Closing the Kubernetes Complexity Gap with Machine Learning Optimization at February 23 Event

When they first met, cloud computing and Kubernetes seemed like the perfect match. Today, with nearly three-quarters of organizations running Kubernetes workloads in production, the honeymoon phase is over.

Kubernetes is excellent for container orchestration; however, its complexity and lack of transparency lead to security issues, skyrocketing cloud costs and deployment delays, and developer frustration and burnout.

“In 2022, I expect many organizations to hit a ‘cloud native wall,’ where complexity challenges become too great to handle manually,” predicted Matt Provo, CEO of StormForge, in a post. recent blog post.

On February 23 at 2:00 p.m. EST, StormForge and theCUBE, SiliconANGLE Media’s livestreaming studio, present the “Solving the Kubernetes Complexity Gap by Optimizing with Machine Learning” event, which will highlight improving cloud-native production environments with machine learning. (*Disclosure below.)

Kubernetes is suffering from a complexity crisis

Invariably, companies hit the “moment of panic,” according to JR Storment, Executive Director of the FinOps Foundation in an interview with theCUBE

“They realize that they are initially spending a lot more than expected,” he said.

More importantly, companies don’t have the people, processes, or tools in place to be able to track Kubernetes spend and figure out how to optimize their cloud costs, Storment added. The FinOps Foundation and Cloud Native Computing Foundation “FinOps for Kubernetes” micro-survey found that while Kubernetes-related spending increased for 68% of companies in 2020, only 6% could accurately predict their monthly cloud bill before its arrival.

The problem is the complexity and lack of transparency inherent in Kubernetes, according to Provo. Although Kubernetes offers developers great flexibility in terms of container management, there is no visibility into the effect on applications when operational parameters are adjusted.

“Often, organizations become more agile [and] ship code faster, but all of a sudden the cloud bill comes in and they’ve overprovisioned by 80, 90%,” Provo told theCUBE at KubeCon + CloudNativeCon 2021.

This waste of the cloud is not only a problem of result. It is estimated that data centers emit almost 100 million metric tons of CO2 per year, which means that in addition to wasting financial resources, the unnecessary use of data centers caused by over-provisioning has a significant environmental impact. .

Then there is security. In the “State of Kubernetes Security” report, Red Hat Inc. found that security was the number one container strategy concern, with more than half of respondents experiencing delayed deployment due to security issues. Misconfiguration tops the list of security flaws, a problem compounded by the well-documented lack of Kubernetes skills. There are currently over 67,000 open positions for Kubernetes-trained engineers on, and the “Kubernetes and Cloud-Native Operations Report 2021” listed “lack of internal skills/limited workforce “as the biggest challenge” when migrating. to/using Kubernetes and containers. »

Adding to the problem, trained Kubernetes engineers burn out and quit. Fifty-one percent of respondents to a 2020 study said they wanted to find a new job.

Machine learning can reduce the carbon footprint of data centers

StormForge was created as Carbon Relay in 2019 with the aim of optimizing energy consumption in data centers. This mission quickly extended to testing and optimizing the performance of cloud-native applications based on machine learning. And in 2020, after purchasing performance testing platform as a service StormForger, the company rebranded and released its automated Kubernetes optimization platform.

However, optimizing data center power consumption is still high on the list of business priorities. StormForge has an ongoing cloud waste reduction challenge where it commits to reducing its customers’ cloud waste bills by at least 30% or paying their cloud bill for them.

Rather than application performance management, StormForge defines itself as an intelligence platform. Instead of competing with APM, monitoring and observability solutions, such as DataDog, Sysdig, Sumo Logic, VMware Tanzu Observability, Dynatrace, New Relic, Prometheus, Splunk and AppDynamics, StormForge has worked to integrate its platform with these companies and counts them within its ecosystem. This means customers don’t have to switch from their existing management tools to benefit from the StormForge platform.

“We have machine learning capabilities that can very accurately predict what organizations will need from a resource perspective to achieve their goals, not only from a cost perspective, but also from a performance perspective,” said Provo.

StormForge closes the gap with Kubernetes

The product announcement to be made at the February 23 event “Solving the Kubernetes Complexity Gap by Optimizing with Machine Learning” leverages StormForge’s expertise in machine learning to enable optimization automation of Kubernetes. The promise is to remove the barriers discussed at the beginning of this article and make it easier for enterprises to use Kubernetes in production.

According to Provo, three key shortcomings prevent Kubernetes and cloud-native technology from reaching their full potential. These are complexity, data overload and lack of skills. The three combine to form a perfect storm that leads to inefficiencies in managing Kubernetes. StormForge promises its new platform will solve all three of these problems, using its machine learning intelligence to automatically optimize Kubernetes in pre-production and production, eliminate manual trial-and-error application tuning, and make the expense transparent and easy-to-follow cloud-related.

“2022 will be the year we get closer to closing the gap in complexity, data and skills,” predicts Provo. “But it’s going to take work, and it requires sophisticated machine learning and automation.”

Live stream of StormForge’s “Solving the Kubernetes Complexity Gap by Optimizing With Machine Learning” event

StormForge’s “Solving the Kubernetes Complexity Gap by Optimizing with Machine Learning” event will feature interviews that will be streamed on theCUBE. Add this event to your calendar to watch the event live. Additionally, you can watch theCUBE interviews here on demand after the live event.

How to watch theCUBE interviews

We’ve got you covered with multiple ways to watch StormForge’s “Solving Kubernetes Complexity Problem by Optimizing with Machine Learning” event, including theCUBE’s dedicated website and YouTube channel. You can also get all of this year’s event coverage on SiliconANGLE.

TheCUBE Insights Podcast

SiliconANGLE also offers podcasts of archived interview sessions, available on iTunes, Stitcher, and Spotify, for you to enjoy on the go.


At StormForge’s “Solving the Kubernetes Complexity Gap by Optimizing With Machine Learning” event, theCUBE will speak with Matt Provo, Founder and CEO of StormForge, and Charley Dublin, Vice President of Product Management at Acquia Inc.

(*Disclosure: TheCUBE is a paid media partner for the “Solving the Kubernetes complexity gap by optimizing with machine learning“an event. Neither StormForge, theCUBE’s event coverage sponsor, nor other sponsors have editorial control over theCUBE or SiliconANGLE content.)

Image: StormForge

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Sharon D. Cole