What Is Kubernetes?

Kubernetes is an open source system for deploying, managing, and scaling containerized applications, particularly cloud native applications written using microservices. Sometimes abbreviated as K8s, Kubernetes allows administrators to cluster containers together to make them easier to manage.

What does Kubernetes do? It starts by deploying containers that hold either a complete application or a component of an application, often called a service. These containers are deployed onto servers, which can be in a cloud, on-premises, spread across several clouds in a multicloud configuration, or in a hybrid cloud/on-premises configuration.

Once containers are deployed, Kubernetes provides discovery, enabling an application or service to find the services it needs from other containers. Kubernetes directs traffic to the correct resource under its control. If a system is running multiple copies of a containerized application or service, usually to accommodate high demand, Kubernetes automatically balances the load.

Part of the power of Kubernetes is that it can group containers together in ways that make sense for the deployment. For example, it can group together several containers that share the same storage and network into a pod—that’s a word you’ll see often. You’ll also see references to Kubernetes nodes. These are individual machines, either physical services or traditional virtual machines, that house containers. A collection of nodes running Kubernetes—that is, set of physical or virtual containers—is referred to as a cluster.

For every container, pod, node, and cluster, Kubernetes will manage storage resources, detect and restart failed containers, a process called “healing,” and even implement security protocols across a distributed application. It can be configured to handle passwords, security tokens, and encryption keys, making it easier to protect critical assets.

The Kubernetes platform’s development is overseen by the Cloud Native Computing Foundation (CNCF).

Containers vs. Virtual Machines

Containers often replace a different model of cloud deployment: virtual machines (VMs). Containers are more lightweight than VMs because they use the host server’s underlying operating system and device drivers. By contrast, VMs are larger and require more processing resources because each one contains its own operating system. Therefore, a server can run more containers than VMs, and more importantly, can dedicate more processor and memory resources to running applications rather than maintaining multiple operating system instances—one for each VM. While there are specialized cases where VMs are required, containers offer a far more efficient model for most cloud application deployments.

Kubernetes vs. Containers

While Kubernetes and containers are related, they’re best considered complementary technologies. Kubernetes is a management platform for containers that’s often used for large-scale deployments numbering in the hundreds or thousands of containers. Containers themselves put all necessary code and dependencies for a capability—be it a microservice or complete application—into a single executable format.

Kubernetes is a tool for managing many containers at once, usually in the cloud. Sometimes referred to as the operating system for the cloud, Kubernetes lets organizations manage containers at scale.

Key Takeaways

Kubernetes orchestrates large deployments of containers by automatically deploying, scaling, and even fixing and restarting containers for a large-scale cloud deployment.
By maximizing utilization of cloud resources, Kubernetes can reduce the cost of deploying enterprise applications.
Kubernetes provides a rich ecosystem that contains everything needed to build and deploy containerized applications, then manage the containers in production.

Kubernetes Explained

Kubernetes is a platform that automates the deployment, scaling, and management of containers. Kubernetes also has capabilities for healing containers—that is, detecting when they’re malfunctioning and then fixing them. Kubernetes is all about orchestration: Like a symphony conductor directing musicians, it knows what needs to be done, keeps all containers in place and working properly, and acts if something goes wrong.

And just as a symphony conductor works from a musical score that calls for a piano, violins, a few cellos, and a brass section, Kubernetes has a document explaining the desired state of an application’s containers. This document, called a configuration file, describes the functions required to make the application work and specifies which containers can provide those functions. The configuration file also lists the servers, storage devices, networks, and other physical machines available for the application’s containers.

When an application is launched, Kubernetes loads the necessary containers onto the available servers according to the configuration file, then starts running the software within those containers. It monitors the resource utilization of each server (or node), making sure that the systems aren’t overloaded. If they are, it moves containers onto a less-loaded server by starting up a new container and then stopping the old one. If a container itself is overloaded, Kubernetes starts up an identical copy of that container on a different server and automatically configures a load balancer to split the workload between them. As demand increases, it starts a third container, and so on as needed. Later, if the workload decreases, Kubernetes shuts down any unnecessary containers to help reduce costs and free up server resources for other tasks.

When a container fails, Kubernetes quickly starts up a new container on another server and redirects network traffic away from the problem area, providing for rapid failover.

Why Use Kubernetes?

Imagine you have an application that requires hundreds or thousands of containers, each providing services needed by the application. Systems administrators could manually deploy and manage the containers, possibly with the aid of automation tools for specific tasks such as load balancing or detecting faults. In fact, there are even tools for managing containers in small-scale deployments. These are most often used by software developers and DevOps teams when building and testing containerized software.

However, without a more complete orchestration system, the demands of system administration eventually become overwhelming.

The beauty of Kubernetes is that it’s a single platform that handles automation tasks from deploying containers to scaling them to resolving faults. In addition, Kubernetes is open source and widely supported, including by every major cloud provider. In short, it’s ubiquitous. That makes Kubernetes the preferred system for managing a large, containerized enterprise application.

Benefits of Using Kubernetes

When an organization decides to containerize its applications, adopting Kubernetes to orchestrate those containers just makes sense—especially given the wide-ranging payoffs.

High availability: Kubernetes is designed for high availability (HA). Its automatic features for healing malfunctioning containers by restarting them either on the same or a different server are one piece of the HA equation. So, too, is Kubernetes’ ability to spin up many copies of a container, place them on different servers or even in different data centers, and then split the workload among them. An extra feature: If you’re deploying a software update to a containerized application, Kubernetes can be configured to roll out the updated containers gradually. If a fault is detected, it can revert to the previous version.
Isolation: Each container operates within its own isolated environment, providing a secure and protected space for application execution. This isolation, which is designed to prevent external interference and resource conflicts, helps maintain the stability and integrity of the application. Because Kubernetes follows its configuration file when automating container deployments and routing network traffic between them, it offers an additional layer of protection against potential human error that might compromise uptime.
Portability: Containers are highly portable among different types of servers, and even different clouds. The only real constraint is that containers must be built for a specific operating system. For example, the software inside a container written for Linux must run on a Linux server, and the software inside a container written for Windows must run on a Windows server. Within that one limitation, Kubernetes can move containers between servers, data centers, and even clouds. In fact, Kubernetes can manage a large application with containers spanning multiple data centers and cloud providers—again, automatically.
Resource efficiency: A container doesn’t know much about the server it’s running on. But Kubernetes, by contrast, can determine each server’s utilization of processors, memory, storage, and network communications. It can deploy and move containers between servers to maximize each server’s utilization, helping reduce costs. Kubernetes also starts up new containers only when necessary and shuts them down again when they’re no longer needed, which can also cut costs.
Scalability: Consider cashiers in a grocery store. If one checkout has too long a queue, the manager can open another line. Similarly, if a containerized piece of software gets overloaded, Kubernetes can detect that and start another identical container with the same service, splitting the workload between the two. If fact, it can start up a hundred more identical containers if needed. All automatically.

Key Components of Kubernetes Architecture

The Google engineers who created Kubernetes chose the name based on an ancient Greek word for pilot or helmsman—the person who steers a ship—because it moves and manages a fleet of containers. And much like an actual container ship, Kubernetes depends on many components working together to get its data cargo where it needs to be. These are the terms that you’ll commonly encounter in discussions about containers and the Kubernetes orchestration platform.

Cluster: A cluster is a set of machines, referred to as nodes, used to run containerized applications managed by Kubernetes. A cluster typically consists of a master node and multiple worker nodes.
Node: A node is a virtual or physical machine that forms part of a Kubernetes cluster.
Pod: A pod is a single container or a set of containers running on a Kubernetes cluster. Pods are the fundamental unit of deployment in Kubernetes; a pod consists of one or more containers guaranteed to be colocated on the same node.
Control plane: The control plane consists of the Kubernetes API server, Kubernetes scheduler, Kubernetes controller manager, application controller, and cloud controller manager. It manages nodes and pods across a cluster for high availability.
Container: A container is an image that encapsulates software and its dependencies, forming the basis of containerization.
Deployment: A deployment manages replicated applications, represented by pods, that are deployed onto the nodes of a cluster.
Replicaset: A replicaset is some number of pod replicas running the same services simultaneously, providing redundancy and high availability for all their containers.
Service: A service describes how to access applications represented by a set of pods. Services typically work with load balancers to optimize internal and external access to a cluster.

Key Features of Kubernetes

Kubernetes is not merely a container management platform; it’s a sophisticated orchestration tool that automates and simplifies the entire application lifecycle, from design to deployment to production use. Its robust feature set helps efficiently manage complex, distributed applications. Here are some of the key features of Kubernetes:

Container orchestration: Kubernetes automates the deployment, scaling, and maintenance of containers. It intelligently manages container placement, optimizing resource utilization and efficient load balancing.
Self-healing and autoscaling: Kubernetes can automatically detect and respond to failures. It can swiftly restart, replicate, or reschedule containers, maintaining the application’s health and availability.
Service discovery and load balancing: Kubernetes enables communication among containers through built-in service discovery. It also offers load balancing capabilities, distributing incoming traffic across multiple containers to provide high availability and make resource utilization more efficient.
Storage orchestration: Kubernetes simplifies storage management by allowing applications to access various storage systems, locally and on public and private clouds.
Automatic rollouts and rollbacks: Kubernetes automates the rollout of new software versions, gradually introducing them to the production environment. If problems arise, it can automatically roll back to the previous stable version, minimizing disruption to users.
Configuration management: Kubernetes simplifies configuration management, centralizing and managing application configurations across multiple environments. This also simplifies updates and maintenance.
Declarative configuration and automation: Kubernetes uses a configuration file, sometimes called a YAML file, meaning “YAML Ain’t Markup Language” or “Yet Another Markup Language,” depending on who you ask. This file, which is understandable to both Kubernetes and actual humans, describes what the complete application looks like and how to manage the application’s containers through automation.
Storage and data management: Kubernetes can create persistent volumes to provision and manage storage within a cluster, independent of any specific pod or container. They can also be assigned to specific tasks as needed. Persistent volumes make it simpler to meet applications’ data management requirements.

Common Challenges of Adopting Kubernetes

While Kubernetes has been honed over many years, there is a steep learning curve. Still, it’s far better to take the time to learn Kubernetes than to use other methods for managing large, distributed applications. Here are a few challenges to consider:

Complexity in deployment and management: There are a lot of moving parts in a large-scale application, including both the software requirements and the infrastructure it will run on. The Kubernetes configuration file needs to accommodate such concepts as cluster scaling, healing, failover, monitoring and logging, and plans for upgrading parts of the system.
Security risks and compliance gaps: Containers and containerized applications have the potential to be highly secure, but that security isn’t automatic. Network policies, for example, must be properly configured inside Kubernetes to help protect all parts of the application against malicious access from inside or outside the cluster. If there are passwords and encryption keys, they must be protected—not stored in easy-to-read documents such as the YAML configuration file. The container image repository must also be protected to help guard against tampering. And all these measures must be properly documented.
Networking complexity: Simply put, the complexity of even a midsize containerized application makes it difficult to manage without automation. Add in hundreds of thousands of pods, each consisting of multiple containers, as well as connections to internal and external storage, other applications, end users, the internet, and so on. And what if the application spans multiple data centers or even several clouds? Kubernetes can assist with network configuration, but there are still decisions to be made. In a very large Kubernetes deployment, network configuration issues can be difficult to diagnose and remediate.

Kubernetes Use Cases

Enterprises use Kubernetes for many types of applications; you’ll find it in ecommerce, manufacturing, research, finance, utilities—basically every industry. Many large distributed applications that use containers can benefit from Kubernetes orchestration and automation. Here are a few of the common scenarios where Kubernetes can really shine.

Web-scale microservices architecture: It’s common to have hundreds or thousands of microservices making up a complex web application, handling tasks such as user authentication, session management, search, shopping carts, payments, inventory management, and AI-driven recommendation engines. Kubernetes helps isolate each type of microservice in its own pod and then managing those services independently.
Elastic scaling for high-traffic applications: Kubernetes can help with scaling a high-traffic application’s services by dynamically allocating new containers and load balancing to quickly accommodate changes in the workload. Kubernetes can base its automatic scaling on resource utilization, such as when a container’s processor or memory utilization exceeds certain limits or when a server is nearing its capacity. It can also scale back down when demand lessens. This can keep the application’s performance within desired parameters—a specified response time, for example—while also reducing resource consumption during low-traffic periods.
Safer deployments of software updates: Kubernetes integrates with many software development tools that use the continuous integration/continuous deployment (CI/CD) model for incrementally improving enterprise software. Kubernetes can roll these updates out gradually in new containers and quickly roll back to an earlier version if problems occur. It can even run two versions in parallel and report on their comparative performance.

Kubernetes and Artificial Intelligence

The intersection of Kubernetes with AI can be transformative for a business, since Kubernetes can play a pivotal role in managing and orchestrating AI workloads in the cloud. In particular, Kubernetes provides a robust and flexible platform for AI training and deployment, offering several advantages:

Automation: Kubernetes can automatically deploy and update AI models.
Containerization: Kubernetes enables the packaging of AI models and their dependencies into containers, helping ensure consistent performance and easy deployment across various environments. This portability is vital for AI because it allows models to be trained and deployed in different settings.
High availability: High availability is critical for AI applications serving as the backbone of essential business processes. Kubernetes' self-healing capabilities provide high availability for AI applications by automatically detecting and responding to failures.
Scalability: AI applications often require significant computational resources, especially during training and large-scale inference. Kubernetes can dynamically scale AI workloads based on demand for optimal resource utilization and cost efficiency.

Embracing the Kubernetes Ecosystem

The widespread adoption of Kubernetes in the past decade has led to the emergence of a thriving ecosystem of tools, services, and supporting technologies. This ecosystem further enhances Kubernetes’ capabilities, providing organizations with diverse options to tailor their infrastructure and development practices. The main categories in this ecosystem include:

CI/CD tools: Tools for continuous integration and continuous deployment integrate with Kubernetes to automate build, test, and deployment processes. These tools create an efficient development lifecycle.
Container registries: These serve as centralized repositories for storing and managing container images. These registries seamlessly integrate with Kubernetes, enabling easy deployment and management of containerized applications.
Monitoring and logging tools: To deliver efficient tracking and analysis, Kubernetes integrates with dozens of monitoring and logging systems that provide insights into application performance, behavior, and potential issues.
Security solutions: Kubernetes integrates with a range of security tools as well, including container-native firewalls and runtime protection tools. These systems fortify the applications running on Kubernetes, helping safeguard them against potential threats and vulnerabilities.

Any discussion of the Kubernetes ecosystem would be incomplete without a mention of KubeCon, the annual conference for Kubernetes developers and users hosted by the Cloud Native Computing Foundation (CNCF). Since the first KubeCon convened in 2015 with 500 attendees, the event has grown substantially. In 2024, the Salt Lake City conference drew more than 9,000 developers and engineers.

5 Best Practices for Deployment

Best practices for Kubernetes could fill a book—and in fact, many have been written. Make no mistake: Kubernetes is complex. However, following these best practices can help companies leverage this platform successfully.

Optimize resource allocation. You don’t want to pay for more servers or other compute, storage, and networking resources than your application needs at any given moment. Take advantage of Kubernetes’ ability to autoscale clusters and pods up and down based on real-time metrics and actual use.
Implement observability and logging. It’s hard to fully grasp what’s going on in a large-scale enterprise application. The best visibility tools are used for log aggregation, so all the performance and fault-report data is in one place. These tools provide metrics-laden reports and dashboards as well as tracing based on the aggregated data.
Implement GitOps and infrastructure as code. GitOps is a process of provisioning cloud infrastructure, in which a network design is stored in configuration files that are stored in a Git repository. Git is a popular open source software configuration management system. The configuration files are called infrastructure as code, or IaC, and Kubernetes uses them to create a network of servers, storage, and connectivity resources.
Enhance security and compliance. Use role-based access control, or RBAC, and zero trust computing methodologies to set up a “least privilege by default” network that’s resistant to attack, even by rogue or corrupted software running within a container. Consult your legal department to see what auditing logs and configuration files should be kept for compliance.
Simplify multicloud deployments. Kubernetes allows you to automate the deployment and orchestration of large-scale applications across many data centers and more than one cloud. Such a deployment is inherently complex to architect, manage, and troubleshoot. Whenever possible, simplify such deployments with abstraction, centralized identity management, and cloud-agnostic storage systems. You can use GitOps and IaC to provision identical networks in multiple data centers and in different clouds.

Simplify Kubernetes Management with Oracle

OCI Kubernetes Engine (OKE) is an Oracle-managed container orchestration service that can reduce the time and cost of building cloud native applications. OKE helps simplify operations of enterprise-grade Kubernetes at scale, letting you easily deploy and manage resource-intensive workloads, such as AI, with automatic scaling, patching, and upgrades. OKE offers:

Autoscaling: OKE automatically adjusts compute resources based on application load, which can reduce your costs.
Efficiency: Processors—including the most advanced models used for AI workloads—can be scarce, but OKE job scheduling can help you maximize resource utilization.
Portability: OKE is consistent across clouds and on-premises in your own data center, enabling portability and avoiding vendor lock-in.
Simplicity: OKE can reduce the time and expense needed to manage the complexities of Kubernetes infrastructure.
Reliability: Automatic upgrades and security patching can boost reliability for your enterprise application.

The ability of the Kubernetes platform to orchestrate and automate application deployment and management has revolutionized the way applications run in the cloud native era. As Kubernetes continues to evolve and gain momentum, it’s becoming even more significant. Businesses that embrace Kubernetes can gain a marked competitive advantage, so understanding this technology is vital for developers and business leaders alike.

Kubernetes is important to cloud native development—which is key to more resilient, scalable, and agile applications, whether they’re running in the cloud, on-premises, or in a hybrid or multicloud model.

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Kubernetes FAQs

Why is Kubernetes a critical component of enterprise cloud strategy?

Kubernetes is critical because it’s how enterprises deploy, scale, and manage their distributed applications, especially those running in the cloud. Kubernetes automation improves application reliability while also maximizing the utilization of resources, thereby keeping costs down.

What key factors should enterprises consider when adopting Kubernetes at scale?

There are two main factors to consider. The first is organizational readiness: Are your engineers and developers ready for this model of application development and deployment? The other is technical: Do you have the right architectural approach to design and deploy Kubernetes and containers in a way that’s secure, stable, and compliant with governance requirements?

What are the main cost considerations for enterprises running Kubernetes at scale?

Kubernetes can help reduce costs by maximizing the use of cloud resources and by releasing resources such as servers and storage when they’re not needed. However, there are costs involved in training, tooling, and optimizing your network and application models to take full advantage of available resources.

How can enterprises ensure a smooth transition to Kubernetes from traditional infrastructure?

It’s a big shift! Begin by introducing Kubernetes for a small application that may already be running in one or a relatively few containers. Consider starting with a cloud-based Kubernetes service that manages the data plane on your behalf rather than trying to learn, deploy, and operate all the different elements on your own. Experiment with upgrades, rollbacks, monitoring, deliberate failures, and more to help your team gain the experience needed to tackle bigger projects, such as refactoring from a monolithic application to one based on microservices.