Managing modern applications isn’t just about how you architect them. It’s about ensuring they run reliably across environments, scale efficiently, and recover quickly from unexpected disruptions.
Managing modern applications requires container orchestration. Read on to learn what container orchestration is, why it’s so important for modern application deployments, about the most popular container orchestration tools, and how Portworx enhances these tools with leading container data management capabilities.
Related Reading: Scale Kubernetes Storage On-Demand with Portworx
Introduction to Container Orchestration
What is Container Orchestration?
Container orchestration, including Kubernetes orchestration, automates container deployment, scaling, networking, and management. A container bundles an application with everything it needs to run, such as dependencies, libraries, and configuration files. Orchestration ensures these containers work harmoniously no matter where they’re deployed, distributing workloads across environments and scaling to meet demand.
Why Is Container Orchestration Important?
Manual container management can become a logistical nightmare as the number of containers grows. Container orchestration automates processes like scaling, load balancing, and self-healing (the ability to detect and resolve failures within a containerized application). It ensures applications run smoothly across distributed systems — on-premises, in the cloud, and in hybrid- and multi- cloud environments.
Key Concepts in Container Orchestration
Containers vs. Virtual Machines
Containers are a form of lightweight virtualization that share the host system’s operating system kernel and resources while isolating applications and processes. Their portability means they can be distributed via container registries and run on various hosts with compatible container runtimes, regardless of the underlying base image.
Containers are also efficient — they can start in seconds, avoiding the lengthy boot times associated with traditional operating systems. This portability and efficiency make containers ideal for microservices and modern application architectures. However, they’re increasingly capable of supporting a range of use cases, including those with legacy, monolithic architectures.
Virtual machines (VMs) are generally more resource-intensive; each instance runs a full operating system. This difference in resource usage often makes VMs more suitable for applications that require strict isolation or have hardware-level dependencies.
Key Components of a Container Orchestration System
Every orchestration platform includes:
- Schedulers : Decide where containers run based on available resources.
- Load balancers: Distribute traffic to prevent bottlenecks.
- Monitoring tools: Track container performance and health.
- Networking solutions: Ensure communication between containers.
Together, these components create an integrated system capable of scaling applications, recovering from failures, and maintaining performance with minimal manual oversight.
Popular Container Orchestration Tools
Kubernetes
The most popular container orchestration platform, Kubernetes (“K8s”), excels in managing large-scale containerized applications. Its modular architecture and ecosystem make it the go-to choice for enterprises, but smaller teams can still benefit from its scalability and development consistency. It features:
- Declarative management: Users define the desired state of their applications, and Kubernetes automatically works to maintain it.
- Autoscaling: Adjusts the number of running container instances based on resource usage or custom metrics.
- Self-healing: Automatically restarts failed containers, replaces unresponsive pods, and reschedules workloads on healthy nodes.
- Service discovery: Ensures communication between containers through built-in DNS services.
- Extensibility: Supports custom plugins and integrations for organizations to tailor Kubernetes to their specific requirements.
Kubernetes is incredibly powerful, but its complexity can challenge teams new to the orchestrator. While it provides some key capabilities to support persistent storage, it lacks built-in solutions for cross-cluster data management and high availability for storage—a critical requirement for many analytics platforms and other stateful applications.
Portworx fills this gap by providing advanced storage capabilities such as dynamic provisioning, disaster recovery, and automated backups directly integrated into Kubernetes environments.
Red Hat OpenShift
Built on Kubernetes, OpenShift simplifies deployment with enhanced developer tools and a robust user interface. It features:
- Developer tools: A user-friendly interface, CI/CD pipelines, and pre-configured frameworks accelerate development.
- Security enhancements: Enforces stricter security policies, such as built-in monitoring for container vulnerabilities and role-based access control.
- Hybrid cloud capabilities: Simplifies deployment across on-premises, private, and public cloud environments.
- Managed services options: OpenShift offers fully managed solutions on public cloud providers such as Google Cloud, Microsoft Azure, and Amazon Web Services, for organizations that prefer to offload infrastructure management.
Portworx integrates with Red Hat OpenShift to provide Kubernetes-native container data management. By leveraging OpenShift’s container orchestration capabilities, Portworx enhances the platform with persistent storage, data backup, and automated storage provisioning.
SUSE Rancher
Rancher focuses on multi-cluster management, providing centralized control over Kubernetes clusters across environments. It features:
- Multi-cluster management: Allows administrators to manage multiple Kubernetes clusters from a single interface — on-premises or in the cloud.
- User management: Provides granular access control and role-based permissions for better governance.
- Simplified Kubernetes installation: Offers easy deployment and management of Kubernetes distributions, such as RKE and K3s.
- Integrated app catalog: A curated catalog of applications helps teams quickly deploy and scale workloads.
Through Rancher, users can deploy and manage Portworx alongside their Kubernetes clusters for consistent container data management across diverse infrastructures.
Managed Services
While Kubernetes, Red Hat OpenShift, and SUSE Rancher are popular self-managed solutions for container management, container orchestration solutions are also available from public cloud providers as a managed service. Popular options include:
- Amazon EKS: AWS’s managed Kubernetes service handles control plane management, patching, and upgrades. It integrates with AWS services like IAM for security and CloudWatch for monitoring.
- Azure AKS: Microsoft’s AKS simplifies Kubernetes deployment and integrates with Azure services like Active Directory and Log Analytics.
- Google GKE: A powerful Kubernetes service native to Google Cloud. It automates many aspects of Kubernetes management, including scaling, upgrades, and monitoring.
Kubernetes in Depth
Overview of Kubernetes Architecture
Let’s take a closer look at how Kubernetes is constructed. Kubernetes architecture comprises several key components that work together to manage containerized applications. Here’s an overview of common (but not all) components:
Pods, Nodes, and Clusters:
- pod: The smallest deployable unit, encapsulating one or more containers.
- node: A worker machine where pods run.
- cluster: A group of nodes managed as a single entity.
Additional Reading: What is a Kubernetes Cluster?
Control Plane Components:
- kube-apiserver: Acts as the central management entity, exposing the Kubernetes API. It processes RESTful requests, validates them, and updates the cluster’s state accordingly.
- etcd: A consistent and highly available key-value store that holds all cluster data, serving as the source of truth for the cluster’s state.
- kube-scheduler: Assigns newly created pods to nodes based on resource availability and other constraints, ensuring optimal distribution of workloads.
- kube-controller-manager: Runs controller processes that regulate the state of the cluster, such as managing replication and handling node failures.
- cloud-controller-manager: Integrates with cloud service providers to manage resources like load balancers and storage volumes for efficient cloud operations.
Node Components:
It’s worth noting that some node components may vary depending on specific configurations and requirements, but here are some examples.
- kubelet: An agent running on each node ensuring that containers are running as specified. It communicates with the control plane to maintain the desired state.
- kube-proxy: Maintains network rules on each node, facilitating communication between pods and managing network routing for services.
- container runtime: The software responsible for running containers, such as containerd or CRI-O, interfaces with Kubernetes to manage container lifecycles.
Kubernetes Key Features
- Autoscaling: Automatically adjusts container replicas. This includes both horizontal scaling (adding more pods) and vertical scaling (adjusting resource limits on existing pods).
- Rolling updates: Deploys updates with minimal downtime. Rolling updates gradually replace pods with newer versions, ensuring the application remains accessible during the update process.
- Service discovery: Ensures containers can communicate seamlessly. Each service in the cluster is assigned a stable IP address and DNS name, enabling automatic load balancing and routing requests to the appropriate pods.
Persistent Storage and Stateful Workloads
Kubernetes can also enhance data storage with Persistent Volumes (PVs), Persistent Volume Claims (PVCs), and Storage Classes. These components enable stateful workloads by persisting data beyond container lifecycles. This is essential for Kubernetes orchestration of critical applications.
- Persistent Volumes (PVs): These are pre-provisioned or dynamically provisioned storage resources that exist independently of Pods, ensuring data remains intact even when Pods are deleted or recreated.
- PersistentVolumeClaims (PVCs): These are requests for storage by applications. They allow Pods to specify their storage needs, such as size and access modes, which Kubernetes fulfills by binding to a matching PV.
- StorageClasses: These define the storage backend and parameters, such as performance or availability levels, enabling dynamic provisioning of PVs tailored to specific workload requirements.
Related Reading: Persistent Storage for Kubernetes
Challenges in Container Orchestration
Persistent Storage
One of the biggest challenges with running stateful, container-based applications like databases or analytics platforms is ensuring they have persistent storage. While Kubernetes provides objects such as Persistent Volumes (PVs), PersistentVolumeClaims (PVCs), StorageClasses, and StatefulSets to support persistent storage, it doesn’t natively interact with storage arrays, nor does it address advanced data management or protection requirements like automated backups, disaster recovery, or data replication across clusters.
Portworx provides a platform for persistent Kubernetes storage via RWO (ReadWriteOnce) and RWX ReadWriteMany) PersistentVolumes, as well as container data management to ensure that data survives container crashes, restarts, or migrations. It also provides automated storage operations for Kubernetes, such as resizing PVCs or scaling storage pools, all integrated with nearly any Kubernetes distribution, storage array, or on-premises or cloud environment.
Data Management
Managing data backups, disaster recovery, and application migration in containerized environments is complex. Orchestration platforms often lack built-in tools to handle these tasks efficiently, leaving organizations vulnerable to data loss or downtime.
Portworx solves this by allowing users to set up automated, incremental backups for containerized applications and cross-cluster application migration. These capabilities provide data protection, application continuity, and operational flexibility in dynamic, containerized environments.
High Availability
Ensuring high availability for containerized applications can be difficult, especially when running stateful workloads. Kubernetes provides mechanisms for pod rescheduling but doesn’t natively handle storage replication or failover for persistent data.
Portworx enables high availability by synchronously replicating volumes across nodes in a cluster, Portworx also provides disaster recovery through synchronous and asynchronous replication of storage volumes and kubernetes objects across data centers. This means that even if a node or zone goes offline, data remains accessible and applications won’t be interrupted.
Additional Reading: Best Practices for Kubernetes Storage
Future Trends in Container Orchestration
Emerging Technologies and Tools
The convergence of serverless computing and containers is gaining traction, offering developers higher abstraction. Serverless container orchestration allows developers to focus solely on code development without concerning themselves with the underlying infrastructure.
Sustainability has remained a global focus for organizations, and the container orchestration market is no exception. Efficient container orchestration optimizes resource allocation to minimize energy consumption and carbon footprint.
Organizations are also integrating security directly into the orchestration pipeline through DevSecOps practices. Kubernetes-native security solutions and policy management systems are being implemented to continuously monitor containers for threats and compliance violations.
The Role of AI in Orchestration
It’s no surprise that AI and ML are transforming container orchestration with new levels of intelligence. Here are just a few ways this rapidly evolving technology may play a role:
- Predictive scaling: AI and ML algorithms may analyze historical and real-time data on application performance, traffic patterns, and resource usage to predict workload spikes or drops and adjust resources proactively.
- Intelligent diagnostics: While Kubernetes already offers basic self-healing by restarting failed containers, AI-powered orchestration could enhance this by diagnosing the root causes of failures. ML algorithms may also detect patterns in logs and telemetry data to identify anomalies. This exploration of AIOps may provide a valuable intelligence layer in managing containerized environments.
- DevOps pipeline automation: While most CI/CD pipelines are already automated, AI may introduce valuable improvements — prioritizing tests based on historical failure patterns, optimizing resource usage during builds, or intelligently scheduling deployments to minimize disruption.
Elevate Your Container Orchestration With Portworx
Container orchestration has transformed how we deploy and manage applications, making scalability and reliability achievable at unprecedented levels. While container orchestration solutions like Kubernetes set the foundation, Portworx integrates with these solutions to automate, protect, and unify container data by providing enterprise-grade Kubernetes storage, backup, disaster recovery, and database solutions in a single platform.
Ready to get started with leading container data management? Try Portworx for free