September 26, 2022

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3 insights you might have missed from the StormForge ‘Solving the Kubernetes Complexity Gap’ event

4 min read

Forty-eight percent of cloud spend is wasted on unused or idle resources, according to the 2021 Cloud Waste Survey conducted by intelligent optimization software provider StormForge. Cloud and Kubernetes complexity is the number one cause, as developers over-provide as a safety net to ensure application performance.

StormForge, a flagship platform provider at Gramlabs Inc., has been on a mission to apply real science to the issue of cloud spend waste for the better part of a decade. This week on the CUBE, SiliconANGLE Media’s livestreaming studio, StormForge announced a major buildout of its intelligent automation platform with the release of Optimize Live.

In case you missed watching the event as it aired, here are three key insights from the “Solving the Kubernetes Complexity Gap by Optimizing With Machine Learning” event. (* Disclosure below.)

1) StormForge machine learning is more than marketing hype

Machine learning is core to StormForge’s purpose. The company has a workforce of data scientists, machine-learning experts, and DevOps engineers focused on solving the practical problem of managing Kubernetes workloads with artificial intelligence. Optimize Live is the latest product to come from their work and is billed as providing “ML-powered multi-dimensional optimization.”

It’s easy to view that claim as sales hyperbole. But company founder and Chief Executive Officer Matt Provo emphasized that the ML behind StormForge’s ability to optimize cloud-native production environments is the real deal, not a marketing hook.

“I’m fortunate to have found a number of folks from the outset of the company with Ph.Ds in applied mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems,” he said.

Managing Kubernetes workloads from a resource efficiency standpoint is a “tailor-made for the use of real machine learning” problem according to Provo.

Charley Dublin, vice president of product management at Acquia Inc. – and Optimize Live user – agreed. “The logic and decision-making and insights are outstanding. So we can get to the best decision, the optimal decision much more quickly, ”said Dave Vellante, host of theCUBE, during the event.

Watch the complete video interview with Charley Dublin below:

2) Optimize Live leverages data from existing observability solutions

Optimize Live is flexible, working with any cloud-native environments built on any Cloud Native Computing Foundation-certified Kubernetes distribution. Rather than competing with players in the application performance monitoring and observability market, Optimize Live’s patent-pending machine learning uses the data companies are already obtaining from existing solutions, such as Datadog, Prometheus and AppDynamics.

Optimize Live takes that data, runs it through its ML, and comes up with recommendations to improve Kubernetes CPU and memory settings. These can be set to be either approved by an engineer or automatically applied depending on user preference. A patch is then deployed into the production environment, and the cycle repeats.

Image: StormForge

“From a time-to-market perspective, we’re able to get to market with a higher-performing, more cost-effective solution earlier,” Dublin said. “What we get is resource efficiency and our network and platform efficiency. We’re not over-allocating a capacity that costs us more money than we should. We’re not under-allocating capacity that could have a lower performance solution for our customers. “

3) StormForge offers pre-production and production optimization in a single platform

Optimize Live builds upon StormForge’s existing Optimize Pro platform, which provides experimentation-based optimization for pre-production environments. A StormForge blog comparing the two solutions shows that Optimize Live uses an observation-based optimization approach and works across all Kubernetes applications in production environments.

The solution addresses the needs of the ongoing, and dramatic, rise in Kubernetes adoption. This has been driven by the increased importance of cloud-native applications to business operations and accompanied by a move towards hybrid cloud and edge computing strategies. In 2021, Red Hat Inc. reported that 74% of companies were running Kubernetes in production, and the number of production projects that use Kubernetes is estimated to increase by approximately 60% by the start of 2023, according to a study by enterprise Kubernetes management software company D2iQ Inc.

“My vision has been for us to be able to close the loop between data coming out of pre-production and the associated optimizations and data coming out of a production environment and our ability to optimize that,” Provo said.

Optimize Live fulfills Provo’s vision. Together, Optimize Pro and Optimize Live create a platform that addresses the complexity gap encountered by companies as they move into day two of their cloud-native operations.

Watch the complete video interview with Matt Provo below:

Watch SiliconANGLE’s and theCUBE’s complete coverage of the StormForge “Solving the Kubernetes Complexity Gap by Optimizing With Machine Learning” event on theCUBE’s dedicated event channel. (* Disclosure: TheCUBE is a paid media partner for the “Solving the Kubernetes Complexity Gap by Optimizing With Machine Learning” event. Neither StormForge, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Image: PopTika

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