Container orchestration: Automate, scale, and optimize your apps effortlessly

Since the revolutionization of the concept by Docker in 2013, containers have become a mainstay in application development. Their speed and resource efficiency make them ideal for a DevOps environment as they allow developers to run software faster and more reliably, no matter where it is deployed. With containerization, it’s possible to move and scale several applications across clouds and data centers. 

However, this scalability can eventually become an operational challenge. In a scenario where an enterprise is tasked with efficiently running several containers carrying multiple applications, container orchestration becomes not just an option but a necessity. 

What is container orchestration?

Container orchestration is the automated process of managing, scaling, and maintaining containerized applications. Containers are executable units of software containing application code, libraries, and dependencies so that the application can be run anywhere. Container orchestration tools automate the management of several tasks that software teams encounter in a container’s lifecycle, including the following:

• Deployment
• Scaling and load balancing/traffic routing
• Networking
• Insights
• Provisioning
• Configuration and scheduling
• Allocation of resources
• Moving to physical hosts
• Service discovery
• Health monitoring 
• Cluster management

How does container orchestration work?

There are different methodologies that can be applied in container orchestration, depending on the tool of choice. Container orchestration tools typically communicate with YAML or JSON files that describe the configuration of the application. Configuration files guide the container orchestration tool on how and where to retrieve container images, create networking between containers, store log data, and mount storage volumes. 

The container orchestration tool also schedules the deployment of containers into clusters and automatically determines the most appropriate host for the container. After a host has been determined, the container orchestration tool manages the container’s lifecycle using predefined specifications provided in the container’s definition file. 

Container orchestration tools can be used in any environment that runs containers. Several platforms offer container orchestration support, including Kubernetes, Docker Swarm, Amazon Elastic Container Service (ECS), and Apache Mesos. 

Challenges and best practices in container orchestration

While container orchestration offers transformative benefits, it’s not without its challenges. Understanding these potential pitfalls and adopting best practices can help organizations maximize the value of their orchestration efforts.

Common challenges

1. Complexity in setup and operation
   Setting up container orchestration can be daunting, especially for teams new to the technology. Configuring clusters, managing dependencies, and defining orchestration policies often require significant expertise. The steep learning curve, particularly with tools like Kubernetes, can slow adoption and hinder productivity.
2. Security risks with containerized environments
   Containerized applications introduce unique security challenges, including vulnerabilities in container images, misconfigurations in orchestration platforms, and potential network exposure. Orchestrators need robust security measures to safeguard data and applications.
3. Vendor lock-in with proprietary solutions
   Organizations relying on proprietary orchestration tools or cloud-specific platforms may find it difficult to migrate workloads or integrate with other environments. This can limit flexibility and increase long-term costs.
4. Performance bottlenecks
   Resource contention, inefficient scaling policies, and poorly optimized configurations can lead to performance issues, impacting application reliability and user experience.

Best practices for successful container orchestration

1. Simplify and automate with CI/CD pipelines
   Automating workflows using Continuous Integration and Continuous Deployment (CI/CD) pipelines reduces manual intervention and ensures consistency in deployments. Tools like Jenkins or GitLab can integrate seamlessly with container orchestration platforms to streamline operations.
2. Proactively monitor and manage clusters
   Monitoring tools like LogicMonitor can be used to track container performance, resource usage, and application health. Proactive alerts and dashboards help identify and resolve issues before they impact users, ensuring reliability and uptime.
3. Prioritize security from the start
   Implement security best practices such as:
   • Regularly scanning container images for vulnerabilities.
   • Enforcing Role-Based Access Control (RBAC) to restrict permissions.
   • Configuring network policies to isolate containers and protect sensitive data. By building security into the orchestration process, organizations can mitigate risks and maintain compliance.
4. Start small and scale gradually
   Begin with a minimal setup to gain familiarity with orchestration tools. Focus on automating a few processes, then gradually expand the deployment to handle more complex workloads as the team’s expertise grows.
5. Optimize resource allocation
   Regularly review resource usage and scaling policies to ensure efficient operation. Use orchestration features like auto-scaling to adjust resources based on demand dynamically.
6. Choose flexible, open solutions
   To avoid vendor lock-in, prioritize tools like Kubernetes that support multi-cloud or hybrid deployments and integrate with a wide range of environments and services.

How does Kubernetes orchestration work?

Kubernetes is an open-source container orchestration platform that is considered the industry standard. The Google-backed solution allows developers and operators to deliver cloud services, either as Platform-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS). It’s a highly declarative solution, allowing developers to declare the desired state of their container environment through YAML files. Kubernetes then establishes and maintains that desired state.

The following are the main architecture components of Kubernetes:

Nodes

A node is a worker machine in Kubernetes. It may be virtual or physical, depending on the cluster. Nodes receive and perform tasks assigned from the Master Node. They also contain the necessary services to run pods. Each node comprises a kubelet, a container runtime, and a kube-proxy.

Master Node

This node controls all the worker nodes and originates all assigned tasks. It does this through the control pane, which is the orchestration layer that exposes the API and interfaces to define, deploy, and manage the lifecycles of containers.

Cluster

A cluster represents the master node and multiple worker nodes. Clusters combine these machines into a single unit to which containerized applications are deployed. The workload is then distributed to various nodes, making adjustments as nodes are added or removed.

Pods

Pods are the smallest deployable computing units that can be created and managed in Kubernetes. Each Pod represents a collection of containers packaged together and deployed to a node.

Deployments

A deployment provides declarative updates for Pods and ReplicaSets. It enables users to designate how many replicas of a Pod they want running simultaneously. 

How does Docker orchestration work?

Docker, also an open-source platform, provides a fully integrated container orchestration tool known as Docker Swarm. It can package and run applications as containers, locate container images from other hosts, and deploy containers. It is simpler and less extensile than Kubernetes, but Docker provides the option of integration with Kubernetes for organizations that want access to Kubernetes’ more extensive features.

The following are the main architectural components of Docker Swarm:

Swarm

A swarm is a cluster of Docker hosts that run in swarm mode and manage membership and delegation while also running swarm services.    

Node

A node is the docker engine instance included in a swarm. It can be either a manager node or a worker node. The manager node dispatches units of work called tasks to worker nodes. It’s also responsible for all orchestration and container management tasks like maintaining cluster state and service scheduling. Worker nodes receive and execute tasks.

Services and Tasks

A service is the definition of a task that needs to be executed on the nodes. It defines which container images to use and which commands to execute inside running containers.

A task carries a container alongside the commands to run inside the container. Once a task is assigned to a node, it cannot move to another node.

How does container orchestration work with other Platforms?

Although Docker and Kubernetes are leading the pack when it comes to container orchestration, other platforms are capitalizing on their open-source software to provide competition.

Red Hat OpenShift is an open-source enterprise-grade hybrid platform that provides Kubernetes functionalities to companies that need managed container orchestration. Its framework is built on a Linux OS that allows users to automate the lifecycles of their containers. 

Google Kubernetes Engine is powered by Kubernetes and enables users to easily deploy, manage, and scale Docker containers on Google Cloud.

Other platforms like Apache Mesos and Amazon ECS have developed their own container tools that allow users to run containers while ensuring security and high scalability.

Tool comparisons: Finding the right fit for your needs

When choosing the best container orchestration tool for an organization, several factors have to be taken into consideration. These factors vary across different tools. With a tool like Mesos, for instance, the software team’s technical experience must be considered as it is more complex than simple tools like Swarm. Organizations also have to consider the number of containers to be deployed, as well as application development speed and scaling requirements. 

With the right tools and proper resource management, container orchestration can be a valuable approach for organizations looking to achieve improved productivity and scalability.

Below is a comparison of the most popular tools in the container orchestration space, highlighting their key features and ideal use cases.

Tool	Scalability	Learning Curve	Supported Environments	Key Integrations	Best For
Kubernetes	Excellent for large, complex setups	Steep, requires expertise	On-premises, cloud (AWS, GCP, Azure)	CI/CD pipelines, monitoring tools, Istio	Enterprises requiring robust orchestration for multi-cloud or hybrid environments.
Docker Swarm	Moderate, ideal for small clusters	Low, easy for Docker users	On-premises, cloud	Docker ecosystem, Kubernetes (optional integration)	Small to medium teams seeking straightforward orchestration within the Docker platform.
Amazon ECS	Highly scalable within AWS ecosystem	Moderate, AWS-specific knowledge	AWS (native service)	AWS services (EKS, CloudWatch, IAM)	Businesses already leveraging AWS services for containerized applications.
Red Hat OpenShift	Enterprise-grade, highly scalable	Moderate, depends on Kubernetes base	Hybrid environments, Linux-based on-premise/cloud	OpenShift tools, Kubernetes integrations	Enterprises needing managed Kubernetes with robust security and enterprise-grade features.
Apache Mesos	Extremely scalable for large systems	High, requires advanced expertise	On-premises, private cloud	Marathon, custom integrations	Advanced users managing diverse workloads beyond containers, such as big data and microservices.

Examples of container orchestration

Container orchestration provides a number of benefits for organizations, but what do those benefits look like in real-world work situations? We included a couple of common orchestration examples below:

First, consider a large e-commerce platform that experiences heavy traffic during the holiday season. In the past, that platform would have to manually provision additional servers to handle the increased holiday load, which is a time-consuming and error-prone process. With container orchestration, the platform can use an auto-scaling feature that automatically provisions additional containers as traffic increases and scales back down when traffic decreases. That way, increased traffic for the holiday rush can die down in January once everyone buys, returns, and exchanges their items.

Second, consider a company that has a website, a mobile app, and a back-end processing system that all runs on different servers in different environments. In the past, managing these different applications and environments would require much manual effort and coordination. With container orchestration, the company can use a single platform to manage all of its containers and environments, allowing it to easily deploy, manage, and scale its applications across different environments. This allows the company to adopt new technologies more easily and streamline its development process.

Monitor your containers with LogicMonitor today

Container orchestration is a critical component of modern application development, enabling teams to efficiently manage, scale, and secure containerized environments. By addressing the challenges of complexity, security, and resource management, and leveraging best practices like CI/CD pipelines and proactive monitoring, organizations can maximize the benefits of container orchestration while minimizing operational overhead.

To fully realize the potential of container orchestration, having a reliable monitoring solution is essential. LogicMonitor offers scalable, dynamic monitoring for ephemeral containerized resources alongside your hybrid cloud infrastructure. With LogicMonitor, you gain visibility into your Kubernetes and Docker applications through a single, unified platform that automatically adapts to your container resource changes.