Kubernetes is an open-source system for automating deployment, scaling and management of containerized applications. Founded by Google way back in 2014, it was eventually donated to the Cloud Native Computing Foundation (CNCF). Considering it forms an integral part of cloud computing, it is imperative for developers using it to understand the improvement tips for Kubernetes deployment.
Bear in mind Kubernetes is a portable, extensible., open-source platform for managing containerized workloads and services. It is written in Go Language and YAM. Now let’s a quick look at the simple hacks for improving Kubernetes deployment.
Define Deployment Resources in Line with Your Application Requirement
The focus of Kubernetes is to automate the operational effort needed to run containerized workloads and services is the efficient scheduling of pods into nodes. This is easily attained by specifying resource constraints.
You have to clearly define the requests and put limits on the CPU, memory, and other resources. That way, defining the resource requirements in the deployment descriptor makes it easier for the scheduler to make sure that each resource is allocated to the best available nodes to maximize runtime performance.
Deploy Kubernetes Clusters Close to Your Clients
Kubernetes cluster deployment location plays a vital role in the customer experience since far away locations can increase network latency. A good number of cloud providers boast multiple geographic zones situated across the world that allow system operators to match the location of the end-users of Kubernetes cluster deployment. This helps keep the latency low.
Pod and Node Affinity
Affinity is essential as it allows the allocation of pods on nodes. You can leverage node affinity to ensure that particular nodes are used to host certain pods. Pod affinity, on the other hand, can be used to co-locate two pods on the same node.
Depending on the resource requirements of the pod and its consumption within the cluster, the Kubernetes scheduler does a remarkable job of placing the pods on the associated nodes. But you might have to control the scheduling of these pods on the nodes for things to work your way.
