KubeFM

Running 30 Kubernetes clusters serving 300,000 requests per second sounds impressive until your Vertical Pod Autoscaler goes rogue and starts evicting critical system pods in an endless loop.

Thibault Jamet shares the technical details of debugging a complex VPA failure at Adevinta, where webhook timeouts triggered continuous pod evictions across their multi-tenant Kubernetes platform.

You will learn:

• VPA architecture deep dive - How the recommender, updater, and mutating webhook components interact and what happens when the webhook fails

• Hidden Kubernetes limits - How default QPS and burst rate limits in the Kubernetes Go client can cause widespread failures, and why these aren't well documented in Helm charts

• Monitoring strategies for autoscaling - What metrics to track for webhook latency and pod eviction rates to catch similar issues before they become critical

Sponsor

This episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/rf1pbWXdN

• Interested in sponsoring an episode? Learn more.

This episode unpacks the technical and governance milestones that secured Flux's place in the cloud-native ecosystem, from a 45-minute production outage that led to the birth of GitOps to the CNCF process that defines project maturity and the handover of stewardship after Weaveworks' closure.

You will learn:

• How a single incident pushed Weaveworks to adopt Git as the source of truth, creating the foundation of GitOps.

• How Flux sustained continuity after Weaveworks shut down through community governance.

• Where Flux is heading next with security guidance, Flux v2, and an enterprise-ready roadmap.

Sponsor

Join the Flux maintainers and community at FluxCon, November 11th in Atlanta—register here

More info

• Find all the links and info for this episode here: https://ku.bz/5Sf5wpd8y

• Interested in sponsoring an episode? Learn more.

Jorrick Stempher shares how his team of eight students built a complete predictive scaling system for Kubernetes clusters using machine learning.

Rather than waiting for nodes to become overloaded, their system uses the Prophet forecasting model to proactively anticipate load patterns and scale infrastructure, giving them the 8-9 minutes needed to provision new nodes on Vultr.

You will learn:

• How to implement predictive scaling using Prophet ML model, Prometheus metrics, and custom APIs to forecast Kubernetes workload patterns

• The Node Ranking Index (NRI) - a unified metric that combines CPU, RAM, and request data into a single comparable number for efficient scaling decisions

• Real-world implementation challenges, including data validation, node startup timing constraints, load testing strategies, and the importance of proper research before building complex scaling solutions

Sponsor

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/clbDWqPYp

• Interested in sponsoring an episode? Learn more.

If you're running Java applications in Kubernetes, you've likely experienced the pain of slow pod startups affecting user experience during deployments and scaling events.

Frédéric Gaudet, Senior SRE at BlaBlaCar, shares how his team solved the cold start problem for their 1,500 Java microservices using Istio's warm-up capabilities.

You will learn:

• Why Java applications struggle with cold starts and how JIT compilation affects initial request latency in Kubernetes environments

• How Istio's warm-up feature works to gradually ramp up traffic to new pods

• Why other common solutions fail, including resource over-provisioning, init containers, and tools like GraalVM

• Real production impact from implementing this solution, including dramatic improvements in message moderation SLOs at BlaBlaCar's scale of 4,000 pods

Sponsor

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/grxcypt9j

• Interested in sponsoring an episode? Learn more.

Brian Donelan, VP Cloud Platform Engineering at JPMorgan Chase, shares his ingenious side project that automatically scales Kubernetes workloads based on whether his MacBook is open or closed.

By connecting macOS screen lock events to CloudWatch, KEDA, and Karpenter, he built a system that achieves 80% cost savings by scaling pods and nodes to zero when he's away from his laptop.

You will learn:

• How KEDA differs from traditional Kubernetes HPA - including its scale-to-zero capabilities, event-driven scaling, and extensive ecosystem of 60+ built-in scalers

• The technical architecture connecting macOS notifications through CloudWatch to trigger Kubernetes autoscaling using Swift, AWS SDKs, and custom metrics

• Cost optimization strategies including how to calculate actual savings, account for API costs, and identify leading indicators of compute demand

• Creative approaches to autoscaling signals beyond CPU and memory, including examples from financial services and e-commerce that could revolutionize workload management

Sponsor

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/sFd8TL1cS

• Interested in sponsoring an episode? Learn more.

Data centers consume over 4% of global electricity and this number is projected to triple in the next few years due to AI workloads.

Dave Masselink, founder of Compute Gardener, discusses how he built a Kubernetes scheduler that makes scheduling decisions based on real-time carbon intensity data from power grids.

You will learn:

• How carbon-aware scheduling works - Using real-time grid data to shift workloads to periods when electricity generation has lower carbon intensity, without changing energy consumption

• Technical implementation details - Building custom Kubernetes schedulers using the scheduler plugin framework, including pre-filter and filter stages for carbon and time-of-use pricing optimization

• Energy measurement strategies - Approaches for tracking power consumption across CPUs, memory, and GPUs

Sponsor

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/zk2xM1lfW

• Interested in sponsoring an episode? Learn more.

Alessandro Pomponio from IBM Research explains how his team transformed their chaotic bare-metal clusters into a well-governed, self-service platform for AI and scientific workloads. He walks through their journey from manual cluster interventions to a fully automated GitOps-first architecture using ArgoCD, Kyverno, and Kueue to handle everything from policy enforcement to GPU scheduling.

You will learn:

• How to implement GitOps workflows that reduce administrative burden while maintaining governance and visibility across multi-tenant research environments

• Practical policy enforcement strategies using Kyverno to prevent GPU monopolization, block interactive pod usage, and automatically inject scheduling constraints

• Fair resource sharing techniques with Kueue to manage scarce GPU resources across different hardware types while supporting both specific and flexible allocation requests

• Organizational change management approaches for gaining stakeholder buy-in, upskilling admin teams, and communicating policy changes to research users

Sponsor

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/5sK7BFZ-8

• Interested in sponsoring an episode? Learn more.

Mac Chaffee, a platform engineer and security champion, examines why developers often underestimate the complexity of running modern applications and how overconfidence leads to expensive technical mistakes.

You will learn:

• Why teams reject Kubernetes then rebuild it piece by piece - understanding the psychological factors, like overconfidence, that drive initial rejection of complex but proven tools

• How to identify the tipping point when DIY solutions become more complex than adopting established orchestration tools, especially around scaling and high availability challenges

• The right approach to abstracting Kubernetes complexity - why hiding the Kubernetes API often backfires and how to build effective guardrails instead of reinventing interfaces

• Why mentorship gaps lead to poor technical decisions - how the lack of proper apprenticeship programs in tech results in teams making expensive mistakes when building infrastructure

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/9nFPmG85f

• Interested in sponsoring an episode? Learn more.

Marc Campora, a systems consultant with experience in high-throughput platforms, shares his analysis of a real customer deployment with 500+ microservices. He breaks down the cost implications, technical constraints, and operational trade-offs between Kubernetes containers and AWS Lambda functions based on actual production data and migration assessments.

You will learn:

• Cost analysis frameworks for comparing Lambda vs Kubernetes across different traffic patterns, including specific examples of 3x savings potential and the 80/20 rule for service utilization

• Migration complexity factors when moving existing microservices to Lambda, including cold start issues, runtime model changes, and why it's often a complete rewrite rather than a simple port

• Decision criteria for choosing between platforms based on traffic consistency, computational requirements, and operational overhead tolerance

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/5gMTkzLhV

• Interested in sponsoring an episode? Learn more.

Molly Sheets, Director of Engineering for Kubernetes at Zynga, discusses her team's approach to platform engineering. She explains why their initial one-cluster-per-team model became unsustainable and how they're transitioning to multi-tenant architectures.

You will learn:

• Why slowing down deployments actually increases risk and how manual approval gates can make systems less resilient than faster, smaller deployments

• The operational reality of cluster proliferation - why managing hundreds of clusters becomes unsustainable and when multi-tenancy becomes necessary

• Practical multi-tenancy implementation strategies including resource quotas, priority classes, and namespace organization patterns that work in production

• Better metrics for multi-tenant environments - why control plane uptime doesn't matter and how to build meaningful SLOs for distributed platform health

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/Rmpl8948_

• Interested in sponsoring an episode? Learn more.