Container Scheduler

March 9, 2016

by Matt Conran Blog

Container Scheduler

In modern application development and deployment, containerization has gained immense popularity. Containers allow developers to package their applications and dependencies into portable and isolated environments, making them easily deployable across different systems. However, as the number of containers grows, managing and orchestrating them becomes complex. This is where container schedulers come into play.

A container scheduler is a crucial component of container orchestration platforms. Its primary role is to manage the allocation and execution of containers across a cluster of machines or nodes. By efficiently distributing workloads, container schedulers ensure optimal resource utilization, high availability, and scalability.

Container schedulers serve as a crucial component in container orchestration frameworks, such as Kubernetes. They act as intelligent managers, overseeing the deployment and allocation of containers across a cluster of machines. By automating the scheduling process, container schedulers enable efficient resource utilization and workload distribution.

Enhanced Resource Utilization: Container schedulers optimize resource allocation by intelligently distributing containers based on available resources and workload requirements. This leads to better utilization of computing power, minimizing resource wastage.

Scalability and Load Balancing: Container schedulers enable horizontal scaling, allowing applications to seamlessly handle increased traffic and workload. With the ability to automatically scale up or down based on demand, container schedulers ensure optimal performance and prevent system overload.

High Availability: By distributing containers across multiple nodes, container schedulers enhance fault tolerance and ensure high availability. If one node fails, the scheduler automatically redirects containers to other healthy nodes, minimizing downtime and maximizing system reliability.

Microservices Architecture: Container schedulers are particularly beneficial in microservices-based applications. They enable efficient deployment, scaling, and management of individual microservices, facilitating agility and flexibility in development.

Cloud-Native Applications: Container schedulers are a fundamental component of cloud-native application development. They provide the necessary framework for deploying and managing containerized applications in dynamic and distributed environments.

DevOps and Continuous Deployment: Container schedulers play a vital role in enabling DevOps practices and continuous deployment. They automate the deployment process, allowing developers to focus on writing code while ensuring smooth and efficient application delivery.

Conclusion: Container schedulers have revolutionized the way organizations develop, deploy, and manage their applications. By optimizing resource utilization, enabling scalability, and enhancing availability, container schedulers empower businesses to build robust and efficient software systems. As technology continues to evolve, container schedulers will remain a critical tool in streamlining efficiency and scaling applications in the dynamic digital landscape.

Matt Conran

Highlights: Container Scheduler

Virtualization

Virtualization systems, such as VMware or KVM, allow you to run Linux kernels and operating systems on top of a virtualized layer, commonly called a hypervisor. On top of a hardware virtualization layer, each VM hosts its operating system kernel in a separate memory space, providing extreme isolation between workloads. A container is fundamentally different since it shares only one kernel and achieves all workload isolation within it. Operating systems are virtualized in this way.

Docker and OCI Images

There is almost no place today that does not use containers. Many production systems, including Kubernetes and most “serverless” cloud technologies, rely on Docker and OCI images as the packaging format for a significant and growing amount of software delivered into production environments.

Container Scheduling

Often, we want our containers to restart if they exit. Containers can come and go quickly, but some are very short-lived. You expect production applications, for example, to be constantly running after you tell them to do so. Schedulers may handle this for you if your system is more complex.

Docker’s cgroup-based CPU share constraints can have unexpected results, unlike VMs. Like the excellent command, they are relative limits, not hard limits. Suppose a container is limited to half the CPU share on a system that is not very busy. As the CPU is not busy, the CPU share limit would only have a limited effect since the scheduler pool is not competitive. Suddenly, the constraint will affect the first container when a second container using a lot of CPU is deployed to the same system. When allocating resources and constraining containers, keep this in mind.

Scheduling with Docker Swarm

Container scheduling lies at the heart of efficient resource allocation in containerized environments. It involves intelligently assigning containers to available resources based on various factors such as resource availability, load balancing, and fault tolerance. Docker Swarm simplifies this process by providing a built-in orchestration layer that automates container scheduling, making it seamless and hassle-free.

Scheduling with Apache Mesos

Apache Mesos is an open-source cluster manager designed to abstract and pool computing resources across data centers or cloud environments. Acting as a distributed systems kernel, Mesos enables efficient utilization of resources by offering a unified API for managing diverse workloads. With its modular architecture, Mesos ensures flexibility and scalability, making it a preferred choice for large-scale deployments.

Scheduling with Kubernetes

Kubernetes employs a sophisticated scheduling system to assign containers to appropriate nodes in a cluster. The scheduling process considers various factors such as resource requirements, node capacity, affinity, anti-affinity, and custom constraints. Through intelligent scheduling algorithms, Kubernetes optimizes resource allocation, load balancing, and fault tolerance.

Traditional Application

Applications started with single server deployments and no need for a container scheduler. However, this was an inefficient deployment model, yet it was widely adopted. Applications mapped to specific hardware do not scale. The landscape changed, and the application stack was divided into several tiers. Decoupling the application to a loosely coupled system is a more efficient solution. Nowadays, the application is divided into different components and spread across the network with various systems, dependencies, and physical servers.

Example: OpenShift Networking

An example of this is with OpenShift networking. OpenShift is based on Kubernetes and borrows many of the Kubernetes constructs. For pre-information, you may find this post informative on Kubernetes and Kubernetes Security Best Practice

The Process of Decoupling

The world of application containerization drives the ability to decouple the application. As a result, there has been a massive increase in containerized application deployments and the need for a container scheduler. With all these changes, remember the need for new security concerns to be addressed with Docker container security.

The Kubernetes team conducts regular surveys on container usage, and their recent figures show an increase in all areas of development, testing, pre-production, and production. Currently, Google initiates about 2 billion containers per week. Most of Google’s apps/services, such as its search engine, Docs, and Gmail, are packaged as Linux containers.

Container Scheduler. Key Container Scheduler Discussion points:	Introduction to containerized technologies. The role of the scheduler. Discussion on the Kubernetes Orchestrator. Kubernetes POD and Labels.

For pre-information, you may find the following helpful

Back to basics: Container scheduler

With a container orchestration layer, we are marrying the container scheduler’s decisions on where to place a container with the primitives provided by lower layers. The container scheduler knows where containers “live,” and we can consider it the absolute source of truth concerning a container’s location.

So, a container scheduler’s primary task is to start containers on the most suitable host and connect them. It also has to manage failures by performing automatic fail-overs and be able to scale containers when there is too much data to process/compute for a single instance.

Key Features of Container Schedulers:

1. Resource Management: Container schedulers allocate appropriate resources to each container, considering factors such as CPU, memory, and storage requirements. This ensures that containers operate without resource contention, preventing performance degradation.

2. Scheduling Policies: Schedulers implement various scheduling policies to allocate containers based on priorities, constraints, and dependencies. They ensure containers are placed on suitable nodes that meet the required criteria, such as hardware capabilities or network proximity.

3. Scalability and Load Balancing: Container schedulers enable horizontal scalability by automatically scaling up or down the number of containers based on demand. They also distribute the workload evenly across nodes, preventing any single node from becoming overloaded.

4. High Availability: Schedulers monitor the health of containers and nodes, automatically rescheduling failed containers to healthy nodes. This ensures that applications remain available even in node failures or container crashes.

Popular Container Schedulers:

1. Kubernetes: Kubernetes is an open-source container orchestration platform with a powerful scheduler. It provides extensive features for managing and orchestrating containers, making it widely adopted in the industry.

2. Docker Swarm: Docker Swarm is another popular container scheduler provided by Docker. It simplifies container orchestration by leveraging Docker’s ease of use and integrates well with existing workflows.

3. Apache Mesos: Mesos is a distributed systems kernel that provides a framework for managing and scheduling containers and other workloads. It offers high scalability and fault tolerance, making it suitable for large-scale deployments.

Benefits of Container Schedulers:

1. Efficient Resource Utilization: Container schedulers optimize resource allocation, allowing organizations to maximize their infrastructure investments. By eliminating resource wastage, they reduce operational costs.

2. Improved Application Performance: Schedulers ensure containers have the necessary resources to operate at their best, preventing resource contention and bottlenecks.

3. Simplified Management: Container schedulers automate the deployment and management of containers, reducing manual effort and enabling faster application delivery.

4. Flexibility and Portability: With container schedulers, applications can be easily moved and deployed across different environments, whether on-premises, in the cloud, or hybrid setups. This flexibility allows organizations to adapt to changing business needs.

Containers – Raising the Abstraction Level

Container networking raises the abstraction level. The abstraction level was at a VM level, but with containers, the abstraction is moved up one layer. So, instead of virtual hardware, you have an idealized O/S stack.

Containers change the way applications are packaged. They allow application tiers to be packaged and isolated, so all dependencies are confined to individual islands and do not conflict with other stacks. Containers provide a simple way to package all application pieces into an easily deployable unit. The ability to create different units radically simplifies deployment.

It creates a predictable isolated stack with ALL userland dependencies. Each application is isolated from others, and dependencies are sealed in. Dependencies are the natural killer as they can slow down deployment lifecycles. Containers combat this and fundamentally change the operational landscape. Docker and Rocket are the main Linux application container stacks in production.

Containers don’t magically appear. They need assistance with where to go; this is the role of the container scheduler. The scheduler’s main job is to start the container on the correct host and connect it. In addition, the scheduler needs to monitor the containers and deal with container/host failures.

The schedulers are Docker Swarm, Google Kubernetes, and Apache Mesos. Docker Swarm is probably the easiest to start with, and it’s not attached to any cloud provider. The container sends several requirements to the cluster scheduler. For example, I have this amount of resources and want to run five copies of this software with this amount of CPU and disk space – now find me a place.

Kubernetes – Container scheduler

Hand on Kubernetes. Kubernetes is an open-source cluster solution for containerized environments. It aims to make deploying microservice-based applications easy by using the concepts of PODS and LABELS to group containers into logical units. All containers run inside a POD.

PODS are the main difference between Kubernetes and other scheduling solutions. Initially, Kubernetes focused on continuously running stateless and “cloud native” stateful applications. In the coming future, it is said to support other workload types.

Kubernetes Networking 101

Kubernetes is not just interested in the deployment phase but works across the entire operational model—scheduling, updating, maintenance, and scaling. Unlike orchestration systems, it actively ensures the state matches the user’s requirements. Kubernetes is also involved in monitoring and healing if something goes wrong.

The team at Google refers to this as a flight control mechanism. It provides the cluster and the decoupling between it. The application containers view the world as a sea of computing, an entirely homogenous (similar kind) cluster. Every machine you create in your fleet looks the same. The application is completely decoupled from low-level computing.

The user does not need to care about physical placement anymore. The unit of work has changed and become a service. The administrator only needs to care about services, such as the amount of CPU, RAM, and disk space. The unit of work presented is now at a service level. The physical location is abstracted, all taken care of by the Kubernetes components.

This does not mean that the application components can be spread randomly. For example, some application components require the same host. However, selecting the hosts is no longer the user’s job. Kubernetes provides an abstracted layer over the infrastructure, allowing this type of management.

The scheduling of containers is on a homogenous pool of resources. The VM disappears, and you think about resources such as CPU and RAM. Everything else, like location, disappears.

Kubernetes pod and label

The main building blocks for Kubernetes clusters are PODS and LABELS. So, the first step is to create a cluster, and once complete, you can proceed to PODS and other services. The diagram below shows the creation of a Kubernetes cluster. It consists of a 3-node instance created in us-east1-b.

A POD is a collection of applications running within a shared context. Containers within a POD share fate and some resources, such as volumes and IP addresses. They are usually installed on the same host. When you create a POD, you should also make a kubernetes replication controller.

It monitors POD health and starts new PODS as required. Most PODS should be built with a replication controller, but it may not be needed if your POD is short-lived and is writing non-persistent data that won’t survive a restart. There are two types of PODS a) single container and b) Multi-container.

The following diagram displays the full details of a POD named example-tglxm. It has a label run=example located in the default network (namespace).

A POD may contain either a single container with a private volume or a group with a shared volume. If a container fails within a POD, the Kubelet automatically restarts it. However, if an entire POD or host fails, the replication controller needs to restart it.

Replication to another host must be specifically configured. It is not automatic by default. The Kubernetes replication controller dynamically resizes things and ensures that the required number of PODS and containers are running. If there are too many, it will kill some; if not enough, it will start some more.

Kubernetes operates with the concept of LABELS – a key-value pair attached to objects, such as a POD. A label is a tag that can be used to query against. Kubernetes is an abstraction, and you can query whatever item you want using a label in an entire cluster.

For example, you can select all frontend containers with a label called “frontend”; it then selects all front ends. The cluster can be anywhere. Labels can also be building blocks for other services, such as port mappings. For example, a POD whose labels match a specific service selector is accessible through the defined service’s port.

Summary: Container Scheduler

Container scheduling plays a crucial role in modern software development and deployment. It efficiently manages and allocates resources to containers, ensuring optimal performance and minimizing downtime. In this blog post, we explored the world of container scheduling, its importance, key strategies, and popular tools used in the industry.

Understanding Container Scheduling

Container scheduling involves orchestrating the deployment and management of containers across a cluster of machines or nodes. It ensures that containers run on the most suitable resources while considering resource utilization, scalability, and fault tolerance factors. By intelligently distributing workloads, container scheduling helps achieve high availability and efficient resource allocation.

Key Strategies for Container Scheduling

1. Load Balancing: Load balancing evenly distributes container workloads across available resources, preventing any single node from being overwhelmed. Popular load-balancing algorithms include round-robin and least connections.

2. Resource Constraints: Container schedulers consider resource constraints such as CPU, memory, and disk space when allocating containers. By understanding the resource requirements of each container, schedulers can make informed decisions to avoid resource bottlenecks.

3. Affinity and Anti-Affinity: Schedulers can leverage affinity rules to ensure containers with specific requirements are placed together on the same node. Conversely, anti-affinity rules can separate containers that may interfere with each other.

Popular Container Scheduling Tools

1. Kubernetes: Kubernetes is a leading container orchestration platform with robust scheduling capabilities. It offers advanced features like auto-scaling, rolling updates, and cluster workload distribution.

2. Docker Swarm: Docker Swarm is a native clustering and scheduling tool for Docker containers. It simplifies the management of containerized applications and provides fault tolerance and high availability.

3. Apache Mesos: Mesos is a flexible distributed systems kernel that supports multiple container orchestration frameworks. It provides fine-grained resource allocation and efficient scheduling across large-scale clusters.

Conclusion:

Container scheduling is critical to modern software deployment, enabling efficient resource utilization and improved performance. Organizations can optimize their containerized applications by leveraging strategies like load balancing, resource constraints, and affinity rules. Furthermore, popular tools like Kubernetes, Docker Swarm, and Apache Mesos offer powerful scheduling capabilities to manage container deployments effectively. Embracing container scheduling technologies empowers businesses to scale their applications seamlessly and deliver high-quality services to end-users.

Container Based Virtualization

February 24, 2016

by Matt Conran Blog

Container Based Virtualization

Container-based virtualization, or containerization, is a popular technology revolutionizing how we deploy and manage applications. In this blog post, we will explore what container-based virtualization is, why it is gaining traction, and how it differs from traditional virtualization techniques.

Container-based virtualization is a lightweight alternative to traditional methods such as hypervisor-based virtualization. Unlike virtual machines (VMs), which require a separate operating system (OS) instance for each application, containers share the host OS. This means containers can be more efficient regarding resource utilization and faster to start and stop.

Container-based virtualization, also known as operating system-level virtualization, is a lightweight virtualization method that allows multiple isolated user-space instances, known as containers, to run on a single host operating system. Unlike traditional virtualization techniques, which rely on hypervisors and full-fledged guest operating systems, containerization leverages the host operating system's kernel to provide resource isolation and process separation. This streamlined approach eliminates the need for redundant operating system installations, resulting in improved performance and efficiency.

Enhanced Portability: Containers encapsulate all the dependencies required to run an application, making them highly portable across different environments. Developers can package their applications with all the necessary libraries, frameworks, and configurations, ensuring consistent behavior regardless of the underlying infrastructure.

Scalability and Resource Efficiency: Containers enable efficient resource utilization by sharing the host's operating system and kernel. With their lightweight nature, containers can be rapidly provisioned, scaled up or down, and migrated across hosts, ensuring optimal resource allocation and responsiveness.

Isolation and Security: Containers provide isolation at the process level, ensuring that each application runs in its own isolated environment. This isolation prevents interference and minimizes security risks, making container-based virtualization an attractive choice for multi-tenant environments and cloud-native applications.

Container-based virtualization has gained significant traction across various industries and use cases. Some notable examples include:

Microservices Architecture: Containerization seamlessly aligns with the principles of microservices, allowing applications to be broken down into smaller, independent services. Each microservice can be encapsulated within its own container, enabling rapid development, deployment, and scaling.

DevOps and Continuous Integration/Continuous Deployment (CI/CD): Containers play a crucial role in modern DevOps practices, streamlining the software development lifecycle. With container-based virtualization, developers can easily package, test, and deploy applications across different environments, ensuring consistency and reducing deployment complexities.

Hybrid and Multi-Cloud Environments: Containers facilitate hybrid and multi-cloud strategies by abstracting away the underlying infrastructure dependencies. Applications can be packaged as containers and seamlessly deployed across different cloud providers or on-premises environments, enabling flexibility and avoiding vendor lock-in.

Matt Conran

Traditional Deployment Models

So, how do containers facilitate virtualization? Firstly, the traditional application deployment was based on a single-server approach. As a result, one application was installed per physical server, wasting server resources, and components such as RAM and CPU were never fully utilized. There was also considerable vendor lock-in, making moving applications from one hardware vendor to another hard.

Then, the world of hypervisor-based virtualization was introduced, and the concept of a virtual machine (VM) was born. Soon after, we had container-based applications. Container-based virtualization introduced container networking, and new principles arose for security around containers, specifically, Docker container security.

Introducing hypervisors

We still deployed physical servers but introduced hypervisors on the physical host, enabling the installation of multiple VMs on a single server. Each VM is isolated from its operating system. Hypervisor-based virtualization introduced better resource pooling as one physical server could now be divided into multiple VMs, each hosting a different application type. This was years better than single-server deployments and opened the doors to open networking.

The VM deployment approach increased agility and scalability, as applications within a VM are scaled by simply spinning up more VMs on any physical host. While hypervisor-based virtualization was a step in the right direction, a guest operating system for each application is pretty intensive. Each VM requires RAM, CPU, storage, and an entire guest OS, all-consuming resources.

Introducing Virtualization

Another advantage of virtualization is the ability to isolate applications or services. Each virtual machine operates independently, with its resources and configurations. This enhances security and stability, as issues in one virtual machine do not affect others. It also allows for easy testing and development, as virtual machines can be quickly created and discarded.

Virtualization also offers improved disaster recovery and business continuity. By encapsulating the entire virtual machine, including its operating system, applications, and data, into a single file, organizations can quickly back up, replicate, and restore virtual machines. This ensures that critical systems and data are protected and can rapidly recover during a failure or disaster.

Furthermore, virtualization enables workload balancing and dynamic resource allocation. Virtual machines can be dynamically migrated between physical servers to optimize resource utilization and performance. This allows for better utilization of computing resources and the ability to respond to changing workload demands.

Related: You may find the following helpful post before proceeding to how containers facilitate virtualization.

Container Virtualization. Key Container Based Virtualization Discussion points:	Introduction to containerized technologies. The role of container based applications. Discussion on container networking and Linux kernel. A final note on microsegmentation.

Back to Basics: Containers and Container Virtualization

The Traditional World

Before we address how containers facilitate virtualization, let’s address the basics. In the past, we could solely run one application per server. However, the open-systems world of Windows and Linux didn’t have the technologies to safely and securely run multiple applications on the same server.

So, every time we needed a new application, we would buy a new server. We had the virtual machine (VM) to solve the waste of resources. With the VM, we had a technology that permitted us to safely and securely run applications on a single server. Unfortunately, the VM model also has additional challenges.

Migrating VMs

For example, VMs are slow to boot, and portability isn’t great — migrating and moving VM workloads between hypervisors and cloud platforms is more complicated than it needs to be. All of which drove the need for a new technology of containers with container virtualization.

How do containers facilitate virtualization? So, we needed a lightweight tool without losing the scalability and agility benefits of the VM-based application approach. The lightweight tool is container-based virtualization, and Docker acts at the forefront. The container offers a similar capability to that of object-oriented programming. They let you build composable modular building blocks, making it easier to design distributed systems.

1st Lab Guide on Container Networking

In the following example, we have one Docker host. We can list the available networks for these Docker hosts with the command docker network ls. These are not WAN or VPN networks; they are only Docker networks.

Docker networks are virtual networks that allow containers to communicate with each other and the outside world. They provide isolation, security, and flexibility to manage network traffic flow between containers. By default, when you create a new Docker container, it is connected to a default bridge network, which allows communication with other containers on the same host.

Notice the subnets assigned of 172.17.0.0/16. So, the default gateway ( exit point) is set to the docker0 bridge.

Types of Docker Networks:

Docker offers various types of networks, each serving a specific purpose:

1. Bridge Network:

The bridge network is the default network that enables communication between containers on the same host. Containers connected to the bridge network can communicate using IP addresses or container names. It provides a simple way to connect containers without exposing them to the outside world.

2. Host Network:

In the host network mode, a container shares the network stack with the host, using its network interface directly. This mode provides maximum network performance as no network address translation (NAT) is involved. However, it also means the container is directly exposed to the host’s network, potentially introducing security risks.

3. Overlay Network:

The overlay network allows containers to communicate across multiple Docker hosts, even in different physical or virtual networks. It achieves this by encapsulating network packets and routing them to the appropriate destination. Overlay networks are essential for creating distributed and scalable applications.

4. Macvlan Network:

The Macvlan network mode allows containers to have MAC addresses and appear as separate devices. This mode is useful when assigning IP addresses to containers and making them accessible from the external network. It is commonly used when containers must be treated as physical devices.

5. None Network:

The non-network mode isolates a container from all networking. It effectively disables all networking capabilities and prevents the container from communicating with other containers or the outside world. This mode is typically used when networking is not required or desired.

A key point: Lab Guide on Container Networking

You can attach as many containers as you like to a bridge. They will be assigned IP addresses within the same subnet, meaning they can communicate by default. You can have a container with two Ethernet interfaces ( virtual interfaces ) connected to two different bridges on the same host and have connectivity to two networks simultaneously.

Also, remember that the scope is local when you are doing this, and even if the docker hosts are on the same underlying network but with different hosts, they won’t have IP reachability. In this case, you may need a VXLAN overlay network to connect containers on different docker hosts.

inspecting container networks — Diagram: Inspecting container networks

Container-based Virtualization

One critical benefit of container-based virtualization is its portability. Containers encapsulate the application and all its dependencies, allowing it to run consistently across different environments, from development to production. This portability eliminates the “it works on my machine” problem and makes it easier to maintain and scale applications.

Scalability

Another advantage of containerization is its scalability. Containers can be easily replicated and distributed across multiple hosts, making it straightforward to scale applications horizontally. Furthermore, container orchestration platforms, like Kubernetes, provide automated management and scaling of containers, simplifying the deployment and management of complex applications.

Security

Security is crucial to any virtualization technology, and container-based virtualization is no exception. Containers provide isolation between applications, preventing them from interfering with each other. However, it is essential to note that containers share the same kernel as the host OS, which means a compromised container can potentially impact other containers. Proper security measures, such as regular updates and vulnerability scanning, are essential to ensure the security of containerized applications.

Tooling

Container-based virtualization also offers various tools and platforms for application development and deployment. Docker, for example, is a popular containerization platform that provides a user-friendly interface for building, running, and managing containers. It simplifies container image creation and enables developers to package their applications and dependencies.

Applications of Container-Based Virtualization:

1. DevOps and Continuous Integration/Continuous Deployment (CI/CD): Containerization enables developers to package applications, libraries, and configurations into portable and reproducible containers. This simplifies the deployment process and ensures consistency across different environments, facilitating faster software delivery.

2. Microservices Architecture: Container-based virtualization aligns well with the microservices architectural pattern. Organizations can develop, deploy, and scale each service independently using containers by breaking down complex applications into more minor, loosely coupled services. This approach enhances modularity, scalability, and fault tolerance.

3. Hybrid Cloud and Multi-Cloud Environments: Containers provide a unified platform for deploying applications across hybrid and multi-cloud environments. With container orchestration tools, organizations can leverage the benefits of multiple cloud providers while ensuring consistent deployment and management practices.

Application Landscape Changes

The application landscape has changed from a monolithic design to a design consisting of microservices. Today, applications are constantly developed. Patches usually patch only certain parts of the application, and the entire application is built from loosely coupled components instead of existing tightly coupled ones.

The entire application stack is broken into components and spread over multiple servers and locations, all requiring cross-communication.

For example, users connect to a presentation layer, the presentation layer then connects to some shopping cart, and the shopping cart connects to a catalog library. These components are potentially stored on different servers, maybe different data centers.

The application is built from several small parts, known as microservices. Each component or microservice can now be put into a lightweight container—a scaled-down VM.

container based virtualization — Diagram: Container based virtualization.

How do containers facilitate virtualization?

Container-Based Applications

Now, we have complex distributed software stacks based on microservices. Its base consists of loosely coupled components that may change and software that runs on various hardware, including test machines, in-house clusters, cloud deployments, etc. The web front end may include the following:

Ruby + Rail.
API endpoints with Python 2.7.
Stack website with Nginx.
A variety of databases.

We have a very complex stack on top of various hardware devices. While the traditional monolithic application will likely remain for some time, containers still exhibit the use case to modernize the operational model for conventional stacks. Both monolithic and container-based applications can live together.

Container-based virtualization

The application’s complexity, scalability, and agility requirements have led us to the market of container-based virtualization. Container-based virtualization uses the host’s kernel to run multiple guest instances. Now, we can run multiple guest instances (containers), and each container will have its root file system, process, and network stack.

Containers allow you to package up an application with all its parts in an isolated environment. It is a complete abstraction and does not need to run dependencies on the hosts. Docker, a type of container (first based on Linux Containers but now powered by runC), separates the application from infrastructure using container technologies.

Similar to how VMs separate the operating system from bare metal, containers let you build a layer of isolation in software that reduces the burden of human communication and specific workflows. An excellent way to understand containers is to accept that they are not VMs—they are simple wrappers around a single Unix process. Containers contain everything they need to run (runtime, code, libraries, etc.).

Linux kernel namespaces

Isolation or variants of isolation have been around for a while. We have mount namespacing in 2.4 kernels and userspace namespacing in 3.8. These technologies allow the kernel to create partitions and isolate PIDs. Linux containers (Lxc) started in 2008, and Docker was introduced in Jan 2013, with a public release of 1.0 in 2014. We are now at version 1.9, which has some new networking enhancements.

Docker uses Linux kernel namespaces and control groups, providing an isolated workspace, which offers the starting grounds for the Docker security options. Namespaces offer an isolated workspace that we call a container. They help us fool the container.

We have PID for process isolation, MOUNT for storage isolation, and NET for network-level isolation. The Linux network subsystem has the correct information for additional Linux network information.

how do containers facilitate virtualization — Diagram: How do containers facilitate virtualization

Container based application: Container operations

Containers use schedulers. A scheduler starts containers on the correct host and then connects them. It also needs to manage container failover and handle container scalability when there is too much data to process for a single instance. Popular container schedulers include Docker Swarm, Apache Mesos, and Google Kubernetes.

The correct host is selected depending on the type of scheduler used. For example, Docker Swarm will have three strategies: spread, binpack, and random. Spread means node selection is based on the fewest containers, disregarding their states. Binpack selection is based on hosts with minimum resources, i.e., the most packed. Finally, random strategy selections are chosen randomly.

Containers are quick to start.

How do containers facilitate virtualization? First, they are quick. Starting a container is much faster than starting a VM—lightweight containers can be started in as little as 300ms. Initial tests on Docker revealed that a newly created container from an existing image takes up only 12 kilobytes of disk space.

A VM could take up thousands of megabytes. The container is lightweight as it references points to a layered filesystem image. Container deployment is also swift and network-efficient.

Fewer data needs to travel across the network and storage fabrics. Elastic applications that have frequent state changes can be built more efficiently. Both Docker and Linux containers fundamentally change application consumption.

As a side note, not all workloads are suitable for containers, and heavy loads like databases are put into VMs to support multi-cloud environments.

Docker networking

Docker networking is an essential aspect of containerization that allows containers to communicate with each other and external networks. In this document, we will explore the different networking options available in Docker and how they can facilitate seamless communication between containers.

By default, Docker provides three networking options: bridge, host, and none. The bridge network is the default network created when Docker is installed. It allows containers to communicate with each other using IP addresses. Containers within the same bridge network can communicate with each other directly without the need for port mapping.

As the name suggests, the host network allows containers to share the network namespace with the host system. This means that containers using the host network can directly access the host system’s network interfaces. This option is helpful for scenarios where containers must bind to specific network interfaces on the host.

On the other hand, the non-network option completely isolates the container from the network. Containers using the none network cannot communicate with other containers or external networks. This option is useful when running a container in complete isolation.

Creating custom networks

In addition to these default networking options, Docker also provides the ability to create custom networks. Custom networks allow containers to communicate with each other, even if they are not in the same network namespace. Custom networks can be made using the `docker network create` command, specifying the desired driver (bridge, overlay, macvlan, etc.) and any additional options.

One of the main benefits of using custom networks is the ability to define network-level access control. Docker provides the ability to define network policies using network labels. These labels can control which containers can communicate with each other and which ports are accessible.

Closing Points on Docker networking

Networking is very different in Docker than what we are used to. Networks are domains that interconnect sets of containers. So, if you give access to a network, you can access all containers. However, if you want external access to other networks or containers, you must specify rules and port mapping.

A driver backs every network, be it a bridge or overlay driver. These Docker-based drivers can be swapped out with any ecosystem driver. The team at Docker views them as pluggable batteries.

Docker utilizes the concept of scope—local (default) and Global. The local scope is a local network, and the global scope has visibility across the entire cluster. If your driver is a global scope driver, your network belongs to a global scope. A local scope driver corresponds to the local scope.

Containers and Microsegmentation

Microsegmentation is a security technique that divides a network into smaller, isolated segments, allowing organizations to create granular security policies. This approach provides enhanced control and visibility over network traffic, preventing lateral movement and limiting the impact of potential security breaches.

Microsegmentation offers organizations a proactive approach to network security, allowing them to create an environment more resilient to cyber threats. By implementing microsegmentation, organizations can enhance their security posture, minimize the risk of lateral movement, and protect their most critical assets. As the cyber threat landscape continues to evolve, microsegmentation is an effective strategy to safeguard network infrastructure in an increasingly interconnected world.

Docker and Micro-segmentation

Docker 0 is the default bridge. They have now extended into bundles of multiple networks, each with independent bridges. Different bridges cannot directly talk to each other. It is a private, isolated network offering micro-segmentation and multi-tenancy features.

The only way for them to communicate is via host namespace and port mapping, which is administratively controlled. Docker multi-host networking is a new feature in 1.9. A multi-host network comprises several docker hosts that form a cluster.

Several containers in each host form the cluster by pointing to the same KV (example -zookeeper) store. The KV store that you point to defines your cluster. Multi-host networking enables the creation of different topologies and lets the container belong to several networks. The KV store may also be another container, allowing you to stay in a 100% container world.

Final points on container-based virtualization

In recent years, container-based virtualization has become famous for deploying and managing applications. Unlike traditional virtualization, which relies on hypervisors to run multiple virtual machines on a single physical server, container-based virtualization leverages lightweight, isolated containers to run applications.

So, what exactly is container-based virtualization, and why is it gaining traction in the technology industry? In this blog post, we will explore the concept of container-based virtualization, its benefits, and how it differs from traditional virtualization.

Operating system-level virtualization

Container-based virtualization, also known as operating system-level virtualization, is a form of virtualization that allows multiple containers to run on a single operating system kernel. Each container is isolated from the others, ensuring that applications and their dependencies are encapsulated within their runtime environment. This isolation eliminates application conflicts and provides a consistent environment across deployment platforms.

Critical advantages of container virtualization

One of the critical advantages of container-based virtualization is its lightweight nature. Containers are designed to be portable and efficient, allowing for rapid deployment and scaling of applications. Unlike virtual machines, which require an entire operating system to run, containers share the host operating system kernel, reducing resource overhead and improving performance.

Another benefit of container-based virtualization is its ability to facilitate microservices architecture. By breaking down applications into more minor, independent services, containers enable developers to build and deploy applications more efficiently. Each microservice can be encapsulated within its container, making it easier to manage and update without impacting other application parts.

Greater flexibility and scalability

Moreover, container-based virtualization offers greater flexibility and scalability. Containers can be easily replicated and distributed across hosts, allowing for seamless horizontal scaling. This ability to scale quickly and efficiently makes container-based virtualization ideal for modern, dynamic environments where applications must adapt to changing demands.

Container virtualization is not a complete replacement.

It’s important to note that container-based virtualization is not a replacement for traditional virtualization. Instead, it complements it. While traditional virtualization is well-suited for running multiple operating systems on a single physical server, container-based virtualization is focused on maximizing resource utilization within a single operating system.

In conclusion, container-based virtualization has revolutionized application deployment and management. Its lightweight nature, isolation capabilities, and scalability make it a compelling choice for modern software development and deployment. As technology continues to evolve, container-based virtualization will likely play a significant role in shaping the future of application deployment.

Container-based virtualization has transformed how we develop, deploy, and manage applications. Its lightweight nature, scalability, portability, and isolation capabilities make it an attractive choice for modern software development. By adopting containerization, organizations can achieve greater efficiency, agility, and cost savings in their software development and deployment processes. As container technologies continue to evolve, we can expect even more exciting possibilities in virtualization.

Summary: Container Based Virtualization

In recent years, container-based virtualization has emerged as a game-changer in technology. This innovative approach offers numerous advantages over traditional virtualization methods, providing enhanced flexibility, scalability, and efficiency. This blog post delved into container-based virtualization, exploring its key concepts, benefits, and real-world applications.

Understanding Container-Based Virtualization

Container-based virtualization, or operating system-level virtualization, is a lightweight alternative to traditional hypervisor-based virtualization. Unlike the latter, where each virtual machine runs on a separate operating system, containerization allows multiple containers to share the same OS kernel. This approach eliminates redundant OS installations, resulting in a more efficient and resource-friendly system.

Benefits of Container-Based Virtualization

2.1 Enhanced Performance and Efficiency

Containers are lightweight and have minimal overhead, enabling faster deployment and startup times than traditional virtual machines. Additionally, the shared kernel architecture reduces resource consumption, allowing for higher density and better utilization of hardware resources.

2.2 Improved Scalability and Portability

Containers are highly scalable, allowing applications to be easily replicated and deployed across various environments. With container orchestration platforms like Kubernetes, organizations can effortlessly manage and scale their containerized applications, ensuring seamless operations even during periods of high demand.

2.3 Isolation and Security

Containers provide isolation between applications and the host operating system, enhancing security and reducing the risk of malicious attacks. Each container operates within its own isolated environment, preventing interference from other containers and mitigating potential vulnerabilities.

Section 3: Real-World Applications

3.1 Microservices Architecture

Container-based virtualization aligns perfectly with the microservices architectural pattern. By breaking down applications into more minor, decoupled services, organizations can leverage the agility and scalability containers offer. Each microservice can be encapsulated within its container, enabling independent development, deployment, and scaling.

3.2 DevOps and Continuous Integration/Continuous Deployment (CI/CD)

Containerization has become a cornerstone of modern DevOps practices. By packaging applications and their dependencies into containers, development teams can ensure consistent and reproducible environments across the entire software development lifecycle. This facilitates seamless integration, testing, and deployment processes, leading to faster time-to-market and improved collaboration between development and operations teams.

Conclusion:

Container-based virtualization has revolutionized how we build, deploy, and manage applications. Its lightweight nature, scalability, and efficient resource utilization make it an ideal choice for modern software development and deployment. As organizations continue to embrace digital transformation, containerization will undoubtedly play a crucial role in shaping the future of technology.

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