Tips for Improving Kubernetes Deployment
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It was founded by Google in 2014, after which it was donated to the CNCF (Cloud Native Computing Foundation).
Kubernetes is an ancient Greek word that means steering a ship and, in this case, steering containers. Today, it is a common household word within the cloud community. It forms an integral part of cloud computing. Thus, it is imperative that developers using it understand improvement strategies for Kubernetes deployment.
Before we delve into this, though, we need to have a basic comprehension of Kubernetes and the things involved in deployment.
Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services. It is a multi-container management solution that allows the grouping of containers into logical units so that the containers can communicate with each other. It is written in Go Language and YAML.
Building Blocks of Kubernetes
Container
A container instance comprises dependencies such as the framework, code, OS interface, system libraries, and additional settings required to execute an application. The container consisting of the application can work as a ready-to-run software package as it has the essential requirements fulfilled.
Pod
It represents the unit of deployment in the Kubernetes cluster and can hold single or multiple containers. A single container pod is used when the container runs on a physical machine on top of an operating system. A multi-container pod is used when the application is dependent on other applications/services for their functioning.
Replication Controller
It is one of the key features of Kubernetes for managing the pod’s lifecycle. It ensures that the required number of pods always exists. It helps create, scale, and maintain multiple pods as part of the desired state. In the case that a pod crashes, the replication counter creates a new one.
Replication Sets
Replication sets are similar to the replication controller. The key difference between them is based on the support of selectors. The replication controller supports ‘equity-based selectors’, and replication sets support ‘set-based selectors’.
In equity-based selectors, the filtering criterion is based on key and value, where the object must satisfy the specified label values. In set-based selectors, there is better flexibility in comparison to its counterpart. It allows filtering keys according to a set of values.
Deployments
The deployment controller is the upgraded and higher version of the replication controller. It manages the deployment of the replica sets. They are declarative in nature and provide the required information/updates to pods and replica sets. The replica sets are automatically created for the pods on the creation of deployment. The pods are connected with other pods through nodes.
Deployment.yaml apiVersion: apps/v1 kind: Deployment Metadata: name: myapp-deployment Labels: app: myapp Spec: replicas: 3 Strategy: type: RollingUpdate rollingUpdate: maxSurge: 25% maxUnavailable: 25% Selector: matchLabels: app: myapp Template: Metadata: Labels: app: myapp Spec: Containers: — name: myapp-container image: busybox
Services are responsible for exposing pods to other pods. Ingress are responsible for exposing your services to the outside world.
Improvement Strategies for Kubernetes Deployment
Efficient deployment can play a vital role in server cost management, increasing infrastructure utilization, and reducing costs to get the most out of the environmental setup.
Below are points to help you achieve your high-performance goals with Kubernetes deployment.
Define deployment resources as per your application requirement
The core of Kubernetes to automate the operational effort required to run containerized workloads and services is the efficient scheduling of pods into nodes. This can be achieved by specifying resource constraints.
We need to define the requests and put limits on the CPU, memory, and other resources. As a result, defining the resource requirements in the deployment descriptor makes it easier for the scheduler to ensure that each resource is allocated to the best available nodes to maximize runtime performance.
Pod.yaml Resources: Requests: memory: 1Gi cpu: 250m limits: memory: 2.5Gi cpu: 750m
Implementing taints and tolerations
When we deal with a large number of pods, labels, or nodes, it’s difficult to steer particular pods to land on certain nodes. The pod affinity allows the pod to be scheduled over a node, but taint nodes have the ability to repel a set of pods. Toleration is applied to these pods for them to be scheduled over any node having a matching taint.
Taint and toleration work as reverse node selectors that help avoid scheduling pods over inappropriate nodes. For example, this may be useful in a case where you want to allow only backup jobs to be scheduled over a node. They give operators very fine-grained control over deployment performance.
Pod priorities
The Kubernetes scheduler continuously monitors the cluster for unbound (unscheduled) pods. When found, the scheduler uses pod priority to decide which to schedule first. The number with a higher value indicates a higher priority order. The priority order helps run the deployment efficiently.
Pod and node affinity
Affinity allows the allocation of pods on nodes. Node affinity can be used to ensure that particular nodes are used to host certain pods. Pod affinity can be used to co-locate two pods on the same node.
Based on the resource requirements of the pod and its consumption within the cluster, the Kubernetes scheduler does a good enough job of placing the pods on the associated nodes. However, we might need to control the scheduling of these pods on the nodes. In such cases, pod affinity can be used. After analyzing the workload, we need to utilize node and pod affinity strategies for the deployments.
Build optimized images
To fine-tune the Kubernetes deployment performance, it is important to have optimized images. A container-optimized image will greatly reduce your container image size, and this helps Kubernetes retrieve the image faster and run the resultant running container more efficiently.
Deploy Kubernetes clusters near your clients
Kubernetes cluster deployment location can play a major role in the customer experience because far away locations can increase network latency. The cloud providers have multiple geographic zones located across the world that allow system operators to match the location of the end-users of Kubernetes cluster deployment, which helps keep the latency low.
Conclusion
Kubernetes is a part of every cloud provider service and will continue playing a critical role in microservices for a while now. Thus, it is a very efficient multi-container management orchestration tool and will provide out-of-the-box performance when your deployments are configured structurally as per your need.
I hope you found this article insightful. Thank you for reading!