Embedded systems, which are often resource-constrained, require efficient storage management to optimize performance and minimize resource usage. Docker, a containerization platform, provides a convenient way to package and deploy applications in embedded systems, making it easier to manage and distribute software across devices. Before understanding the docker storage in embedded systems, let us first have a quick overview of what Docker is.
Docker is a platform that provides an efficient and streamlined way to package, distribute, and run applications in isolated environment called a container. A container is a lightweight, standalone executable package that includes everything that is needed to run an application (including code, libraries, their dependencies etc.). In other words, we can say that containers provide a consistent and isolated environment for applications to run. Docker is one of the platforms that can be used to run our application in containers.
With Docker, you can create, deploy, and manage applications as containers that run consistently across different hardware architectures and operating systems. Docker provides a simple and consistent way to manage complex applications, streamline development workflows, and reduce deployment risks.
Docker was first released in March 2013 by a company called Docker, Inc. This platform quickly gained popularity among developers and operations teams due to its ease of use, portability, and scalability. Today, Docker is widely used across a range of industries and applications, from web development to scientific research to IoT to 5G and what not.
The Docker platform have many key components. Some of them are mentioned below. (We will cover each of them in some other blog post)
Docker Engine: This is the core component of the Docker platform. It is a lightweight runtime environment that provides all the functionality which are needed to create and run the Docker containers. The Docker Engine includes a container runtime, an image format, a network driver, and a storage driver.
Docker Hub: Docker Hub makes it easy to find, share, and distribute Docker images with other developers and organizations. Docker Hub also provides features like automated builds, webhooks, and team collaboration tools.
Docker CLI: It is a command-line tool which allows interaction with the Docker Engine and perform common tasks like creating, running, and managing Docker containers. The Docker CLI provides a simple and intuitive interface for working with Docker containers and images. This is very popular & must among developers.
Docker Compose: This is a tool for defining and running multi-container Docker applications. Docker Compose allows you to define the services and dependencies for an application in a simple YAML file and then start and stop the entire application (having multiple services) with a single command.
Docker Swarm: This is a tool for managing clusters of Docker nodes. Docker Swarm allows you to deploy and manage Docker containers across multiple nodes in a cluster, providing a scalable and fault-tolerant environment for running applications.
on the other hand, Embedded systems are computing devices that are designed for a specific purpose and are often integrated into other devices or systems e.g., smart home thermostats, industrial control system, medical monitoring devices, home appliances, automotive systems, aerospace guidance systems etc. These systems typically have limited resources in terms of processing power, memory, and storage, and are often required to operate in harsh or remote environments.
Traditionally, these embedded systems have been developed and deployed using a monolithic approach, where all the application code and dependencies are bundled together and deployed as a single binary image. This approach has several drawbacks, including:
- Limited portability
- Limited scalability
- Limited modularity
- Limited flexibility
Initially the embedded industry had resistance for using the docker but as the memory & processor becomes somewhat cheaper, the whole industry has not adopted docker with both the hands. Docker has also provided the solution to the above-mentioned problems by packaging the applications and dependencies as containers. Now, by combining the docker with the embedded systems, we can design the Embedded product that is not dependent on any specific HW & is more portable, more scalable, more modular and flexible.
Concepts of Docker Storage in Embedded Systems
For beginners, the docker storage concepts can be a little bit confusing, but if paid attention you understand them and then it becomes easier to work with Docker. Let us understand then one by one.
Docker images: A Docker image is a read-only template that contains the application code, libraries, and dependencies required to run an application. In embedded world language, we can compare it to the flash memory of the microcontroller where the whole firmware is written. You can create a Docker image by writing a Dockerfile, which is a text file that contains instructions on how to build a docker image. Once the image is built, it can be stored in a Docker registry, such as Docker Hub, where it can be accessed by others.
Docker images are stored as read-only layers on the host’s file system, typically on the solid-state drive (SSD) or hard disk drive (HDD). When a Docker image is pulled from a registry, it is downloaded from the registry and stored on the host’s file system.
When a new Docker container is created from an image, Docker uses a copy-on-write mechanism to create a new read-write layer on top of the read-only image layer. This new layer is stored on the host’s file system and is referred to as the container layer. Any changes made to the container’s file system are written to the container layer, while the image layer remains read-only.
The amount of memory used by a Docker image itself depends on the size of the image and the number of layers it contains. When an image is pulled, it is downloaded and stored on the host’s file system, typically in the /var/lib/docker directory. This requires storage space on the host’s SSD or HDD, but does not consume any RAM.
Docker containers: A Docker container is a running instance of a Docker image. It’s like a lightweight virtual machine that runs on top of the host operating system. Containers are isolated from each other and from the host system. Container have their own file system, network interfaces, and process space. Containers are created from images, and each container can have its own configuration and environment variables.
When a Docker container is started, Docker takes a portion of the host’s RAM to the container for its runtime. This memory is used by the container’s processes to execute and store temporary data. The amount of memory allocated to the container can be specified using the -m or --memory flag when starting the container.
In addition to RAM memory, Docker also uses the host’s storage (i.e. from SSD or HDD) to store container images and running container file systems. Docker uses a copy-on-write mechanism to create new containers based on existing images. When a container is started, Docker creates a read-write layer on top of the read-only image layer. Any changes made to the container’s file system are written to the read-write layer, which is stored on the host’s storage.
Docker also uses the host’s storage to persist container data that needs to survive the container’s lifecycle, such as logs or databases. This can be done using Docker volumes, which are directories on the host’s file system that are mounted into the container (explained in next bullet). Any data written to the volume by the container is stored on the host’s storage, outside of the container file system.
Docker volumes: A Docker volume is a persistent storage mechanism that can be used to store data that needs to be shared between containers or between the host and the container. Volumes can be used to store database files, logs, or any other type of data that needs to persist even if the container is deleted or recreated. Volumes can be managed by Docker or by a third-party storage driver. We can compare it with the EEPROM of an embedded world where the MCU stores the data which can be used later even after restart or simple/OTA binary upgrade.
When a Docker volume is created, Docker creates a directory on the host’s file system and mounts it into the container. Any data written to the volume by the container is stored in the directory on the host’s file system. Because the volume is stored outside of the container file system, the data can persist even after the container is deleted or stopped.
Docker volumes can be created with different drivers, which determine how the data is stored and accessed. The default driver is the local driver, which stores the data on the host’s file system
Different types of storage drivers
Docker supports multiple storage drivers that can be used to manage the storage of Docker images, containers, and volumes. Storage drivers can be thought of as plugins that interface with the underlying storage infrastructure to provide different levels of performance, reliability, and functionality. Some of the popular types of storage drivers available in Docker are:
aufs (Advanced Multi-Layered Unification Filesystem): This is the oldest and most widely used storage driver in Docker. It uses a layered approach to store images and containers, where each layer is represented as a directory that contains files and metadata. The aufs driver provides good performance and is compatible with most Linux distributions.
Overlay: These drivers use the OverlayFS file system to manage storage. The OverlayFS file system allows multiple file systems to be layered on top of each other and provides an efficient way of storing the Docker images and containers. The overlay2 driver is an updated version of the original overlay driver, which provides improved performance and stability.
Device-mapper: This driver uses the Device Mapper subsystem in the Linux kernel to create block devices that are used to store Docker images and containers. It supports copy-on-write (COW) and thin provisioning, which allows Docker to create and manage multiple images and containers using a single physical storage device.
btrfs (B-Tree File System): This driver uses the Btrfs file system to manage storage. Btrfs is a modern file system that provides features such as snapshots, subvolumes, and compression, which make it suitable for managing Docker images and containers.
ZFS (Zettabyte File System): This driver uses the ZFS file system to manage storage. ZFS is a high-performance file system that provides features such as snapshots, data deduplication, and compression, which make it suitable for managing Docker images and containers.
We should pay attention for selecting the driver for docker storage in embedded system as it is directly proportional to the performance of system.
Overview of Docker Storage commands
Docker provides many commands/APIs that allow users to manage storage resources for Docker images, containers, and volumes. These APIs can be accessed using command-line tools, programming languages, or graphical interfaces, and provide a standardized way to interact with Docker storage resources.
Below are most commonly used Docker storage commands:
Docker Image commands: These commands allow users to manage Docker images, including listing, creating, and deleting images. Users can also inspect the metadata of images and perform actions such as pushing and pulling images to and from Docker registries. Some of the CLI commands are:
docker images #To get list of all the loaded images
docker rmi <image-name> #To delete an image
docker pull <image-name> #To pull an image from a Docker registry
Docker Container commands: It allows users to manage Docker containers, including starting, stopping, and deleting containers. Users can also inspect the metadata of containers and perform actions such as copying files to and from containers. Some of the CLI commands are:
docker run <image-name> # To start a Docker container from an image
docker ps # To list all the running docker containers
docker stop <container-id> # To stop a running docker container
Docker Volume command: It allows users to manage Docker volumes, including creating, deleting, and listing volumes. Users can also inspect the metadata of volumes and perform actions such as attaching and detaching volumes from containers.
docker volume create <volume-name> # To create a Docker volume
docker volume ls # To list all Docker volumes
docker volume rm <volume-name> # To remove a Docker volume
Docker Remote commands: These commands allows users to interact with Docker over a network using HTTP requests. Users can perform all the actions available in the Docker storage APIs, including managing images, containers, volumes, and plugins.
docker -H <remote-host> ps # To list all Docker containers on a remote host
docker -H <remote-host> pull <image-name> #To pull a Docker image from a remote registry
docker -H <remote-host> run <image-name> #To start a Docker container on a remote host
Considerations for Docker Storage in Embedded Systems
While using Docker in embedded systems, some specific storage considerations needs to be taken care. Here are some key considerations:
Storage capacity: Embedded systems typically have limited storage capacity, which means that Docker images, containers, and volumes must be optimized for size. It’s important to only include the necessary files and libraries in Docker images and to keep the number of containers and volumes to a minimum. This is particularly important when deploying multiple containers or images on a single device, as the space used by each instance can quickly add up. Therefore, it is important to keep the number of containers and images to a minimum and to only include the necessary files and libraries.
Storage performance: Choosing the right storage driver is important to ensure that Docker storage operations are optimized for the specific hardware and operating system. Embedded systems often have slower storage devices than traditional servers or desktop computers, which means that Docker storage operations may take longer. It’s important to choose a storage driver that is optimized for the specific hardware and to use caching and compression techniques to improve performance.
Optimizing docker storage in embedded system for size and performance involves choosing the right storage driver and implementing best practices for reducing the size of Docker images, containers, and volumes. Some techniques for optimizing storage in embedded systems include:
- Using multi-stage builds to reduce the size of Docker images by removing unneeded dependencies and files.
- Using compression to reduce the size of Docker volumes and images.
- Limiting the number of layers in a Docker image to reduce its size.
- Avoiding the use of large base images and instead starting with minimal base images.
Storage durability: Embedded systems may experience power outages or other failures that could result in data loss. It’s important to use storage drivers that provide data durability features such as journaling or check-summing, and to use backup and recovery mechanisms to protect against data loss.
Security: Embedded systems may be used in environments where security is critical, such as industrial control systems or medical devices. It’s important to use storage drivers that provide encryption and access control features, and to implement secure storage practices such as using encrypted volumes and limiting access to sensitive data.
Resource usage: Embedded systems typically have limited processing power and memory, which means that Docker storage operations may compete with other system processes for resources. It’s important to monitor and optimize resource usage to avoid performance degradation or system crashes.
So, using the Docker storage in embedded systems requires very careful consideration of any specific storage needs and the constraints of the system. It is also very important to choose the storage drivers and the APIs that are optimized for the hardware.
Best Practices for Docker Storage in Embedded Systems
Docker is a very powerful and well tested tool for managing and deploying applications. But when we work on the embedded systems, storage optimization is very critical due to the limited resources available in the embedded systems. In order to get the most out of Docker for the embedded systems, it is very important to follow best practices for storage optimization. Below are some of the best practices for docker storage in embedded systems:
Designing storage-optimized Docker images: We should have no doubt that the Docker images are the foundation of the Docker containers. The images define what will be installed in the container and what configuration the container will have. When designing Docker images for the embedded systems, it is important to keep in mind the limited storage resources available. We should create the images that include only the necessary files & libraries. All the unnecessary dependencies & libraries should be removed from the images. Using multi-stage builds and minimal base images can also help us in reducing the final image size. In this way we can optimize the Docker storage in embedded system
Utilizing Docker volumes for persistent storage: Docker volumes provide persistent storage for containers. When data needs to be stored across container restarts or across multiple containers the docker volumes are the best option just like we have EEPROMs in monolithic embedded systems. While using Docker volumes in embedded systems, it is also important to use the most space-efficient storage driver available. we have already discussed the available storage drives in the Docker. Additionally, when volumes are no longer needed, they should be removed to free up storage space.
Implementing storage-efficient Docker networks: Docker networks are used to allow containers to communicate with each other. When using Docker networks in the Embedded systems, it is important to choose the most space-efficient networking driver available. We must have to remove all the unnecessary networks. Removing these kinds of unnecessary things will free up storage space.
Leveraging Docker storage APIs for custom solutions: Docker provides a variety of storage APIs/commands that can be used to create custom solutions for specific use cases. For example, the Docker Storage command can be used to create a custom storage driver that is optimized for a particular hardware configuration. Additionally, the Docker Volume commands can be used to create custom volume plugins that provide storage options beyond the built-in drivers.
Use Cases for Docker Storage in Embedded Systems
Docker storage is an essential component for managing applications in embedded systems, and it has a wide range of use cases in various industries. Here are some real-life examples of use of Docker storage in embedded systems:
Containerizing applications for IoT devices: IoT devices often have limited resources, and it can be challenging to manage and deploy the applications on these devices. Docker can help by containerizing applications, making them more lightweight and efficient. With Docker, IoT applications can be easily deployed and managed, and updates can be pushed out quickly and easily. Docker’s storage management features also make it easy to manage and store data in a secure and efficient manner.
Building cloud-native edge computing systems: Edge computing is becoming increasingly popular, and it involves processing data at the edge of the network, closer to where it is generated. Docker can help build cloud-native edge computing systems that are scalable, flexible, and efficient. By containerizing applications, developers can easily deploy and manage edge applications, and Docker’s storage management features ensure that data is stored efficiently and securely.
Developing cross-platform embedded applications: Developing embedded applications for different platforms can be challenging, but Docker can help simplify the process. By containerizing applications, developers can ensure that their applications will run consistently across different platforms, regardless of the underlying hardware and software configurations. Docker’s storage management features also make it easy to manage and store data in a consistent manner.
Using Docker for firmware updates and management: Firmware updates are an important part of maintaining embedded systems, but they can be challenging to manage. Docker can help simplify firmware updates by containerizing the firmware update process. By containerizing the update process, developers can ensure that updates are consistent and reliable across different platforms. Docker’s storage management features also make it easy to manage and store firmware updates and other critical system files. SO, Docker storage in Embedded system will add a cherry on the cake.
Custom system configurations: Docker storage can be used to create custom system configurations for embedded systems, allowing developers to tailor the system to specific hardware and software requirements.
Portable applications: Docker storage allows for the creation of portable applications that can be easily moved between different systems and environments. This is particularly useful for embedded systems that may need to be deployed in different locations or environments.
In conclusion, Docker storage in embedded systems can provides a powerful solution for managing storage. By utilizing Docker’s storage concepts, storage drivers, and APIs, developers can optimize storage usage, improve system performance, and create more secure and reliable embedded systems. By following best practices such as designing storage-optimized Docker images, utilizing Docker volumes for persistent storage, and leveraging Docker storage APIs for custom solutions, developers can ensure that their embedded systems are efficient, reliable, and secure. With real-life examples such as secure firmware updates, continuous integration and delivery, and portable applications, it’s clear that Docker storage has a wide range of applications in the world of embedded systems and is a valuable tool for any developer working in this field.
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