Overview
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.
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Multi-layer workload prediction Using machine learning and math-based algorithms, Federator.ai predicts containerized application and cluster node resource usage as the basis for resource recommendations at application level as well as at cluster node level. Federator.ai supports prediction for both physical/virtual CPUs and memories.
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Auto-scaling via resource recommendation Federator.ai utilizes the predicted resource usage to recommend the right number and size of pods for applications. Integrated with Datadog's WPA, applications are automatically scaled to meet the predicted resource usage.
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Application-aware recommendation execution Optimizing the resource usage and performance goals, Federator.ai uses application specific metrics for workload prediction and pod capacity estimation to auto-scale the right number of pods for best performance without overprovisioning.
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Multi-cloud Cost Analysis With resource usage prediction, Federator.ai analyzes potential cost of a cluster on different public cloud providers. It also recommend appropriate cluster nodes and ins
Highlights
- Up to 70% resource savings
- Increased operational efficiency
- Reduced manual configuration time with digital intelligence
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Delivery details
FederatorAI-Operator v5.1.1
- Amazon EKS
- Amazon EKS Anywhere
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Supported Metrics Data Sources
- Prometheus
- Datadog
- Sysdig
- VMware vCenter
- AWS CloudWatch
Supported Platforms
- Kubernetes v1.16.x or later
- Red Hat OpenShift v4.6-4.9
- Amazon AWS/EKS
- Google GCP/GKE
- Microsoft Azure/AKS
- Rancher v2.4.8, 2.5.8, 2.5.9, 2.6.3
- VMware vCenter 5.5, 6.0, 6.5, 6.7, 7.0
- IBM Cloud/IKS
- Alicloud
Enhancements in Release 5.1
- Alert notifications monitor clusters, nodes, namespaces, applications, and controllers for a variety of conditions, based on predicted usage. Alerts can be emailed when triggered in addition to being viewed in the portal.
- Automatically discover all namespaces and controllers in a cluster to simplify the process of adding an application.
- Provide the ability to back up and restore the system configuration.
- Define custom price books to calculate hourly operating costs for CPU, memory, and storage for onpremises clusters.
- Support Spot instance recommendations for local VM clusters and AWS clusters without AWS Auto Scaling groups.
- KEDA integration enables Horizontal Pod Autoscaling (HPA) in remote Kubernetes clusters to scale the number of replicas of a container based on the recommendations of Federator.ai.
- Display the CPU, memory, network bytes received, and number of network bytes transmitted resource distribution among different controllers and cluster nodes.
- Various UI enhancements including the addition of tabs on the Predictions and Planning pages to select the resource level.
Additional details
Usage instructions
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Subscribe to the product
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To install Federator.ai, run the command in kubectl client environment with cluster admin privilege. export ECR_URL="709825985650.dkr.ecr.us-east-1.amazonaws.com/prophetstor-data-services/federator-ai:v5.1.1-b2164"; curl -sL https://raw.githubusercontent.com/containers-ai/prophetstor/master/deploy/federatorai-launcher.sh | bash
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Add Inbond Rule TCP/31012 in the Security Group to allow the access of port 31012 and obtain the URL/IP address from the Amazon Container Services console to access the UI later.
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Access the UI using the random generated password in the output of the installation. You can now access GUI through https://<YOUR IP/URL>:31012 The default login credential is admin/
*Refer to the documents for more information:
https://www.prophetstor.com/wp-content/uploads/datasheets/Federator.ai.pdf https://prophetstor.com/wp-content/uploads/documentation/Federator.ai/Latest%20Version/ProphetStor%20Federator.ai%20Quick%20Installation%20Guide.pdf https://prophetstor.com/wp-content/uploads/2022/05/ProphetStor-Federator.ai-v5.1-Installation-Guide.pdf https://prophetstor.com/wp-content/uploads/2022/05/Federator.ai-5.1-User-Guide.pdf https://prophetstor.com/wp-content/uploads/2022/05/Federator.ai-5.1-Release-Notes.pdf
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ProphetStor provides 24x7x365 support through email. support@prophetstor.com
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Best tool for cloud migration on multi-cloud
Good experience
Actually, I like its features and most of its design in the console to provide information.