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Organizations adopt Kubernetes for many reasons – some of which are its portability, flexibility, multicloud capability and proven ability to increase productivity.
Yet as the popular open-source orchestration platform is being increasingly deployed in the enterprise realm, Kubernetes is creating new and unexpected complexities. And this is only being compounded by the explosion of data.
“Kubernetes is growing rapidly because of the business benefits it enables – greater agility, faster innovation and effortless scalability,” said Dave Keil, member of the board of directors at StormForge, which specializes in cloud-native application performance testing and resource optimization. “It’s an incredibly flexible technology for running cloud-native applications, but we frequently see that with this flexibility comes increased complexity.”
This, in turn, results in rising costs: 68% of respondents to a recent survey conducted by the Cloud Native Computing Foundation (CNCF) said that their cloud and Kubernetes-related bills have increased. Of those, half saw costs jump more than 20% over the past year.
Making Kubernetes easier
While, at its simplest definition, Kubernetes automates manual processes involved in managing containerized applications, the process – and the platform itself – are anything but simple. For this reason, an increasing number of tools are being offered to unravel its complexities.
Companies including Xtivia, Densify, Cast AI and StormForge are staking their claim in the growing segment. For its part, the Cambridge, Mass.-based StormForge aims to help improve the efficiency of enterprise production environments with the launching of its StormForge Optimize Live.
Chris Aniszczyk, CTO at CNCF, underscored the importance of such platforms: “Costs are often mitigated by how well companies are able to accurately and effectively monitor Kubernetes costs, predict those costs and instill processes that can curtail unnecessary overspend,” he said. “Intelligent and automated solutions like those we see coming from StormForge and others can help optimize cloud native infrastructure and reduce unnecessary spending. We’re encouraged by these technological advances.”
StormForge embraces machine learning
The StormForge platform’s patent-pending machine learning (ML) model analyzes existing observability data to recommend real-time configuration changes. This reduces resource usage and cost while maintaining application performance, according to CEO and founder Matt Provo. Optimize Live is part of the existing StormForge platform, which Provo said now closes the loop between pre-production and production optimization to help organizations maximize their Kubernetes ROI.
The platform’s ML is purpose-built for Kubernetes and lays the foundation to optimize the entire Kubernetes stack, Provo said, including application, pod and container. It draws performance insights on all data collected to inform and optimize cloud native environments.
ML is applied to data that enterprises are already collecting so they can “turn observability into actionability” with recommendations for configuration changes, Keil said. The ability to optimize multiple parameters for multiple, competing dimensions “allows customers to better understand the inherent tradeoffs and to make intelligent business decisions,” he said.
The company claims that platform users have seen 40% to 60% cost savings and 30% to 50% performance increases.
“We’ve been able to provide visibility into the applications performance and ecosystem like we haven’t seen before,” Provo said. “We’re showing you what’s going wrong, or what went wrong.”
Optimize Live runs in any CNCF-certified distribution, includes automated optimization with one-click deployment for production optimization and rapid experimentation, and leverages existing observability and cost data to deliver insights and optimization.
“This is how we all realize the promise of Kubernetes and cloud native,” said Provo.
Keil pointed out that, as more organizations scale up their Kubernetes environment for day 2 operations, StormForge only expects accelerated complexity.
Several key gaps will continue to challenge organizations in 2022, he said. First and foremost, organizations will continue to move more and more workloads into production on Kubernetes; however, while the platform will offer new levels of flexibility, downstream complexities will often slow down deployments.
There will also continue to be a gap in data-to-value, in which the amount of data collected by enterprises will continue to grow exponentially, making it more and more difficult to draw insights and act on that information, Keil said. Finally, a cloud native skills gap will accelerate and will soon hit a “crisis point” for many large organizations.
“All three of these factors will continue to accelerate demand for optimization solutions,” Keil said.
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