Cloud-native technologies such as containers and serverless computing are essential for building portable applications in the cloud. By leveraging these technologies, you can design applications that are more resilient, scalable, and adaptable to changes in your environment. One word to describe these three benefits is “portable”.
Unlike monolithic models that are cumbersome and nearly impossible to manage, cloud-native microservices architectures are modular. This approach gives you the freedom to choose the right tool for the job, the service that does one particular function well. This is where a cloud-native approach comes into play, providing an efficient process for updating and replacing individual components without impacting the entire workload. Developing with a cloud-native mindset enables a declarative approach to deployment of applications, supporting software stacks, system configurations, and more.
Think of containers as ultralight virtual machines designed for specific tasks. Containers are also ephemeral, one minute gone and the next minute they’re gone. It has no permanence. Instead, persistence is tied to block storage or other mounts within the host filesystem rather than the container itself.
Containerizing an application makes it portable. By providing a container image, you can deploy and run it on different operating systems and CPU architectures. A containerized application is a self-contained unit packaged with all required dependencies, libraries, and configuration files, so no code changes are required between different cloud environments. So let’s talk about how containers enable portability in a cloud-native design.
- Lightweight virtualization: Containers provide an isolated environment for running applications, sharing the host OS kernel but isolating process, file system, and network resources.
- Portability and Consistency: Containers package applications and their dependencies together so that they can run consistently across different environments, from development to production.
- Resource Efficient: Because containers isolate processes and share the host OS kernel, they are less resource intensive than virtual machines. No need for the overhead of running another “guest” OS on top of the host OS.
- Fast boot and deployment: Containers start quickly because they don’t need to boot a full OS, making them ideal for rapid deployment, scaling, and recovery scenarios.
- immutable infrastructure: Containers are designed to be immutable. This means it’s built once and doesn’t change, simplifying the deployment, versioning and rollback process and ensuring consistent behavior across environments.
When Should You Consider Containers?
Consistency can be maintained using containers. Certain aspects of development are omitted in staging and production environments. For example, detailed debug output. However, code that ships from development remains intact through ongoing testing and deployment cycles.
Containers are extremely resource efficient and super lightweight. We said that containers are similar to virtual machines, but they can grow to tens of megabytes, as opposed to the gigantic (or even smaller but wasted) VM gigs we’re used to. may become. The lighter it is, the faster it will boot. This is important for elastic and performant horizontal scaling in dynamic cloud computing environments. Containers are also designed to be immutable. If something changes, there is no need to embed the new changes inside the container. Just destroy it and create a new container. With this in mind, here are some additional considerations when deciding whether containers should be part of the cloud native model:
- Improved deployment consistency: Containers package applications and their dependencies together to ensure consistent behavior across different environments, simplify deployment, and reduce the risk of configuration-related issues.
- Improved scalability: Containers enable rapid scaling of applications by quickly launching new instances to meet increased demand, optimizing resource usage, and improving overall system performance.
- Cost-effective resource utilization: Containers consume fewer resources than traditional virtual machines, allowing businesses to run more instances on the same hardware, reducing cloud infrastructure costs.
- Shorten development and testing cycles: Using containers facilitates seamless transitions between development, test, and production environments, streamlining the development process and accelerating the release of new features and bug fixes.
- Simplified application management: A container orchestration platform manages the deployment, scaling, and maintenance of containerized applications, automating many operational tasks and reducing the burden on IT teams.
Container best practices
There are different ways to run containers, and they are all interoperable. For example, if you migrate from AWS, you can simply redeploy your container images to the new environment and your entire workload will be gone. There are various tools and engines that can be used to run containers. They all differ in resource utilization and price points. If you’re hosting on Linode (Akamai’s cloud computing service), you can use the Linode Kubernetes Engine (LKE) to run your containers. You can also launch Compose on Podman, HashiCorp Nomad, Docker Swarm, or a virtual machine.
These open standard tools enable rapid development and testing with the added value of simplified management when using services like LKE. Kubernetes becomes the control plane. Think of it as a control plane with all the knobs and dials for adjusting your containers using tools built on open standards. Additionally, when using platform-native products like AWS Elastic Container Service (ECS), you pay a different kind of usage fee.
Another important part of containers is understanding what you use to store and access your container images (called the registry). We highly recommend using Harbor. Harbor, a CNCF project, allows you to run a private container registry and control security around it.
We constantly test and have a very detailed regression test suite to ensure the performance and security of our code is of the highest quality. Containers should also have a plan for failure. If a container fails, what will its retry mechanism be? How will it be restarted? What impact does it have? How does the application recover? Is stateful data persisted on mapped volumes or bind mounts?
Here are some additional best practices for using containers as part of the cloud native development model.
- Use a lightweight base image. Start with a lightweight base image like this: Alpine Linux Or use BusyBox to reduce the overall size of your container and minimize your attack surface.
- Use container orchestration. Use container orchestration tools such as: Kubernetes, Hashicorp Nomad, Docker Swarmagain Apache Methos Manage and scale containers across multiple hosts.
- Use a container registryUse a container registry such as : docker hub, GitHub Package Registry, GitLab container registry, port, and so on to store and access container images. This makes it easy to share and deploy container images across multiple hosts and compute environments.
- Restrict container privileges. Limit the permissions of the container to only those necessary for its intended purpose. Deploy rootless containers whenever possible to reduce the risk of exploitation if the container is compromised.
- Implement resource constraints. Set resource constraints, such as CPU and memory limits, to prevent containers from overusing resources and impacting overall system performance.
- Keep your containers up to date. Keep your container images up-to-date with the latest security patches and updates to minimize the risk of vulnerabilities.
- Thoroughly test your container. Make sure they work as expected and are free of vulnerabilities before deploying them in production. Automate testing at every stage with CI pipelines to reduce human error.
- Implement container backup and recovery: Implement a backup and recovery strategy for the persistent data your containers interact with so that your workload can be quickly recovered in the event of a failure or disaster.