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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., 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.


Main Features

After is deployed in an OpenShift environment, it learns application resource usage patterns and predicts the needed resources on a per namespace level down to the container level. also provides a dashboard that displays the per-application workload and resource recommendations. for OpenShift delivers the following key features:

  • Multi-layer workload prediction

    Applies multiple analytics tools, such as machine learning and signal processing, to predict containerized application and node resource usage as the basis for pod resource recommendations. supports both physical and virtual CPUs and memories.

  • Application-aware recommendation execution

    The application resource demand determines the number and size of pods. utilizes resource usage prediction based on workload patterns to recommend the right pod sizes. 

  • Policy-driven planning of CPU and memory

    Plans cluster-wide CPU and memory allocation for different types of applications according to the policy specified by users.

  • Enterprise-ready

    Designed to work with any OpenShift-operated environment. provides application lifecycle management based on the Operator Framework and works seamlessly with Red Hat OpenShift.

  • Easy installation

    Installing is easy as it works as an Operator on OpenShift. 

  • Continuous recommendations for optimal resource planning

    Continuously generates recommendations and learns better with more accumulated metrics data.®

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. addresses these problems by orchestrating resources in multi-cloud environments. As shown in the Figure, 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, 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).

Dashboard for visualizing prediction and recommendation

Red Hat Summit 2019 Keynote Presentation

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AIOps on OpenShift with Sunny Siu and Tushar Katarki

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Benefits aims to provide optimal resource planning recommendations that will help enterprises make better decisions. The benefits of include:
  • Up to 70% resource savings: 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, simultaneously reduces spending and delivers the necessary performance.
  • Increased operational efficiency: 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
  • Reduced manual configuration time with digital intelligence: allows users to turn on the optimization engine any time. will re-configure pods with the right values at the right time.