It is encouraging to note that enterprises are moving to Kubernetes and containerizing their applications to overcome technical debt. However, this shift in microservices is akin to a double-edged sword: You get the flexibility, speed, and control of your modern apps, but at the cost of an increase in technical complexity and a team that runs out of juice quickly if things aren’t handled properly. Add to this the fact that enterprises are finding it necessary to adopt a multi-cloud approach to cater to demands from a dynamic market. This is because multi-cloud deployments provide flexibility, cost optimization, and risk management by spreading workloads across different cloud providers and regions. They also help avoid vendor lock-in and offer increased availability and disaster recovery options, which are needed if you want to be competitive.
This is where the need for a cloud-native tool is felt more than ever. Something which is powerful to orchestrate containers across clouds doesn’t require different cloud-specific skill sets or workflows and doesn’t cost a fortune. Enter Kubernetes: An open-source cloud-native container orchestration tool.
However, as the saying goes, open source is like a gym membership. It’s free to join, but you’ll end up paying for it in sweat and bugs! The same goes for Kubernetes or K8s. It is open source but very complicated to work with, requiring specific skill sets and infrastructure that work for your specific deployment needs. Setting up, managing, and deploying applications with Kubernetes is complex and requires a lot of time and resources.
In this blog, we wish to take you, the readers, through the different ways in which you can work with Kubernetes – either manage them yourselves locally and on the cloud or subscribe to managed Kubernetes service providers like Google, Microsoft, VMWare, and the likes for a “Kubernetes as a service” experience. The following sections will give clarity to folks who are new to Kubernetes or are already working with them on what makes sense: a self-managed setup or a subscription to Kubernetes as a service. Ultimately, no matter what you do, you will need a seamless mechanism to transition to and from either setup, for which Ozone would be the perfect low-code, no-nonsense companion for you, which you shall see by the end of this blog.
Self-managed Kubernetes
Self-managed Kubernetes is a method of deploying and managing a Kubernetes cluster locally on your own infrastructure, like a laptop or a server. It gives you full control over the configuration and management of your cluster, as well as the ability to customize it to meet your specific needs.
The need for having such a setup depends on your requirements. Developers can test apps and run cluster computing in a predictable and controlled way before it’s used in production environments, which helps shorten the turnaround time. You can also test on a non-critical system in case something goes wrong during testing. However, this should be used only in cases where an uptime guarantee is not required.
Getting Started With a Local Kubernetes Setup
Many solutions are available that facilitate running Kubernetes in local environments. The most popular ones are kind (Kubernetes in Docker), K3s, MicroK8s, and Minikube. Here’s a quick overview of some of these:
Minikube: It is the most popular one among its counterparts. All the Kubernetes’ processes are run inside a virtual machine which it creates on your local machine and deploys one node to make up a simple cluster. It is available for Linux, macOS, and Windows systems.
K3s: It is a lightweight version of Kubernetes built for edge computing, IoT, and other use cases where compute power required is minimal. Developers need not have a deep knowledge of the workings of Kubernetes to set it up.
Kind: It is short for “Kubernetes in Docker,” as each node runs in a Docker container. Hence, to use it, Docker is needed along with the latest release of this kind.
MicroK8s: It’s a great choice for IoT and Edge and runs on Linux. It is lightweight and focuses on simplicity and developer experience. MicroK8s comes with pre-packaged add-ons like DNS management, ML with Kubeflow, etc., giving Kubernetes some extra capabilities.
Projects like Minikube, k3s, kind, MicroK8s, etc., are implementations of the Kubernetes standard but on a single node, like a laptop (even run multi-node clusters inside a single laptop using VMs but with increased overheads). Hence, it is technically not ‘distributed’ computing, but it enables the developers to test their products on the same set of standards. These projects emulate a multi-node cluster system on a single-node system.
There are some benefits and drawbacks to self-managing Kubernetes as compared to its fully-managed, Kubernetes-as-a-service counterpart:
| Pros | Cons |
|---|---|
| More control over cost in case of custom workloads and limited nodes | Requires a high level of expertise to set up and maintain |
| Interoperability, customization, flexibility, and control over the cluster | Needs significant investment in time and resources |
| Scope for experimentations with direct access to Kubernetes | Challenges in scaling quickly in case of a large number of nodes |
| Highly configurable to meet specific security requirements and perfectly isolated | Support and troubleshooting for complex issues |
Managed Kubernetes (Kubernetes-as-a-service)
Kubernetes as a Service (KaaS) refers to a managed version of the Kubernetes platform that cloud providers or other third-party companies provide. This service eliminates the need for organizations to manage their own Kubernetes cluster and infrastructure and provides a simplified and centralized way of deploying and managing containerized applications.
KaaS usually includes features such as automatic updates, cluster scaling, monitoring, and maintenance and provides a unified API and dashboard for managing the entire application delivery pipeline. With KaaS, organizations can focus on developing and deploying their applications while the KaaS provider takes care of the underlying infrastructure and management tasks.
Getting Started with Managed Kubernetes
Kubernetes is implemented slightly differently based on the underlying hardware to optimize its performance. Hence, every cloud provider has a slightly different implementation of Kubernetes while adhering to the open standard decided by the Kubernetes project. Let’s have a look at some of the major managed Kubernetes services provided by Google, Microsoft, Amazon, and the like:
- Google GKE: Google Kubernetes Engine (GKE) is a managed, production-ready environment for deploying, managing, and scaling containerized applications using Google’s infrastructure. It also supports TPUs (Tensor Processing Units) along with GPUs as hardware accelerators for running ML, GPGPU, HPC, and other workloads. It has the most features and automation capabilities.
- Amazon EKS: Amazon initially launched its Elastic Container Service, and in June 2018, they released the Elastic Kubernetes Service (EKS). This is arguably the most widely used managed Kubernetes service. It also boasts a 99.95% SLA (Service Level Agreement: bond for performance negotiated between the cloud services provider and the client).
- VMWare Tanzu: The Tanzu Kubernetes Grid is built and supported by VMWare and offers a complete Kubernetes platform with built-in automation and observability. It integrates with other VMware products and services, including Tanzu Application Service (formerly Pivotal Cloud Foundry), enabling organizations to build and run modern applications on a unified, comprehensive platform.
- Microsoft AKS: Azure Kubernetes Service (AKS) is the most cost-effective option, which also integrates well with everything that’s Microsoft. Subscribers are charged per node, while the control plane is free. It has also been the quickest to provide newer Kubernetes versions and proactive in releasing minor patches.
- OpenShift Kubernetes Engine: It is a managed Kubernetes service by RedHat that provides basic functionality of Red Hat OpenShift. OpenShift Kubernetes Engine (OKE) can run on any cloud infrastructure, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform, providing flexibility and choice for organizations. Like its competitors, it offers multiple layers of security with full-stack automations from deployment to scaling and management.
Each of these cloud providers is a strong contender when it comes to evaluating a managed Kubernetes provider. Each offers a few unique benefits, which might be just what you need for your deployment requirements.
However, there are pros and cons to opting for Kubernetes-as-a-service, and the below table should help to make an informed choice:
| Pros | Cons |
|---|---|
| Automatic resource scaling and failover management for better availability | Cloud lock-in: Provider dependency limits configuration and customization options |
| Abstracts complexity of container management for ease of deployment | Limitations on the type of workload that can be run depending on the provider |
| Reduce operational costs and improve resource utilization | Higher costs in case of large-scale deployments or complex requirements |
| Improved security with RBAC & encryption to protect access and data breach | Specific skill sets required to work with specific cloud vendors and their tooling |
Overall, self-managed Kubernetes can be a good choice for organizations with a high level of expertise and resources available and requiring a high level of control and customization over their cluster environment. However, a managed Kubernetes service may be a better option for organizations that lack these resources. Before choosing self-managed Kubernetes, you should carefully consider your specific use case and the resources you have available to you.
Transitioning Workloads From Self-managed to Managed Kubernetes With Ozone
You might be using a self-managed Kubernetes setup for internal development, testing, or other use cases. Should you find the need to migrate your workloads from there to a managed Kubernetes scenario (production cluster for e.g.), there can be many complications you might face:
- Load balancer and backend storage type might need changing.
- Some changes in resource limiting may be required while deploying on a production cluster from a self-managed cluster.
- Different versions of dependencies may exist for the production cluster.
- Change in integrations – For example, on a local Minikube, a locally hosted Postgres might be used as a database. However, on AWS, it could likely be RDS (relational database service), for which code changes are necessary.
Using a unified platform like Ozone for all your deployment needs helps sort through most of these challenges and maybe offer even more features as part of their native capabilities:
- Ozone enables you to attach all your clusters from multiple clouds (managed) and your local developer environments (self-managed). It is easier to keep track of the changes that are required to be made while deploying from local clusters to a production cluster in a managed Kubernetes setup.
- It enables the team to automate these changes so that developers can push their code into a single repo, but the deployments can happen across multiple clusters in multiple clouds.
- It enables standardization of pipelines, so developers spend less time doing repetitive tasks.
