Kubernetes, or K8s for short, is a massively popular and developer-friendly cloud-based technology for deploying, scaling, and managing containerized applications, including software and, more recently, machine learning models. Kubernetes was originally created by Google for managing in-house application deployment, but now, Kubernetes is an open-source system maintained by the Cloud Native Computing Foundation (CNCF).
Kubernetes is a one-stop cloud-native platform for automating operations associated with container-based applications, like Docker. Its popularity and adoption in the software engineering and AI industry cannot be emphasized enough, with leading cloud providers, like AWS (EKS), Azure (AKS), and Google Cloud Platform (GKE), providing their own Kubernetes-based platform offerings.
It is important to consider the concept of containers that Kubernetes builds upon. Containers are a method of packaging apps, along with all their dependencies and configuration settings, so that the app can be seamlessly deployed across various runtime production environments. While alternatives, like virtual machines and Docker Swarm, abound, Kubernetes has emerged as the de facto platform of choice for container orchestration and management.
Swarm is Docker’s native platform for orchestrating clusters of Docker engines. Virtual machines are related to containers in that containers are more flexible, lightweight, and portable, as there is no need to install an OS in every instance. The evolution of virtual machines to containers to orchestration platforms like Kubernetes has helped organizations better manage their application deployment and operational workloads.
In this article, you will learn more about Kubernetes and its applications in the domain of software engineering and machine learning. Discover the many benefits that Kubernetes offers and why start-ups and enterprises should consider migrating their deployment systems to Kubernetes. You will explore a comprehensive overview of the key factors to consider and evaluate from an organizational perspective before making the decision of whether and when to migrate to Kubernetes from other architectures.
Common Problems that Kubernetes Solves
Kubernetes is the market-leading solution for the orchestration of container-based applications. According to the 2020 CNCF survey, 83% of respondents were using Kubernetes in production.
Kubernetes solves a multitude of problems by helping run containers at scale. Containers on their own are not self-sufficient and, thus, cannot be executed efficiently. This leads to problems, like increased app delivery times and operational burdens, causing delays to product launches and poor customer experience. Slower application development, deployment, and delivery cycles can result in a significant loss of revenue and customer trust. Migration to Kubernetes can help solve application deployment inefficiencies by offering the following advantages:
- Cost efficiency
Having a single, centralized platform to manage all application deployments leads to reduced costs associated with hosting and migration, as well as tech support. Furthermore, autoscaling and the selection of the right type of node also helps to optimize costs.
Kubernetes provides better consistency via a holistic runtime production environment for developers, quality assurance, and administrative staff.
As Kubernetes is portable across cloud as well as on-premises servers, any organization that has its infrastructure and databases hosted on the cloud, on-premises, or through a hybrid approach can use and scale Kubernetes for their applications.
Kubernetes provides for horizontal scalability as well as elasticity and automation with little to no performance issues or downtimes. Its autoscaling feature allows for the total containers to be scaled based on the application requirements. The number of resources can be scaled up or down per the requirement and demand of the service response.
Kubernetes has a number of security features, including controlling access to the Kubernetes API, controlling the capabilities of a workload or user at runtime, and protecting cluster components from being compromised.
Reasons to Avoid Kubernetes
Despite its many benefits and advantages, Kubernetes may not be the right choice for your organization just yet. While migration to Kubernetes might eventually pay off in terms of your investment on time, budget, and organizational efforts, there are still many reasons to avoid Kubernetes in the near term. So migrate only when the organizational processes, systems, staff, and culture are mature enough to adopt Kubernetes.
If the current system is not broken, does it make sense to overhaul it and potentially create new problems that may impact critical business goals? While Kubernetes is a smart long-term solution, there are many challenges to overcome before, during, and after the migration.
In general, it is advisable for a Kubernetes-ready organization to have already moved to the cloud and have considerable experience managing, developing, and deploying via cloud-based services and containerization as well. The turning point comes when the organization is beginning to face difficulties in terms of scaling and stability, and the operational overhead is eating up too much organizational bandwidth, thereby adversely impacting engineering excellence.
Steep Learning Curve
It is important to understand that while Kubernetes may be easier to onboard and start, it is difficult to execute well and involves a steep learning curve. Many organizations jump on the Kubernetes bandwagon too soon, based on a single successful proof of concept. Such pilot experiments, while valuable in terms of the experience, should be treated with caution. Most pilot experiments are not run under production loads, stress tested for scale, integrated with CI/CD pipelines, or based on a stable Kubernetes configuration.
In terms of cost, it is important to bear in mind that Kubernetes will not save costs out of the gate, and it will take some time for the systems to mature before the cost-efficiency of Kubernetes becomes clear. For any organization to make such a critical business decision, the technical and engineering leadership must understand this key economic aspect and show full commitment and patience until the return on investment is evident.
Furthermore, the migration can take up a lot more time than anticipated. It takes time to learn, configure, and optimize Kubernetes settings for various applications across the enterprise. There are no standard timelines for attaining organizational maturity, as it markedly varies from one organization to another.
If your organization does not have engineers who have a deep understanding of Kubernetes’s basics and containers, especially aspects like networking, Docker, pods, and nodes, then migration will be an uphill challenge. Management of Kubernetes takes specialized knowledge, and the limited availability of such talent is a major bottleneck when considering migration to Kubernetes. Even if there are experienced developers in an organization, they need to keep abreast of the latest developments from Kubernetes’s open-source community and make periodic updates to avoid disruption.
Additionally, there is a whole suite of additional tools (for example, kubectl CLI), services, CI/CD workflows, and DevOps and MLOps practices that need to be mastered for confidently managing the entire ecosystem of Kubernetes-based tasks and operations.
Considerations for Migrating from Various Architectures
As shown in the figure below, migrating to Kubernetes is an evolutionary journey that is specific to the existing architectures; for example, monoliths, Docker Swarm, or virtual machines. In the case of a monolith to Kubernetes migration, the change in complexity is immense. Monolith applications are easier to debug and test, and their simplicity makes deployment that much easier. They also facilitate faster end-to-end testing. During this type of migration, it is important to note that not all existing workloads are ready to move to containers. Knowing what workloads to move and what kind of applications can or cannot be containerized are crucial considerations.
Migration from Docker Swarm tends to be relatively easier compared to a transition from monolith applications since container-based applications already exist. While Swarm is simpler and easier to operate, Kubernetes is a more complicated platform with a steep learning curve. For this migration, factors like the nature of current infrastructure, configuration and scale, governance, identity and access controls, networking and storage management, and customer-specific integrations and applications must be taken into consideration.
Migrating directly from virtual machines to Kubernetes might appear like a tough challenge. However, this transition has been made easier through the open-source project KubeVirt, which enables virtual machine workloads to be run as pods inside a Kubernetes cluster. It provides a unified platform for deploying applications based on booth containers and virtual machines in a common, shared environment.
Kubernetes has emerged as the de facto choice for the orchestration of container-based applications. Its popularity and success are due to its many advantages that enable secure, portable, scalable, cost-efficient, and consistent deployment from cloud, on-premises, or hybrid environments. However, the decision to migrate to Kubernetes is not always straightforward. It involves the evaluation of several factors, including the prior experience with cloud or containers, availability of expert staff, overcoming of the steep learning curve, cost and duration of the migration, and organizational support for patiently investing in a platform with benefits that may only become apparent in the longer term.
In this article, you also learned the top considerations for migrating from other architectures, like monoliths, virtual machines, and Docker Swarm. While the decision to move to Kubernetes may eventually be a no-brainer, timing the migration requires a critical understanding of the complex ecosystem of Kubernetes architecture and business-specific analysis of whether your organization is ready to move.