Kubernetes is widely used as an orchestration platform for containers. It helps to speed up the process of deploying, scaling, and managing containers for applications. Kubernetes management, however, is not without its difficulties. Cluster resource management presents a considerable obstacle.
Introduction:
In recent years, Kubernetes has become the most popular orchestration of containers. For enterprises seeking to install, maintain, and scale containerized applications, it is an increasingly common option thanks to its many useful features. Kubernetes management, however, is not without its difficulties. This post will discuss three typical Kubernetes problems and their respective solutions. To make sure your Kubernetes setup is effective, scalable, & safe, we will go over how to handle cluster assets, how to successfully scale applications, as well as how to adopt security recommendations. Any company planning to use Kubernetes for its container workloads would do well to familiarize themselves with these difficulties and the ways in which they might be overcome.
Pain 1: Developer Productivity
Infrastructural support is not a goal in itself. The operation team’s clusters are meant to facilitate delivery of applications for the development teams they back.
Despite the widespread adoption of the phrase “DevOps,” few programmers possess the knowledge necessary to become true experts in web native infrastructures like Kubernetes. As we will show in this article, they should rather be writing code for features than overseeing server maintenance.
All it takes is a few dedicated individuals who aren’t afraid to roll up their sleeves and get their hands dirty in order to make a difference in the world. Giving in to their demands isn’t always a walk in the park.
Take the common practice of putting fresh application code through its paces in a test suite.
The programmer is looking for a completely fresh cluster, or numerous clusters, each with its own customized installation of Kubernetes as well as other software. This is crucial for ensuring reliable testing that can foretell how a product will perform in production. As appealing as it may be, replicating a production server arrangement on a local computer is simply not practical.
If an examination fails for an easy cause, the developer may wish to restart the CI/CD pipeline right away.
It’s not as simple as that, though.
It takes resources and time, regardless of whether you have the ability to immediately respond to a request for an additional cluster, to prepare and launch the cluster. That causes delays for your development team.
This is a tricky situation. Now extrapolate this to hundreds of development teams daily pushing numerous code streams.
However to know better insights on Containerization, Kubernetes Online Training plays an important role.
Providing Virtual Clusters for Developers’ Use –
What do you do then? Virtual clusters provide a potential solution. Developers have access to virtual clusters whilst the infrastructure team maintains control over the host clusters. Because virtual clusters are self-contained and pose no threat to the core infrastructure, you can easily deploy one for each team with minimal effort.
Spectro Cloud’s technology is built on the vcluster free software project, however it is optimized for business use and simple to implement. In other words, we’ve made it more aesthetically pleasing, created a fully descriptive approach using the Cluster API in the background, linked it to role-based entry controls, & packaged it all up as a SaaS offering.
Using Palette, the infrastructure team may design cluster profiles from which developers can buy custom clusters. A separate field can be reserved for each group. Naturally, you can automate everything by making REST API requests or using a Terraform provider. While the infrastructure team still retains fine-grained control, this method gives developers more leeway as well as the opportunity to test their work under production-like conditions.
Pain 2: Multicluster Headaches
Each user is given a single Kubernetes cluster to begin with. But in today’s era, stable teams are the exception.
If you create separate clusters for the development, staging, and manufacturing phases, that number soon increases to three.
Using kubectl, k9s, as well as other free software, you can manually administer a small number of clusters. However, when you have more than a few dozen groups, managing Kubernetes becomes a major undertaking.
How can we get beyond this obstacle?
The first rule of managing several clusters at once is that you shouldn’t be handling each cluster independently using kubectl. You use a declarative style to define their setup instead.
The whole cluster, including its architecture, is based on the “future state” description, which is a crock. In a nutshell, you ought to be ready to manually construct your entire cluster stack from its description.
Multiple clusters, clouds, and ecosystems –
It’s beneficial to be cloud-agnostic at the beginning if you plan on having a large number of clusters in the future.
It’s easy to become stuck with only one public cloud service. There is a single product line and vocabulary to master. Also, cloud service providers may often try to keep you from leaving by offering rewards for staying with them.
But there are various scenarios in which you might be operating in a multicloud setup. Sometimes, providers are brought in-house due to acquisitions and mergers. Multiple clouds can be used to gain access to specialized capabilities, reduce overall risk, and boost availability.
We have also noticed that many businesses are using an amalgam architectural strategy for deploying Kubernetes, utilizing both on-premises and online deployment tools.
A single point of control is crucial when coordinating several Kubernetes environments. You’ll require deployment tools that let you roll out the same code across numerous cloud service providers or on-premises and cloud configurations. It aids standardization efforts & streamlines operational framework.
Pain 3: The Edge Learning Curve
You may expand your search beyond the data center as well as cloud and have a peek at the edge.
Edge computing is being adopted by businesses so that applications can be deployed to the point of value creation, such as in eateries, factories, and various other off-the-beaten-path locales.
It is a well-known fact that the odds of being struck by lightning are slim to none. Your clusters could even consist of single-node gadgets, since the equipment is often low-powered. It can be challenging to perform remote management if the site’s connectivity is spotty or has a low data transfer rate. Defending against hardware modifications is a brand new front in the fight against breaches of security.
And here’s the killer part: deploying computers to tens of thousands of sites is a real possibility when discussing restaurant chains or factories. There won’t be an on-site Kubernetes specialist (or even an IT man) to assist with adding new devices or troubleshooting setup difficulties.
These are significant obstacles, but there are ways to overcome them.
To take use of low-touch or no-touch boarding methods, all one needs to do is connect in a device’s power & ethernet cable and wait for the provisioning process to finish automatically sans any human participation.
The ability to set up your entire cluster in a single operation, including the installation of the operating system on the fundamental device, is a necessary condition for this deployment. Declarative provisioning greatly reduces the complexity of large-scale rollouts. The plan is to have provisioning begin on its own and construct the framework up to a point where it can communicate with headquarters, from which point you can centrally manage the deployment.
If your OS is immutable, you can further reduce risk after your cluster is up and running. Since there are fewer potential points of failure, your clusters can rest safer on this structure. Therefore, be mindful when selecting an operating system. There are several options, both free and paid software, to choose from. To get going, have a look at this contrast.
Having Difficulties? It’s Not Just You.
You may expect to encounter similar difficulties as a platform group as you expand Kubernetes in production. They present a significant challenge. However, there are methods available that can help you achieve achievement.
You’re part of a large community, thanks to Kubernete -, an open-source, cloud-based orchestration tool. You can submit an application to a wide variety of available projects. And at conferences like KubeCon, you may learn from the vast pool of knowledge that the community possesses.
Our most important piece of advice is this, though: never attempt a do-it-yourself megaproject.
DIY is a great place to start learning and testing the waters with a new idea. But keep in mind that the “do it yourself” route only gets more challenging over time. No longer are we at a point where “doing Kubernetes on your own” is required.
Conclusion
To avoid performance difficulties, application downtime, and higher expenses, Kubernetes’s resources, such as CPU, memory, & storage, must be assigned & managed effectively. The issue can be fixed by setting limitations and quotas on the use of resources. The problem of scaling apps is another obstacle. An efficient method for scaling apps is the Horizontal Pod Autoscaling (HPA) feature, which adjusts the quantity of pod replicas in a deployment in response to changes in the pods’ CPU consumption. Last but not least, Kubernetes privacy is paramount. Security best practices, like activating encrypting for all network traffic and employing robust authentication & permission systems, can help overcome this obstacle.
Author Bio
Meravath Raju is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.
