Kubernetes – Container Orchestration for the Software Defined Data Center (SDDC)..(5/7)

The fourth and previous blog in this seven part series on Software Defined Datacenters (@ http://www.vamsitalkstech.com/?p=5010)  discussed how Linux Containers & Docker, are emerging as a key component of digital applications. We looked at various drivers & challenges stemming from running Containerized Applications from both a development and IT operations standpoint. In the fifth blog in this series, we will discuss another key emergent technology – Google’s Kubernetes (k8s)– which acts as the foundational runtime orchestrator for large scale container infrastructure. We will take the discussion higher up the stack in the next blog with OpenShift – Red Hat’s PaaS (Platform as a Service) platform – which includes Kubernetes and provides a powerful, agile & polyglot environment to build and manage microservices based applications.

The Importance of Container Orchestration… 

With Cloud Native application development emerging as the key trend in Digital platforms, containers offer a natural choice for a variety of reasons within the development process. In a nutshell, Containers are changing the way applications are being architected, designed, developed, packaged, delivered and managed. That is the reason why Container Orchestration has become a critical “must have” since for enterprises to be able to derive tangible business value – they must be able to run large scale containerized applications.

Why Linux Containers and Docker are the Runtime for the Software Defined Data Center (SDDC)..(4/7)

While containers have existed in Unix based operating systems such as Solaris and FreeBSD, pioneering work in the Linux OS community has led to the mainstreaming of this disruptive technology. Now, despite all the benefits afforded to both developers and IT Operations by containers, there are critical considerations involved in running containers at scale in complex n-tier real world applications across multiple datacenters.

What are some of the key considerations in running containers at scale –

Consideration #1 – You need a Model/Paradigm/Platform for the lifecycle management of containers – 

This includes the ability to organize applications into groups of containers, scheduling these applications on host servers that match their resource requirements, deploy applications as changes happen, manage complex storage integration, network topologies and provide seamless ways to destroy, restart etc  etc

Consideration #2 – Administrative Lifecycle Management  –

This covers a range of lifecycle processes ranging from constant deployments to upgrades to monitoring and monitoring. Granular issues include support for application patching with minimal downtime, support for canary releases, graceful failures in cloud-native applications, (container) capacity scale up & scale down based on traffic patterns etc.

Consideration #3 – Support DevelopMENT PROCESSES moving to DevOps and microservices

These reasons vary from rapid feature development, ability to easily accommodate CI/CD approaches, flexibility (as highlighted in the above point). For instance,k8s removes one of the biggest challenges with using vanilla containers along with CI/CD tools like Jenkins –  the challenge of linking individual containers that run microservices with one another. Other useful features include load balancing, service discovery, rolling updates and red/green deployments.

While the above drivers are just general guidelines, the actual tipping point for large scale container adoption will vary from enterprise to enterprise. However, the common precursor to supporting containerized applications at scale has to be an enterprise grade management and orchestration platform. And for some very concrete reasons we will discuss,k8s is fast emerging as the defacto leader in this segment.

Introducing Kubernetes (K8s)…

Kubernetes (kube or k8s) is an open-source platform that aims to automate the scheduling, deploying and managing applications running on containers. Kubernetes (and platforms built leveraging it) are designed to bring both development and operations teams together. This affects how Cloud Native applications are architected, composed, deployed, and managed.

k8s was incubated at Google (given their expertise in running billions of container workloads at scale) over the last decade. One caveat, the famous cluster controller & container management system known as Borg is deployed extensively at Google. Borg is a predecessor to k8s but is generally believed that while k8s borrows its core design tenets from Borg, it only contains a subset of the features present in Borg. [4]

Again, from [4] – “Kubernetes traces its lineage directly from Borg. Many of the developers at Google working on Kubernetes were formerly developers on the Borg project. We’ve incorporated the best ideas from Borg in Kubernetes, and have tried to address some pain points that users identified with Borg over the years.

However, k8s is not a Google-only project anymore. In 2015 it was donated to the Cloud Native Foundation. The next year, 2015 also saw the k8s foundational release 1.0. Since then the project has been moving with a fair degree of feature & release velocity. The next version 1.4 was released in 2016. With the current 1.7 release, k8s has found wider industry wide adoption. The last year has seen heavy contributions from the likes of Red Hat, Microsoft, Mirantis, and Fujitsu et al to thek8s codebase.

k8s is infrastructure agnostic with clusters deployable on pretty much any Linux distribution – Red Hat, CentOS, Debian, Ubuntu etc.  K8s also runs on all popular cloud platforms – AWS, Azure and Google Cloud. It is also virtually hypervisor agnostic supporting – VMWare, KVM, and libvirt. It also supports both Docker or Windows Containers or rocket (rkt) runtimes with expanding support as newer runtimes become available.[3]

After this brief preamble, let us now discuss the architecture and internals of this exciting technology. We will then discuss why it has begun to garner massive adoption and why it deserves a much closer look by enterprise IT teams.

The Architecture of Kubernetes…

As depicted in the below diagram, Kubernetes (k8s) follows a master-slave methodology much like Apache Mesos and Apache Hadoop.

Kubernetes Architecture

The k8s Master is the control plane of the architecture. It is responsible for scheduling deployments, acting as the gateway for the API, and for overall cluster management. As depicted in the below illustration, It consists of several components, such as an API server, a scheduler, and a controller manager. The master is responsible for the global, cluster-level scheduling of pods and handling of events. For high availability and load balancing, multiple masters can be setup.  The core API server which runs in the master hosts a RESTful service that can be queried to maintain the desired state of the cluster and to maintain workloads. The admin path always goes through the Master to access the worker nodes and never goes directly to the workers.  The Scheduler service is used to schedule workloads on containers running on the slave nodes. It works in conjunction with the API server to distribute applications across groups of containers working on the cluster. It’s important to note that the management functionality only accesses the master to initiate changes in the cluster and does not access the nodes directly.

The second primitive in the architecture is the concept of a Node. A node refers to a host which may be virtual or physical. The node is the worker in the architecture and runs application stack components on what are called Pods. It needs to be noted that each node runs several kubernetes components such as a kubelet and a kube proxy. The kubelet is an agent process that works to start and stop groups of containers running user applications, manages images etc and communicates with the Docker engine. The kube-proxy works as a proxy networking service that redirects traffic to specific services and pods (we will define these terms in a bit). Both these agents communicate with the Master via the API server.

Nodes (which are VMs or bare metal servers) are joined together to form Clusters. As the name connotes, Clusters are a pool of resources – compute, storage and networking – that are used by the Master to run application components. Nodes, which used to be known as minions in prior releases, are the workers. Nodes host end user applications using their local resources such as compute, network and storage. Thus they include components to aid in logging, service discovery etc. Most of the administrative and control interactions are done via the kubectl script or by performing RESTful calls to the API server. The state of the cluster and the workloads running on it is constantly synchronized with the Master using all these components.  Clusters can be easily made highly available and scaled up/down on demand. They can also be federated across cloud providers and data centers if a hybrid architecture is so desired.

The next and perhaps the most important runtime abstraction in k8s is called a Pod. It is recommended that applications deployed in a K8s infrastructure be composed of lightweight and stateless microservices. These microservices can be deployed in individual or multiple containers. If the former strategy is chosen, each container only performs a specialized business activity. Though k8s also supports stateful applications, stateless applications confer a variety of benefits including loose coupling, auto-scaling etc.

The Pod is essentially the unit of infrastructure that runs an application or a set of related applications and as such it always exists in the context of a set of Linux namespaces or cgroups. A Pod is a group of one or more containers which always run on the same host. They are always scheduled together and share a common IP address/port configuration. However, these IP assignments cannot be guaranteed to stay the same over time. This can lead to all kinds of communication issues over complex n-tier applications. Kubernetes provides an abstraction called a Service – which is a grouping of a set of pods mapped to a common IP address.

The pod level inter-communication happens over IPC mechanisms. Pods also share local storage running on the node with the shared storage essentially mounted on.  The infrastructure can provide services to the pod that span resources and process management.  The key advantage here is that Pods can run related groups of applications with the advantage that individual containers can be made not only more lightweight but also versioned independently, which greatly aids in complex software projects where multiple teams are working on their own microservices which can be created and updated on their own separate cadence.

Labels are key value pairs that k8s uses to identify a particular runtime element be it a node, pod or service. They are most frequently applied to pods and can be anything that makes sense to the user or the administrator. Example of a pod label would be –  (app=mongodb, cluster=eu3,language=python).  Label Selectors determine what Pods are targeted by a Service.

From an HA standpoint, administrators can declare a configuration policy that states the number of pods that they need to have running at any given point. This ensures that pod failures can be recovered from automatically by starting new pods. An important HA feature is the notion of replica sets. The Replication controller ensures that there are a specified set of pods available to a given application and in the event of failure, new pods can be started to ensure that the actual state matches the desired state. Such pods are called replica sets. Workloads that are stateful are covered for HA using what are called pet sets.

The Replication Controller component running in the Master node determines which pods it controls and then uses a pod template file (typically a JSON or YAML file) to create new pods. It also is in charge of ensuring that the number of pods stays in consonance with replica counts. It is important to note that while the Replication controller just replaces dead or dysfunctional pods on the nodes that hosted them, it does not more pods across nodes.

Storage & Networking in Kubernetes  –

Local pod storage is ephemeral and is reclaimed when the pod dies or is taken offline but if data needs to be persistent or shared between pods K8s provides Volumes. So really, depending on the use case, k8s supports a range of storage options from local storage to network storage (NFS, Ceph, Gluster, Ceph) to cloud storage (Google Cloud or AWS). More details around these emerging features are found at the K8s official documentation. [1]

Kubernetes has a pluggable networking implementation that works with the design of the underlying network. [2], there are four networking challenges to solve:

  • Container-container communication within a host – this is based purely on IPC & localhost mechanisms
  • Interpod communication across hosts – Here Kubernetes mandates that all pods be able to communicate with one another without NAT and that the IP of a pod is the same from within the pod and outside of it.
  • Pod to Service communications – provided by the Service implementation. As we have seen above, K8s services are provided with IP addresses that clients can reach them by.  These IP addresses are proxied by the kube-proxy process which runs on all nodes sends to the service which then routes the external request to the correct pod.
  • External to Service communication – again provided by the Service implementation. This is done primarily by mapping the load balancer configuration to services running in the cluster. As outlined above, when traffic is sent to a node, the the kube-proxy process ensures that the traffic is routed to the appropriate service.

Network administrators looking to implement the K8s cluster model have a variety of choices from open source projects such as – Flannel, OpenContrail etc.

Why is Kubernetes such an exciting (and important) Cloud technology –

We have discussed the business & technology advantages of building an SDDC over the previous posts in this series.  As a project, k8s has very lofty goals to simplify the lifecycle of not only containers but also to enable the deployment & management of distributed systems across any kind of modern datacenter infrastructure. It’s designed to promote extensibility and pluggability (via APIs) as we will see in the next blog with OpenShift.

There are three specific reasons why k8s is rapidly becoming a de facto choice for Container orchestration-

  1. Once containers are used to full-blown applications,  organizations need to deal with several challenges to enable efficiency in the overall development & deployment processes. These include enabling a rapid speed of application development among various teams working on APIs, UX front ends, business logic, data etc.
  2. The ability to scale application deployments and to ensure a very high degree of uptime by leveraging a self-healing & immutable infrastructure. A range of administrative requirements from monitoring, logging, auditing, patching and managing storage & networking all come to consideration.
  3. The need to abstract developers away from the infrastructure. This is accomplished by allowing dev teams to specific their infrastructure requirements via declarative configuration.


Kubernetes is emerging as the most popular platform to deploy and manage digital applications based on a microservices architecture. As a sign of its increased adoption and acceptance, Kubernetes is being embedded in Platform as a Service (PaaS) offerings where it offers all of the same advantages for administrators (deploying application stacks) while also freeing up developers of complex underlying infrastructure. The next post in this series will discuss OpenShift, Red Hat’s market leading PaaS offering, which leverages best of breed projects such as Docker and Kubernetes.


[1] Kubernetes Offical documentation – https://kubernetes.io/docs/concepts/storage/persistent-volumes/

[2] Kubernetes Networking
– https://kubernetes.io/docs/concepts/cluster-administration/networking/

[3] Key Commits to Kubernetes – http://stackalytics.com/?project_type=kubernetes-group&metric=commits

[4] Borg: The predecessor to Kubernetes – http://blog.kubernetes.io/2015/04/borg-predecessor-to-kubernetes.html

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