Container adoption is growing, and Kubernetes is becoming the de facto standard of container management platforms. Whether container adoption occurs on-premises, in public clouds, or both, the operational overhead is enormous. IT administrators cannot foresee computing resource demands of applications, so they must reserve more computing resources for a workload than needed. Managing computing resources and optimizing costs on multiple clouds are daunting tasks. Federator.ai, ProphetStor’s Artificial Intelligence for IT Operations (AIOps) platform, provides intelligence to orchestrate container resources on top of VMs (virtual machines) or bare metal, allowing users to operate applications without the need to manage the underlying computing resources.
After Federator.ai is deployed in a Kubernetes environment, it learns application resource usage patterns and predicts the needed resources on a per namespace level down to the container level. Federator.ai also provides a dashboard that displays the per-application workload and resource recommendations. Federator.ai delivers the following key features:
Applies multiple analytics tools, such as machine learning and signal processing, to predict resource usage for Kubernetes objects at different levels: clusters, nodes, namespaces, applications, and controllers. The predictions of resource usages are the basis for resource recommendations for these Kubernetes objects. Federator.ai supports both physical and virtual CPUs and memories.
Federator.ai utilizes resource usage prediction based on workload patterns to recommend the right amount of CPU and memory at different levels of Kubernetes objects. It helps reducing significant wasted resources for overprovisioned clusters and applications or ensuring enough resources for the increased workload demands.
With application-specific workload predictions, Federator.ai automatically scales the right number of replica’s for containers at the right time to maintain the performance goals of applications.
Recommends the most cost-efficient cluster configuration and instance types from each major public cloud service providers for on-prem clusters or clusters on the public cloud.
Complete analysis for potential savings for overprovisioned applications and estimated additional cost for underprovisioned applications.
Over-provisioned computing resources and the deployment of the incorrect number and/or size of VMs and/or pods are two common issues in a cloud-native environment. Federator.ai addresses these problems by orchestrating resources in multi-cloud environments. As shown in the Figure, Federator.ai optimizes costs for both Day-1 deployment and Day-2 operations. It utilizes metrics stored on Prometheus, collected by OpenShift, to predict resource consumption dynamically and recommends the right amount of resources for pods, providing a 20 – 70% reduction of wasted resources for a typical workload, as well as preventing under-provisioning of resources for mission-critical workloads. Users can stack up the predicted pod resources to determine the right number and size of VMs to deploy and enable the automatic execution of these recommendations.
With Federator.ai, users no longer need to specify the CPU and memory requests and limits for each container. It recommends optimal pod configurations. The direct effect is that the configured resources will accurately and dynamically match the workload. It also effectively reduces occurrences of under-provisioned issues, such as out-of-memory (OOM).
Federator.ai Feature DEMO
Red Hat Summit 2019 Keynote Presentation
AIOps on OpenShift with Sunny Siu and Tushar Katarki
Federator.ai aims to provide optimal resource planning recommendations that will help enterprises make better decisions. The benefits of Federator.ai include:
- Up to 70% resource savings: Federator.ai mainly serves to reduce unnecessary spending and increase application service quality for both enterprises and cloud providers. ProphetStor data scientists and engineering teams work together to build the most advanced AIOps solution to reduce resource wastage at different infrastructure layers. With the help of patented prediction technologies, Federator.ai simultaneously reduces spending and delivers the necessary performance.
- Increased operational efficiency: Federator.ai frees users from continuously monitoring OpenShift cluster utilization and cloud spending. Users also do not need to manually record usage data, calculate optimal configurations, and change configurations based on the calculations. These tasks are routinely accomplished when using Federator.ai.
- Reduced manual configuration time with digital intelligence: Federator.ai allows users to turn on the optimization engine any time. Federator.ai will re-configure pods with the right values at the right time.