Docker with no doubt is an excellent open source tool. However, you cannot have complex application deployments just with docker engine and containers. Proper platform has to be done for container clustering for deploying complex application architectures. Your containerized applications should be able to scale up and down based on application resource requirements.
What we need is a good framework for managing containers in an efficient way. Containers are meant to be short-lived and when it comes to container orchestration, the main things to consider are
- High availability
- Ease of deployment
- Good service discovery.
Container Clustering and Orchestration Tools
In this post, we will cover the list of best container clustering and orchestrations tools which are being used in production by many companies.
Docker Swarm is a clustering and scheduling tool for Docker containers. With Swarm, IT administrators and developers can establish and manage a cluster of Docker nodes as a single virtual system.
Swarm mode also exists natively for Docker Engine, the layer between the OS and container images. Swarm mode integrates the orchestration capabilities of Docker Swarm into Docker Engine 1.12 and newer releases.
Clustering is an important feature for container technology, because it creates a cooperative group of systems that can provide redundancy, enabling Docker Swarm failover if one or more nodes experience an outage. A Docker Swarm cluster also provides administrators and developers with the ability to add or subtract container iterations as computing demands change.
An IT administrator controls Swarm through a swarm manager, which orchestrates and schedules containers. The swarm manager allows a user to create a primary manager instance and multiple replica instances in case the primary instance fails. In Docker Engine’s swarm mode, the user can deploy manager and worker nodes at runtime.
Docker Swarm uses the standard Docker application programming interface to interface with other tools, such as Docker Machine.
Kubernetes, at its basic level, is a system for managing containerized applications across a cluster of nodes. In many ways, Kubernetes was designed to address the disconnect between the way that modern, clustered infrastructure is designed, and some of the assumptions that most applications and services have about their environments.
Most clustering technologies strive to provide a uniform platform for application deployment. The user should not have to care much about where work is scheduled. The unit of work presented to the user is at the “service” level and can be accomplished by any of the member nodes.
However, in many cases, it does matter what the underlying infrastructure looks like. When scaling an app out, an administrator cares that the various instances of a service are not all being assigned to the same host.
On the other side of things, many distributed applications build with scaling in mind are actually made up of smaller component services. These services must be scheduled on the same host as related components if they are going to be configured in a trivial way. This becomes even more important when they rely on specific networking conditions in order to communicate appropriately.
While it is possible with most clustering software to make these types of scheduling decisions, operating at the level of individual services is not ideal. Applications comprised of different services should still be managed as a single application in most cases. Kubernetes provides a layer over the infrastructure to allow for this type of management.
Mesos is another cluster management tool which can manage container orchestration very efficiently. It was created by twitter for its infrastructure and then got open sources. It is being used by companies like eBay, Airbnb etc. Mesos is not a dedicated tool for containers,
Mesos leverages features of the modern kernel—”cgroups” in Linux, “zones” in Solaris—to provide isolation for CPU, memory, I/O, file system, rack locality, etc. The big idea is to make a large collection of heterogeneous resources. Mesos introduces a distributed two-level scheduling mechanism called resource offers.
Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. It is a thin resource sharing layer that enables fine-grained sharing across diverse cluster computing frameworks, by giving frameworks a common interface for accessing cluster resources.The idea is to deploy multiple distributed systems to a shared pool of nodes in order to increase resource utilization. A lot of modern workloads and frameworks can run on Mesos, including Hadoop, Memecached, Ruby on Rails, Storm, JBoss Data Grid, MPI, Spark and Node.js, as well as various web servers, databases and application servers.
Cloud Based Container Clustering Services
There are few managed containers cluster and orchestration services that you can use to avoid complex cluster setups.
Google Container Engine
GCE is a managed container service on google cloud. At the backend, GCE used kubernetes. You can use all the kubernetes functionalities on GCE.
Amazon EC2 Container Service
ECS is a service offered by AWS for managing the cluster of containers. ECS is not cloud agnostic as it uses its proprietary cluster management and scheduling technologies at the backend. Only thing you have to worry about is the vendor lockin.
IBM blumix Container Service
With IBM Bluemix Container Service, manage your apps inside Docker containers, on the IBM cloud. Containers virtualize all the objects that an app needs to run. And they have the benefits of resource isolation and allocation, but are more portable and efficient than, for example, virtual machines.