements out of the box.
-Kubernetes Services enable communication between different parts of an application and allow for decoupled architecture. A service defines a logical set of pods and a policy by which to access them. It provides features like load balancing and service discovery, allowing containers to communicate without needing to know the specific details of their internal architecture.
+=== Hybrid and Multi-Cloud Environments ===
-=== Volumes ===
+Kubernetes is also well-suited for hybrid and multi-cloud deployments. Organizations can leverage Kubernetes to manage workloads that span across on-premises infrastructure and various cloud environments, providing a consistent operational model. Either by building custom solutions or utilizing platforms such as Red Hat OpenShift, organizations can maintain flexibility and optimize resource usage across diverse infrastructures.
-Volumes in Kubernetes are used for data storage. They provide a way to persist data beyond the lifecycle of individual containers. Kubernetes supports various types of volumes, such as emptyDir, hostPath, and cloud storage solutions like Persistent Volumes (PV) and Persistent Volume Claims (PVC), allowing flexible data management in a containerized environment.
+== Applications ==
-== Implementation ==
+Kubernetes has found applications across various industries and for numerous use cases. Its orchestration abilities make it suitable for numerous scenarios, including:
+=== Microservices Architecture ===
-Kubernetes can be deployed in various environments, including on-premises data centers, public cloud platforms, and hybrid configurations. The flexibility in deployment models enables organizations to choose an approach that best satisfies their operational requirements and organizational goals.
+Kubernetes is often adopted by organizations transitioning to a microservices architecture. In such environments, applications are broken down into smaller, more manageable services that can be deployed and scaled independently. Kubernetes provides native support for managing these services, ensuring they can seamlessly communicate and scale based on demand.
-=== Cloud Providers ===
+=== Continuous Integration and Continuous Deployment (CI/CD) ===
-Many major cloud service providers offer managed Kubernetes services, which simplify the deployment and management of Kubernetes clusters. Providers such as Google Cloud with Google Kubernetes Engine (GKE), Amazon Web Services with Elastic Kubernetes Service (EKS), and Microsoft Azure with Azure Kubernetes Service (AKS) facilitate the creation and maintenance of clusters while handling routine tasks like upgrades, scaling, and security.
+Kubernetes plays a significant role in modern CI/CD pipelines, automating the deployment of applications across multiple environments. Developers can create and test their applications in isolated environments before deploying them to production. Kubernetes' robust set of APIs and native capabilities for rolling updates and rollbacks enable organizations to implement robust CI/CD practices that enhance release velocity while minimizing downtime.
-=== On-Premises Deployment ===
+=== Big Data and Machine Learning ===
-Organizations may also choose to implement Kubernetes in their existing data centers. Various tools like Kubeadm, OpenShift, and Rancher provide frameworks and user interfaces to simplify the installation and management of Kubernetes clusters on-premises. This approach allows businesses to leverage their existing hardware and networking infrastructure, often combined with best practices for security and isolation.
+Organizations using Kubernetes for big data and machine learning workloads benefit from its ability to scale resources dynamically. Data-intensive applications such as Apache Spark and TensorFlow can be deployed on Kubernetes, enabling organizations to optimize resource usage and facilitate the processing of large datasets efficiently. With Kubernetes, data scientists and engineers can configure clusters that automatically adjust resource allocation based on workload demands.
-=== Hybrid and Multi-cloud Deployments ===
+=== Edge Computing ===
-Hybrid and multi-cloud strategies are becoming increasingly popular as organizations seek to balance flexibility, cost, and performance. Kubernetes seamlessly integrates across public and private clouds, allowing workloads to be easily moved and managed regardless of their environment. Tools such as Istio and Linkerd can help manage traffic flow across different services in hybrid deployments, while Terraform and Pulumi can be used for infrastructure as code, enabling consistent provisioning across environments.
+The rise of edge computing has also seen adoption of Kubernetes as organizations look to deploy containers in remote locations. Kubernetes can manage distributed workloads across edge devices, providing consistent configurations and toolsets regardless of deployment location. This capability is essential for managing IoT applications where processing needs to occur closer to data sources for real-time analysis.
-== Real-World Examples ==
+== Real-world Examples ==
-Kubernetes is widely adopted across various industries due to its ability to scale applications effectively and provide resilient architectures. Some notable companies utilizing Kubernetes include:
+Numerous organizations and technology companies have adopted Kubernetes to leverage its capabilities. Some notable examples include:
 === Google ===
-As the creators of Kubernetes, Google utilizes the platform for its cloud services and internal infrastructure. Google Cloud employs Kubernetes to offer customers scalable solutions for running containerized applications, taking full advantage of automated scaling and orchestration capabilities.
+As the original creator of Kubernetes, Google employs the platform extensively within its data centers to manage containerized applications. Google Cloud Platform utilizes Kubernetes for its container solutions, proving both scalability and reliability while serving its vast customer base.
 === Spotify ===
-Spotify employs Kubernetes to manage its microservices architecture, enabling the music streaming platform to scale services dynamically based on user demand. This approach enhances resource utilization and operational efficiency while fostering rapid deployment of new features.
+Spotify has adopted Kubernetes to power its Backend as a Service (BaaS) platform, which delivers personalized content and recommendations to its millions of users worldwide. By utilizing Kubernetes, Spotify can easily manage its containerized microservices architecture, leading to improvements in developer productivity and faster deployment cycles.
-=== Shopify ===
-Shopify leverages Kubernetes to efficiently manage its e-commerce platform, accommodating fluctuations in web traffic especially during peak shopping seasons. With Kubernetes, Shopify can quickly deploy and scale applications, ensuring a smooth user experience.
 === The New York Times ===
-The New York Times has adopted Kubernetes for managing its content delivery network (CDN) and providing personalized experiences for its readers. By using Kubernetes, The Times can effectively manage deployments of various microservices while maintaining high reliability and performance.
+The New York Times implemented Kubernetes to modernize its content delivery platform, enabling it to serve millions of readers with high availability and reduced latency. By leveraging Kubernetes, the news organization can efficiently deploy resources to accommodate spikes in traffic during breaking news events while effectively managing its infrastructure costs.
 == Criticism and Limitations ==
-Despite its many advantages, Kubernetes is not without criticism. Several limitations and challenges have been raised by the community.
+Despite its widespread adoption, Kubernetes is not without its challenges and criticisms. Understanding these limitations is essential for organizations considering the platform.
 === Complexity ===
-Kubernetes has a steep learning curve due to its complex architecture and myriad of features. Organizations may find it challenging to get started, requiring substantial investment in training and time to develop the necessary expertise. Managing a Kubernetes cluster can be resource-intensive, necessitating a dedicated team of professionals to handle operations.
+One of the primary criticisms of Kubernetes is its complexity. With many components, configurations, and APIs to understand, new users may find the learning curve steep. For smaller organizations or teams without dedicated DevOps resources, managing Kubernetes can prove challenging. Organizations may invest significant time and effort into training and tooling to ensure effective utilization.
+=== Resource Consumption ===
+Kubernetes itself can introduce overhead, particularly for smaller applications. Running Kubernetes clusters involves provisioning infrastructure to support the control plane and cluster components, which can be resource-intensive. This overhead can be a consideration for teams with lean engineering efforts or smaller workloads, as the resources consumed by Kubernetes may detract from application performance.
-=== Overhead ===
+=== Debugging Challenges ===
-The control plane and architecture of Kubernetes may introduce operational overhead, particularly for smaller applications or organizations. The deployment of Kubernetes may overcomplicate simpler applications that do not require orchestration, leading to potential inefficiencies.
+Debugging applications running on Kubernetes can also be complex compared to traditional deployments. The containerized environment obscures traditional methods of troubleshooting. Developers often need to rely on tools such as logging and tracing frameworks to diagnose issues. Addressing performance bottlenecks may also require in-depth knowledge of Kubernetes networking and storage mechanisms, further challenging debugging efforts.
-=== Security Considerations ===
+=== Ecosystem Fragmentation ===
-As with any complex infrastructure, security is a significant concern when deploying Kubernetes. Misconfigurations can lead to vulnerabilities in the cluster, exposing sensitive data or services. Best practices for security must be followed, including network policies, role-based access control (RBAC), and regular audits to ensure a secure environment.
+The Kubernetes ecosystem is vast and rapidly evolving, which can lead to fragmentation. Various projects and tools are created to enhance functionalities, but this also means that selecting the right tools and ensuring compatibility can be overwhelming. Organizations must stay informed and perform thorough evaluations of third-party integrations to maintain a stable environment.
 == See also ==
+* [[Cloud native]]
 * [[Containerization]]
-* [[Docker (software)]]
 * [[Microservices]]
-* [[Cloud computing]]
+* [[Cloud Native Computing Foundation]]
 * [[OpenShift]]
-* [[Helm (package manager)]]
-* [[Istio]]
 == References ==
-* [https://kubernetes.io/ Kubernetes Official Site]
+* [https://kubernetes.io/ Kubernetes Official Website]
 * [https://cloud.google.com/kubernetes-engine Google Kubernetes Engine]
-* [https://aws.amazon.com/eks/ Amazon EKS]
 * [https://azure.microsoft.com/en-us/services/kubernetes-service/ Azure Kubernetes Service]
-* [https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/ Overview of Kubernetes Concepts]
+* [https://aws.amazon.com/eks/ Amazon Elastic Kubernetes Service]
+* [https://www.redhat.com/en/openshift OpenShift by Red Hat]
-[[Category:Computing]]
 [[Category:Cloud computing]]
-[[Category:Containerization]]
+[[Category:Container orchestration]]
+[[Category:Open-source software]]