StormForge Presents at Cloud Field Day 13

Solving the Kubernetes Efficiency Problem with StormForge

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As enterprises scale up their cloud native environments for day 2 operations, cloud waste and inefficiency often become a significant problem. The business impacts of inefficiency include cloud costs that are too high, risk of performance and availability issues that affect user experience, and pulling developers away from innovating to spend time on manual tuning of Kubernetes applications. In this session, we provide an overview of the StormForge platform for Kubernetes optimization, and how it improves efficiency by applying machine learning for both non-production experimentation and scenario analysis as well as production optimization leveraging observability data already being collected. Presented by Rich Bentley, VP of Product Marketing, StormForge.

Personnel: Rich Bentley

Using Machine Learning for Kubernetes Optimization: StormForge Product Demo

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The StormForge platform uses patent-pending machine learning to automate the process of achieving and maintaining efficiency of cloud native environments running on Kubernetes. As the only solution that combines experimentation-based, non-production optimization with observation-based optimization in production, StormForge helps DevOps teams to incorporate optimization as a systematic, continuous process that closes the loop between production and non- production and turns observability into actionability. In this session, we walk through the StormForge platform, showing you how easy it is to tackle the problems of cloud waste, inefficient resource utilization, performance and availability risk, and time-consuming manual application tuning. Presented by Patrick Tavares, Sr. Solutions Architect, StormForge.

Personnel: Patrick Tavares

Kubernetes Optimization at Acquia: An Example StormForge Use Case

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Acquia is the largest provider of hosting capabilities for Drupal, with more than 4,000 organizations using the company’s solutions for content, community and commerce. To continue meeting customer needs and optimize the performance, scalability and cost efficiency of its core hosting offering, Acquia decided to move to a Kubernetes container-based platform. This major re-platforming effort would affect 2500+ customers and tens of thousands of applications. With this move, Acquia needed the ability to right-size resource capacity to support customer applications, while also scaling as needed based on each application’s consumption requirements. With each customer environment being unique, manual application tuning wasn’t feasible due to the massive amount of time and effort that would be required. In this session, we show how Acquia was able to automate the process of achieving and maintaining Kubernetes resource efficiency at scale. With the ability to forecast demand, and then make resource decisions around application configuration based upon the forecast, Acquia can now effectively manage applications and meet their customers’ evolving requirements. Presented by Erwin Daria, Director of Technical Alliances, StormForge.

Personnel: Erwin Daria