StormForge

🙌Ready to finally start autonomously #rightsizing those #Kubernetes workloads? Kick off your 30-day trial of StormForge Optimize Live right now and your initial recommendations will start to populate within one hour! 👉 https://lnkd.in/eArrbaya

🚀 Deconstruct Kubernetes Autoscaling Use Cases 🚀 Dive deep into the intricacies of this critical aspect of container orchestration with special guest Rodrigo Bersa from Amazon Web Services (AWS). Join us to: - Understand the three key autoscaling dimensions: cluster, horizontal, and vertical - Discover how to tailor autoscaling strategies to specific use cases - Best practices to optimize deployments for peak performance and cost-efficiency for dynamic workloads 🗓 Date: July 24 🕒 Time: 12pm ET | 9am PT 🔗 Link in comments to sign up 👇 #Kubernetes #k8s #Autoscaling #AWS #platformengineering

What percentage of over-provisioning do you think you have in your k8s environment? Nicholas Walker breaks down how you can accurately find out.

Take a deep dive into the Kubernetes Vertical Pod Autoscaler (VPA) in the latest edition of the Kubernetes Optimization Digest.

What's your approach to Kubernetes autoscaling? The reality is that there’s no one-size-fits-all. Fitting the right pieces together requires a deep understanding of the various autoscaling dimensions and the open-source projects that can plug in to meet specific use cases. Get detailed guidance through this highly technical approach on this webinar with Amazon Web Services (AWS)'s Rodrigo Bersa and StormForge's CTO John Platt and Erwin Daria. They will provide a short overview of the three key autoscaling dimensions (cluster, horizontal, and vertical) before diving into how each can be leveraged to navigate different use cases. You’ll learn: 💡 Strategies for each autoscaling dimension, their challenges, and how advanced open-source projects like Karpenter and KEDA can improve on or replace them 💡 How to assess workload requirements to choose the right mix of autoscaling tailored to specific app needs 💡 Best practices to optimize deployments for peak performance and cost-efficiency for dynamic workloads Grab the link to join in the comments 👇

What is a platform team to do when the business is asking them to reduce waste in infrastructure spending and app teams have too many things on their plates to really prioritize cost savings measures?

Acquia needed to efficiently allocate compute capacity for the tens of thousands of customer applications their Digital Experience Platform has running at any given time. Each customer environment is essentially a snowflake with different code bases, profiles, traffic, etc. The resources needed to serve traffic from one customer aren’t like any other.  Watch Ed Brennan describe the highly variable nature of their environment and how they rightsized infrastructure when migrating from virtual machines to Kubernetes. Get the full story by following the link in the comments.

There’s no one-size-fits-all approach to Kubernetes autoscaling. Fitting the right pieces together requires a deep understanding of the various autoscaling dimensions and the open-source projects that can plug in to meet specific use cases. Get detailed guidance through this highly technical approach with experts from Amazon Web Services (AWS) and StormForge on our next webinar. Rodrigo Bersa, John Platt, and Erwin Daria will provide a short overview of the three key autoscaling dimensions (cluster, horizontal, and vertical) before diving into how each can be leveraged to navigate the specs associated with different use cases. Sign up to join the discussion live on July 31. https://lnkd.in/eEYgVTEW

Kubernetes Event-Driven Autoscaling (KEDA) is an advanced open-source project, that builds on the HPA to provide significantly more flexibility, easy-to-use options for various metrics out of the box, and the important ability to scale applications to zero. KEDA configures the HPA to manage pod activity effectively, ensuring responsiveness remains consistent while accommodating dynamic environments. This makes KEDA particularly useful for workloads that see fluctuating traffic or spikes due to events. KEDA offers a more nuanced approach to autoscaling that aligns with modern, event-driven application demands. KEDA was designed to extend the horizontal autoscaling capabilities of Kubernetes, enabling precise and more efficient scaling decisions. Its scaling actions are based on various event sources and metrics, addressing the critical challenges that HPA faces in diverse operational contexts. Learn more about in this guide 👉 https://lnkd.in/dGYvPhZr

What phase are you at on the Kubernetes Resource Management Journey?