Carbon Relay’s Red Sky Ops Machine Learning Now Tunes Horizontal Pod Autoscaling to Optimize Application Performance and Scale

 Carbon Relay’s Red Sky Ops Machine Learning Now Tunes Horizontal Pod Autoscaling to Optimize Application Performance and Scale

 Carbon Relay today announced that Red Sky Ops, a platform for automatically configuring and continuously optimizing containerized applications, has released dynamic resource tracking and pre-baked queries that allows users to understand resource utilization and optimize the Kubernetes Horizontal Pod Autoscaler (HPA) for efficient scale without the risk of performance issues.

Red Sky Ops studies, replicates, and stress-tests Kubernetes applications, and then proactively deploys optimal configurations. The machine learning engine can now tune the Kubernetes HPA, ensuring that the optimized configurations are identified and implemented to handle anticipated and real-time traffic spikes without overprovisioning. Pod size and target utilization are constantly tested and optimized which ultimately results in better application performance and lower costs.

“Until now, managing and maintaining consistent and high application performance and reliability in Kubernetes environments has proven to be complicated, but preparing for application scale introduces an entirely new level of complexity that can’t be addressed by manual tuning,” said Matt Provo, co-founder and CEO of Carbon Relay. “By applying the same machine learning principles that we use to identify optimal application configurations to the HPA, we can deliver a much more effective performance testing experience that ultimately leads to scalable and stable applications.”

Optimizing the HPA’s target metrics for applications and specific workloads can be frustrating, with manual tuning often resulting in the overprovisioning of resources or suboptimal application performance. Tuning the Kubernetes HPA with the Red Sky Ops machine learning engine removes the guesswork from scale preparation. Click here to see an example.  

“Too often we hear of an application failing at the least opportune time simply because it wasn’t prepared for anticipated or even unanticipated spikes in traffic. For a retail application to crash in the midst of a Black Friday sale for example is a disaster, and this entirely possible scenario is avoidable,” said Thibaut Perol, Ph.D., Lead Machine Learning Scientist at Carbon Relay. “A machine learning-powered experimentation engine such as Red Sky Ops can help create a highly available, scalable, and cost-efficient application and removes the uncertainty from your application’s most impactful spikes.”

Click here to learn more about how to get started with Red Sky Ops and optimize your applications at scale.


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