Merge pull request #195 from alex-isv/main-source

Fix federatorai after changes done upstream
pull/171/head
alex-isv 2021-10-11 10:06:01 -06:00 committed by GitHub
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# <img src="https://raw.githubusercontent.com/prophetstor-ai/public/master/images/logo.png" width=60/> Federator.ai Operator
Federator.ai helps enterprises optimize cloud resources, maximize application performance, and save significant cost without excessive over-provisioning or under-provisioning of resources, meeting the service-level requirements of their applications.
Enterprises often lack understanding of the resources needed to support their applications. This leads to either excessive over-provisioning or under-provisioning of resources (CPU, memory, storage). Using machine learning, Federator.ai determines the optimal cloud resources needed to support any workload on OpenShift and helps users find the best-cost instances from cloud providers for their applications.
Enterprises often lack understanding of the resources needed to support their applications. This leads to either excessive over-provisioning or under-provisioning of resources (CPU, memory, storage). Using machine learning, Federator.ai determines the optimal cloud resources needed to support any workload on Kubernetes and helps users find the best-cost instances from cloud providers for their applications.
**Multi-layer workload prediction**
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* [Company Information](https://www.prophetstor.com/)
## Prerequisites
- The [Kubernetes](https://kubernetes.io/) version 1.16 or later if using Kubernetes environment.
- The [Openshift](https://www.openshift.com) version 4.x.x or later if using OpenShift platform.
- The [Kubernetes](https://kubernetes.io/) version 1.16 or later.
- The [Helm](https://helm.sh/) version is 3.x.x or later.
## Add Helm chart repository

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# <img src="https://raw.githubusercontent.com/prophetstor-ai/public/master/images/logo.png" width=60/> Federator.ai Operator
Federator.ai helps enterprises optimize cloud resources, maximize application performance, and save significant cost without excessive over-provisioning or under-provisioning of resources, meeting the service-level requirements of their applications.
Enterprises often lack understanding of the resources needed to support their applications. This leads to either excessive over-provisioning or under-provisioning of resources (CPU, memory, storage). Using machine learning, Federator.ai determines the optimal cloud resources needed to support any workload on OpenShift and helps users find the best-cost instances from cloud providers for their applications.
Enterprises often lack understanding of the resources needed to support their applications. This leads to either excessive over-provisioning or under-provisioning of resources (CPU, memory, storage). Using machine learning, Federator.ai determines the optimal cloud resources needed to support any workload on Kubernetes and helps users find the best-cost instances from cloud providers for their applications.
**Multi-layer workload prediction**