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KubeCon 2021 Q&A: StormForge Will Showcase How Its Platform Uses Machine Learning to Ensure Kubernetes Application Efficiency


KubeCon + CloudNativeCon 2021.  Will you be in attendance?  If so, VMblog invites you to swing by and check out the StormForge booth in the sponsor showcase.

Read this exclusive KubeCon pre-show interview between VMblog and Rich Bentley at StormForge.  StormForge brings together world-class data scientists and software engineers to enable businesses to drive breakthrough IT and operations efficiency. The StormForge Platform uses enterprise grade performance testing coupled with machine learning to drive major application performance gains and cost reductions in complex environments. 

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VMblog:  What do you attribute to the success and growth of this industry and the KubeCon event itself?

Rich Bentley:  Kubernetes has tremendous potential to help enterprises transform and modernize their application infrastructure to increase velocity, improve user experience, and scale more easily. But realizing the promise of Kubernetes is not easy. KubeCon is a great opportunity for people to learn from the community and see how others have tackled the challenges and achieved the value of Kubernetes.

VMblog:  How does your company or product fit within the container, cloud, Kubernetes ecosystem?

Bentley:  StormForge is an optimization platform for applications that run on Kubernetes. While there are tools that give you visibility into performance (e.g. observability tools) and cost (e.g. cost management tools), they don't necessarily tell you what to do about issues that they find. StormForge fills that gap by looking at both cost and performance, and then recommending the Kubernetes configuration that will result in optimal efficiency of your application. We fit in with CI/CD tools in your ecosystem to make continuous optimization a regular part of the deployment process.

VMblog:  Can you give us the high-level rundown of your company's technology offerings?  Explain to readers who you are, what you do, what problems you solve, etc.

Bentley:  As mentioned earlier, Kubernetes promises to transform the way applications are built and delivered, but it's also very complex. This complexity results in inefficient Kubernetes and application configurations costing millions of dollars in wasted cloud resources, along with business-impacting performance and availability issues, and thousands of hours of lost productivity every year. StormForge uses patent-pending machine learning to automatically find the optimal configurations for your applications before deployment, saving you time and money while ensuring application performance and resiliency, and allowing your developers to focus on innovation, not tuning Kubernetes.

VMblog: At what stage do you feel we are at with regard to containers?  Is there anything still holding it back?  Or keeping it from a wider distribution?

Bentley:  The complexity that we've talked about is the biggest inhibitor of wider adoption, along with the related skills and resource scarcity given that it's still a relatively new technology. Our goal is to help offload that complexity. Let StormForge worry about configuring your Kubernetes resources in the best way possible while your team focuses on building new and differentiated capabilities that benefit your business.

VMblog: StormForge talks about cloud efficiency beyond just cost savings.  Can you explain?

Bentley:  While there are a lot of tools out there that focus on cloud cost management, we believe that you have to look at the trade-offs between cost, application performance, and the time and effort needed by your team to achieve those outcomes. We focus on helping teams understand those trade-offs and finding the optimal way of deploying apps to minimize cloud costs while still meeting SLOs and maintaining developer velocity.

VMblog: How does StormForge use Machine Learning to help ensure Kubernetes application efficiency?

Bentley:  Machine Learning is perfectly suited to solving complex optimization problems. Kubernetes is a perfect example of this. When deploying an application to run on Kubernetes, your engineers have to set CPU requests and limits, memory requests and limits, and replicas. Multiplied by the number of containers that make up your app and the number of possible combinations is essentially infinite. Machine Learning can do in a matter of hours what would take humans weeks or months - that is to find the configuration that results in the best possible outcomes, in terms of cloud costs and performance.


Published Thursday, September 30, 2021 7:33 AM by David Marshall
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