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Build your Self-Service Kubernetes Platform with Virtual Clusters
With loft, any Kubernetes cluster becomes a self-service platform to create virtual clusters on-demand | From the DevOps tool of the month series.
This is a new series, where each month I will introduce one new useful DevOps tool. 🚀
The second one is: loft - With loft, any Kubernetes cluster becomes a self-service platform to create namespaces and virtual clusters on-demand. At the same time, loft’s sleep mode and cluster sharing help you to save cloud computing cost.
▬▬▬▬▬▬ T I M E S T A M P S ⏰ ▬▬▬▬▬▬
- 0:00 - Intro
- 0:18 - What problem Loft solves?
- 2:22 - How Loft solves this problem?
- 3:15 - Self-Service
- 3:45 - Steps to setup your Kubernetes platform
- 5:03 - Demo to build your Kubernetes platform
- 16:42 - Sleep Mode to save cloud cost
#kubernetes #devops
What is GEEK
Buddha Community
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50+ Useful Kubernetes Tools for 2020 - Part 2
Introduction
Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.
According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.
And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.
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1623323207
Platform App Design | Cross-Platform Development Services
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1601051854
Kubernetes in the Cloud: Strategies for Effective Multi Cloud Implementations
Kubernetes is a highly popular container orchestration platform. Multi cloud is a strategy that leverages cloud resources from multiple vendors. Multi cloud strategies have become popular because they help prevent vendor lock-in and enable you to leverage a wide variety of cloud resources. However, multi cloud ecosystems are notoriously difficult to configure and maintain.
This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.
Kubernetes: Your Multi Cloud Strategy
Maintaining standardized application deployments becomes more challenging as your number of applications and the technologies they are based on increase. As environments, operating systems, and dependencies differ, management and operations require more effort and extensive documentation.
In the past, teams tried to get around these difficulties by creating isolated projects in the data center. Each project, including its configurations and requirements were managed independently. This required accurately predicting performance and the number of users before deployment and taking down applications to update operating systems or applications. There were many chances for error.
Kubernetes can provide an alternative to the old method, enabling teams to deploy applications independent of the environment in containers. This eliminates the need to create resource partitions and enables teams to operate infrastructure as a unified whole.
In particular, Kubernetes makes it easier to deploy a multi cloud strategy since it enables you to abstract away service differences. With Kubernetes deployments you can work from a consistent platform and optimize services and applications according to your business needs.
The Compelling Attributes of Multi Cloud Kubernetes
Multi cloud Kubernetes can provide multiple benefits beyond a single cloud deployment. Below are some of the most notable advantages.
Stability
In addition to the built-in scalability, fault tolerance, and auto-healing features of Kubernetes, multi cloud deployments can provide service redundancy. For example, you can mirror applications or split microservices across vendors. This reduces the risk of a vendor-related outage and enables you to create failovers.
#kubernetes #multicloud-strategy #kubernetes-cluster #kubernetes-top-story #kubernetes-cluster-install #kubernetes-explained #kubernetes-infrastructure #cloud
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AWS Fargate for Amazon Elastic Kubernetes Service | Caylent
On-demand cloud computing brings new ways to ensure scalability and efficiency. Rather than pre-allocating and managing certain server resources or having to go through the usual process of setting up a cloud cluster, apps and microservices can now rely on on-demand serverless computing blocks designed to be efficient and highly optimized.
Amazon Elastic Kubernetes Service (EKS) already makes running Kubernetes on AWS very easy. Support for AWS Fargate, which introduces the on-demand serverless computing element to the environment, makes deploying Kubernetes pods even easier and more efficient. AWS Fargate offers a wide range of features that make managing clusters and pods intuitive.
Utilizing Fargate
As with many other AWS services, using Fargate to manage Kubernetes clusters is very easy to do. To integrate Fargate and run a cluster on top of it, you only need to add the command –fargate to the end of your eksctl command.
EKS automatically configures the cluster to run on Fargate. It creates a pod execution role so that pod creation and management can be automated in an on-demand environment. It also patches coredns so the cluster can run smoothly on Fargate.
A Fargate profile is automatically created by the command. You can choose to customize the profile later or configure namespaces yourself, but the default profile is suitable for a wide range of applications already, requiring no human input other than a namespace for the cluster.
There are some prerequisites to keep in mind though. For starters, Fargate requires eksctl version 0.20.0 or later. Fargate also comes with some limitations, starting with support for only a handful of regions. For example, Fargate doesn’t support stateful apps, DaemonSets or privileged containers at the moment. Check out this link for Fargate limitations for your consideration.
Support for conventional load balancing is also limited, which is why ALB Ingress Controller is recommended. At the time of this writing, Classic Load Balancers and Network Load Balancers are not supported yet.
However, you can still be very meticulous in how you manage your clusters, including using different clusters to separate trusted and untrusted workloads.
Everything else is straightforward. Once the cluster is created, you can begin specifying pod execution roles for Fargate. You have the ability to use IAM console to create a role and assign it to a Fargate cluster. Or you can also create IAM roles and Fargate profiles via Terraform.
#aws #blog #amazon eks #aws fargate #aws management console #aws services #kubernetes #kubernetes clusters #kubernetes deployment #kubernetes pods
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