#11: FinOps 101 with the AWS Optics team (Part 2): Committing Wisely

#14: How Sonos Mastered Spot: Karpenter GA, KubeCon & More

Karpenter has achieved GA and is disrupted the autoscaling game, with data pointing to accelerated adoption.  Today, Josh Cypher, DevOps leader at Sonos, joins us to talk about some unexpected byproducts of adopting Karpenter at Sonos. Josh dives into his favorite features and efficiency gains, from node consolidation to better disruption controls. The public cloud bill is a massive operational expense for tech organizations, yet tracking the success of optimization efforts often frustrates engineers. Josh and James explore these challenges and how to address them effectively. As big-time Spot adopters, Sonos has unlocked impressive savings (50%?!) by focusing on high-impact, low-overhead strategies. Josh explains how visibility brought them quick wins and paved the way for further optimization across Sonos’s infrastructure. Plus, Josh and James preview what they’re looking forward to at KubeCon, including key conversations on Kubernetes, AI, and cloud sustainability.

#13: Multidimensional Pod Autoscaling & Machine Learning for Cloud Optimization

Dr. Haoran Qiu, a fresh PhD from the University of Illinois Urbana-Champaign, joins our host James Wilson, VP of Engineering at nOps. They’re diving into multidimensional autoscaling, an area in which Haoran’s pioneering research is making waves in the Kubernetes community.  Some workloads work better with Horizontal Pod Autoscaler (HPA), others with Vertical Pod Autoscaler (VPA).  Running them together can create conflicts, but using only one limits efficiency gains. A Multidimensional Pod Autoscaler solves this dilemma by combining the benefits of both VPA and HPA to dynamically adjust both the number and size of pods. But is MPA poised to redefine resource optimization? What problems does it solve, and what fresh complexities are involved in its implementation? Haoran and James dig into these questions while debating traditional heuristic versus Machine Learning approaches, industry versus academia, and other hot topics in Kubernetes. Listen now to discover if MPA is the holy grail of cloud optimization as we discuss the evolution of autoscaling technologies and their impact on cost, sustainability, and developer experience. Chapters: 0:00 - 2:20: Haoran Chu and the state of cloud resource management 2:20-6:00: Historical evolution of autoscaling 6:01 - 10:45: HPA, VPA and Multidimensional Autoscaling 10:46 - 18:50: Challenges of MPA: heuristics versus machine learning 18:51 - 24:20: How to quantify excess capacity? 24:21 - 32:16: The state of ML in autoscaling 32:16 - 37:37: Operationalizing ML in production environments 37:37 - 42:01: The near-term future of autoscaling

#12: Optimizing for Sustainability

Tech thought leader and host of the Kubernetes Unpacked podcast Kristina Devochko joins nCast today to talk all things cloud cost optimization, Kubernetes and green tech.  We start by talking about the fact that many companies aren’t even using HALF of their compute resources. But does slashing your AWS bill necessarily mean that you’re saving the plant? We delve into cost optimization and how it aligns (or not) with sustainability.  Kristina shares her insights on measuring your cloud carbon footprint and the tools you need (KEDA, Karpenter, Kepler) to increase cloud sustainability. We discuss key practical ways to get started cutting unnecessary cloud waste, from eliminating orphaned resources to scheduling during off hours.  Plus, we're revealing how nOps has managed to run our production on Spot instances — talk about recycling!  0:00 - 1:09: Introduction 1:10 - 4:20: Sustainability at Kubecon Europe and other recent events 4:21 - 9:31: Is cost optimization the same as sustainability? 9:32 - 12:53: Green data centers and your carbon footprint 12:54 - 15:21: Portability and the downsides of over-committing to pricing plans 15:22 - 19:51: Measuring your organization’s cloud sustainability 19:53 - 26:51: KEDA, Karpenter, Kepler and the tools you need 26:52 - 31:12: Leveraging available Spot capacity and choosing instances 31:13 - 37:15: Running production environments on Spot  37:15 - 44:46: Continual rightsizing and automated tools 44:47 - 48:18: Carbon-efficient Karpenter scaling Show notes * GitHub issue [https://github.com/aws/karpenter-provider-aws/pull/4686%20and%20https://github.com/kubernetes-sigs/karpenter/issues/675] for proposal of carbon-efficient design to Karpenter that needs some community support * Kepler [https://sustainable-computing.io] project * Carbon-aware KEDA operator [https://github.com/Azure/carbon-aware-keda-operator] * Cloud Carbon Footprint [https://www.cloudcarbonfootprint.org] open source tool * BoaviztAPI [https://github.com/Boavizta/boaviztapi] open source API for environmental impacts of ICT * APIs that provide electricity data, data on carbon emissions and electricity sources: https://app.electricitymaps.com [https://app.electricitymaps.com/] and https://watttime.org [https://watttime.org/] * CNCF TAG Environmental Sustainability [https://tag-env-sustainability.cncf.io] * Contact Kristina Devochko [https://www.biodrop.io/guidemetothemoon] * Kristina Devochko’s Tech blog [https://www.kristhecodingunicorn.com]

#11: FinOps 101 with the AWS Optics team (Part 2): Committing Wisely

Today we’re joined by Wade Piehl, Senior FinOps Success Manager on the Optics Team at AWS, to discuss all things On Demand, Reserved, and Spot.  Good purchasing decisions have an enormous impact on your bottom line — but how do you know what and how much to buy? We walk through the step-by-step decision-making process and the most common pitfalls that can cost you. Wade shares all the practical advice you need to decide between Reserved Instances vs. Savings Plans. Find out how the equation changes from compute spend to database, storage and networking. We’ll also dig into strategies like layering purchase plans, leveraging Spot, and other methods of maximizing your savings.

#10: FinOps 101 with the AWS Optics team (Part 1): Automating Governance

Today we’re joined by Savanna Jensen, Senior FinOps Success Manager on the Optics Team at AWS, to discuss how to implement a Cloud FinOps automation strategy. The less amount of “people time” you can dedicate to cloud management and the more automation you can bake into the system, the easier it will be — but what are the right tools to use? We start out by tapping Savanna’s insider knowledge on the latest and greatest AWS Cost Management tools. Get the latest on the shiny new updates to Cost Explorer that just launched. Plus, pro tips on the best filters and features to use for various use cases when it comes to the Cost and Usage Report (CUR), Cost Explorer, and QuickSight. We dive into Engineering vs. FinOps perspectives and frustrations. How can you orchestrate a culture where cost optimization feels motivating rather than punitive to Engineers (recognition, career rewards, gamification…)? How do you troubleshoot if you’re a FinOps leader seeing zero traction on your initiatives? Hear real-world battle stories about organizations tackling cost management challenges — and the takeaways for achieving true visibility and control over cloud costs. And stay tuned next week for Part 2 with the AWS Optics team.