How to Choose the Right Container Orchestration Platform and Deploy It Successfully

Containers have revolutionized how we develop, package, and deploy applications. By providing a lightweight, portable, and isolated environment for application code and dependencies, containers enable a microservices architecture, improve resource utilization, and accelerate development cycles.

As your containerized environment grows, managing containers across multiple hosts becomes increasingly difficult. Manually scheduling containers, managing networking and storage, ensuring high availability, and scaling the application requires significant time and effort. This is where container orchestration comes in.

The Need for Container Orchestration

Container orchestration automates the deployment, scaling, and management of containerized applications. An orchestration platform takes care of:

• Scheduling containers across a cluster of machines
• Service discovery and load balancing
• Storage provisioning
• Application updates and rollbacks
• Self-healing and rescheduling failed containers
• Scaling the application based on demand

According to a 2019 survey by the Cloud Native Computing Foundation (CNCF), 84% of respondents were using containers in production, with 78% using Kubernetes as their orchestration platform. The need for container orchestration is no longer a question – it‘s now a necessity for running container workloads at scale.

Factors to Consider When Choosing an Orchestration Platform

With multiple orchestration options available, choosing the right one for your needs can be challenging. Here are the key factors to evaluate:

Application Architecture and Environment Fit

Consider how well the orchestration platform fits with your application architecture and existing environment. For example:

• If you‘re using a microservices architecture with polyglot languages and frameworks, you‘ll want an orchestrator that supports heterogeneous workloads and has integrations with your existing CI/CD tooling.
• If you‘re heavily invested in a particular cloud provider, you may want to choose their native orchestration service (like EKS on AWS or GKE on Google Cloud) to simplify integration with other cloud services.
• If you need to run containers on IoT/edge devices, you‘ll want an orchestrator with a small footprint and support for ARM architectures.

Core Features and Capabilities

Evaluate the core feature set of each orchestration platform against your application requirements. Some key features to look for:

• Automated rollouts and rollbacks for application updates
• Service discovery and load balancing for internal and external traffic
• Persistent storage orchestration for stateful services
• Self-healing and rescheduling of failed containers
• Configuration management for application secrets and config
• Batch/cron job scheduling
• Autoscaling based on CPU/memory usage or custom metrics
• Federation for managing multiple clusters

According to the CNCF survey, the top reasons for adopting Kubernetes were:

Reason	Percentage
Automated deployment	67%
Service discovery & load balancing	66%
Orchestration	63%
Desired state and self-healing	58%
Open source	54%

Community, Ecosystem and Momentum

The strength of the community and ecosystem around an orchestration platform directly impacts the availability of skilled talent, support resources, and third-party integrations. Judge the health of the community by:

• GitHub activity (stars, forks, pull requests)
• Stack Overflow questions and answers
• Meetup members and events
• Conference talks and case studies
• Vendor support and managed services

As of June 2020, Kubernetes had over 64,000 stars and 2,800 contributors on GitHub, 75,000 StackOverflow questions, and over 155 certified service providers. This demonstrates the massive momentum behind Kubernetes and its expanding ecosystem.

Learning Curve and Complexity

Container orchestration platforms can have a steep learning curve, especially for complex systems like Kubernetes. When selecting a platform, honestly assess your team‘s skills and bandwidth to learn and manage a sophisticated system.

However, don‘t just select the simplest tool – an orchestrator that‘s too basic may not provide the capabilities you need for more complex use cases. Strike a balance based on your team‘s aptitude and commitment to learning.

For organizations adopting Kubernetes, the CNCF‘s Kubernetes Certification and training resources can help build in-house expertise. As of August 2020, over 23,000 people had achieved Certified Kubernetes Administrator (CKA) certification.

Comparing the Top Orchestration Platforms

With these evaluation criteria in mind, let‘s dive deeper into the top container orchestration platforms and see how they stack up.

Kubernetes

Developed by Google based on their internal Borg system, Kubernetes (K8s) has become the de-facto standard for container orchestration. It was open sourced in 2014 and is now maintained by the Cloud Native Computing Foundation (CNCF).

Image Source: Kubernetes.io

Key aspects of Kubernetes‘ architecture:

• Master node runs the control plane components (API server, scheduler, controller manager)
• Worker nodes run the container runtime (Docker, containerd, CRI-O) and Kubelet agent
• etcd distributed key-value store holds the cluster state
• Deployments define the desired state for a set of pods
• Services provide a stable endpoint and load balancing for pods
• Ingress exposes HTTP/HTTPS routes from outside the cluster to services

Strengths of Kubernetes:

• Most mature and feature-rich platform
• Highly modular and extensible architecture
• Broad ecosystem of add-ons, tools, and extensions
• Used in production by many large enterprises (like Spotify, Airbnb, CERN)
• Strong community and industry support (from Google, AWS, Microsoft, Red Hat, etc.)

Challenges with Kubernetes:

• Steep learning curve with many complex abstractions (pods, services, ingress, etc.)
• Can be complex to set up and manage, especially at scale
• etcd dependency introduces a single point of failure and performance bottleneck

Docker Swarm

Docker Swarm is the native clustering and orchestration feature built into the Docker Engine. Its main appeal is simplicity – if you‘re already using Docker, enabling Swarm mode lets you orchestrate containers across multiple Docker hosts without needing to learn a complex new system.

Image Source: Collabnix

Key aspects of Docker Swarm:

• Manager nodes maintain the cluster state and schedule tasks
• Worker nodes execute containers and services
• Services define the desired state for a group of containers
• Swarm mode provides built-in service discovery, load balancing, and overlay networking

Strengths of Docker Swarm:

• Easy to set up and use if already familiar with Docker
• Tight integration with Docker Compose and CLI
• No single point of failure in the architecture
• Secure by default with automatic PKI and mutual TLS

Challenges with Docker Swarm:

• Less feature-rich and flexible than Kubernetes
• Limited to Docker‘s own ecosystem and API
• Smaller community and mindshare compared to Kubernetes
• Unclear development momentum and long-term roadmap

According to a 2019 Datadog survey, Docker Swarm usage was nearly flat from 2018 to 2019 while Kubernetes adoption grew by over 50%. Docker itself now supports Kubernetes as an alternative orchestrator.

Apache Mesos

Apache Mesos is a distributed systems kernel that abstracts CPU, memory, storage, and other compute resources away from physical or virtual machines. Mesos is actually a general-purpose cluster manager – it relies on specialized frameworks to handle scheduling for specific workloads.

Image Source: Mesosphere

Key aspects of Mesos:

• Abstracts cluster resources (CPU, RAM, disk, ports) and offers them to frameworks
• Frameworks (like Marathon or Kubernetes) receive resource offers and decide which tasks to run on which agents
• Masters handle resource allocation and task scheduling across agent nodes
• Agents launch and monitor tasks assigned to them by the master

Strengths of Mesos:

• Proven to scale to tens of thousands of nodes at companies like Twitter, Airbnb, and Apple
• Very flexible and extensible with ability to run any type of workload (not just containers)
• Wide ecosystem with many frameworks for different use cases (big data, machine learning, etc.)
• Highly available master via Paxos algorithm and ZooKeeper

Challenges with Mesos:

• Complex architecture with many components and frameworks to manage
• Need to rely on third-party frameworks for core orchestration capabilities
• Steeper learning curve, especially for developers used to working only with containers
• Less mindshare and community compared to Kubernetes and Swarm

Deploying Kubernetes for Production

Given its rich feature set, strong ecosystem, and industry momentum, Kubernetes is the default choice for most organizations looking to adopt container orchestration. But using Kubernetes in production requires careful planning and execution.

Image Source: Kubernetes.io

Here are some key considerations and best practices:

Deployment Approach

Choose the right deployment approach based on your requirements and in-house skills:

• Fully-managed Kubernetes service (GKE, EKS, AKS) – simplest but most expensive, requires least in-house expertise
• Kubernetes installer (kops, kubespray) – good balance of control and ease of deployment
• Manual deployment (kubeadm) – most complex, requires deep Kubernetes expertise, provides most control

Networking Model

Understand the Kubernetes networking model and choose the right networking plugin for your environment:

• Overlay network (Flannel, Weave Net) – simplest, good for getting started
• Layer 2/3 network (Calico, Cilium) – better performance but requires compatible network infrastructure
• Cloud provider network (GCE, AWS VPC) – integrate directly with cloud provider‘s SDN, best performance and management

Storage

Understand Kubernetes storage concepts and provide the right abstractions for your stateful services:

• Use Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) to decouple pods from storage implementation details
• Choose appropriate Storage Class based on backend storage (EBS, Azure Disk, NFS, etc.)
• Consider storage orchestration solutions like Rook for automated provisioning and management

Deployment Pipeline

Implement a robust continuous deployment pipeline that integrates with Kubernetes:

• Use declarative config (YAML/JSON) to represent desired state of application
• Manage config in version control (Git) and deploy via pull requests
• Implement Blue-Green or Canary deployments for zero-downtime updates
• Leverage Kubernetes‘ rolling update and rollback functionality

Monitoring and Observability

You can‘t manage what you can‘t measure. Implement comprehensive monitoring and observability for your Kubernetes environment:

• Collect resource metrics (CPU, RAM) using Metrics Server
• Collect application and business metrics using Prometheus
• Implement centralized logging using the ELK/EFK stack
• Consider an observability platform like Grafana or Datadog for dashboarding and alerting

Service Mesh

For a microservices architecture with dozens or hundreds of services, consider using a service mesh to handle cross-cutting concerns like:

• Service-to-service communication and load balancing
• Traffic management (routing, splitting, mirroring)
• Security (mTLS, access control, rate limiting)
• Observability (metrics, logging, tracing)

Istio is the most popular service mesh for Kubernetes, followed by Linkerd. Service meshes add a lot of power and flexibility but also introduce another layer of complexity to manage.

Future Outlook and Emerging Trends

The container orchestration space continues to evolve rapidly. Kubernetes has won the orchestration battle and is becoming the standard layer for deploying and managing modern applications in public, private, and hybrid cloud environments. All major cloud providers now offer managed Kubernetes services and many vendor products are being re-platformed on top of Kubernetes.

Some emerging trends in the Kubernetes ecosystem to keep an eye on:

• Operators for extending Kubernetes to manage complex, stateful applications
• Virtual Kubelet for integrating Kubernetes with serverless platforms like AWS Fargate
• Service Mesh Interface (SMI) for standardizing interoperability between service mesh technologies
• gRPC as an alternative to REST for high-performance microservices communication

Conclusion

Container orchestration is now a critical part of the modern application deployment stack. While there are several viable options, Kubernetes has emerged as the clear leader and is seeing massive adoption across the industry.

Choosing the right orchestration platform requires careful evaluation of your application architecture, required features, team skills, and other factors. However, for most organizations, Kubernetes will be the default choice due to its rich capabilities, extensive ecosystem, and strong momentum.

Deploying Kubernetes in production requires significant planning and effort to get right. Managed Kubernetes services from cloud providers can greatly simplify deployment and operation, but you still need to understand the core concepts and architecture to use it effectively.

As with any powerful technology, container orchestration in general and Kubernetes in particular introduce complexity that needs to be managed. Make sure your organization has the skills and commitment to leverage Kubernetes effectively before diving in head-first. When done right, Kubernetes can be a huge force multiplier for development velocity and operational efficiency.