KubeFM

Amos Wenger walks through his production incident where adding a home computer as a Kubernetes node caused TLS certificate renewals to fail. The discussion covers debugging techniques using tools like netshoot and K9s, and explores the unexpected interactions between Kubernetes overlay networks and consumer routers.You will learn:How Kubernetes networking assumptions break when mixing cloud VMs with nodes behind consumer routers, and why cert-manager challenges fail in NAT environmentsThe differences between CNI plugins like Flannel and Calico, particularly how they handle IPv6 translationDebugging techniques for network issues using tools like netshoot, K9s, and iproute2Best practices for mixed infrastructure including proper node labeling, taints, and scheduling controlsSponsorThis episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.More infoFind all the links and info for this episode here: https://ku.bz/6Ll_7slr9Interested in sponsoring an episode? Learn more.

Tanat Lokejaroenlarb shares the complete journey of replacing EKS Managed Node Groups and Cluster Autoscaler with AWS Karpenter. He explains how this migration transformed their Kubernetes operations, from eliminating brittle upgrade processes to achieving significant cost savings of €30,000 per month through automated instance selection and AMD adoption.You will learn:How to decouple control plane and data plane upgrades using Karpenter's asynchronous node rollout capabilitiesCost optimization strategies including flexible instance selection, automated AMD migration, and the trade-offs between cheapest-first selection versus performance considerationsScaling and performance tuning techniques such as implementing over-provisioning with low-priority placeholder podsPolicy automation and operational practices using Kyverno for user experience simplification, implementing proper Pod Disruption BudgetsSponsorThis episode is sponsored by StormForge by CloudBolt — automatically rightsize your Kubernetes workloads with ML-powered optimizationMore infoFind all the links and info for this episode here: https://ku.bz/T6hDSWYhbInterested in sponsoring an episode? Learn more.

Festus Owumi walks through his project of building a lightweight version of Kubernetes in Go. He removed etcd (replacing it with in-memory storage), skipped containers entirely, dropped authentication, and focused purely on the control plane mechanics. Through this process, he demonstrates how the reconciliation loop, API server concurrency handling, and scheduling logic actually work at their most basic level.You will learn:How the reconciliation loop works - The core concept of desired state vs current state that drives all Kubernetes operationsWhy the API server is the gateway to etcd - How Kubernetes prevents race conditions using optimistic concurrency control and why centralized validation mattersWhat the scheduler actually does - Beyond simple round-robin assignment, understanding node affinity, resource requirements, and the complex scoring algorithms that determine pod placementThe complete pod lifecycle - Step-by-step walkthrough from kubectl command to running pod, showing how independent components work together like an orchestraSponsorThis episode is sponsored by StormForge by CloudBolt — automatically rightsize your Kubernetes workloads with ML-powered optimizationMore infoFind all the links and info for this episode here: https://ku.bz/pf5kK9lQFInterested in sponsoring an episode? Learn more.

Understanding what's actually happening inside a complex Kubernetes system is one of the biggest challenges architects face.Oleksii Kolodiazhnyi, Senior Architect at Mirantis, shares his structured approach to Kubernetes workload assessment. He breaks down how to move from high-level business understanding to detailed technical analysis, using visualization tools and systematic documentation.You will learn:A top-down assessment methodology that starts with business cases and use cases before diving into technical detailsPractical visualization techniques using tools like KubeView, K9s, and Helm dashboard to quickly understand resource interactionsSystematic resource discovery approaches for different scenarios, from well-documented Helm-based deployments to legacy applications with hard-coded configurations buried in containersDocumentation strategies for creating consumable artifacts that serve different audiences, from business stakeholders to new team members joining the projectSponsorThis episode is sponsored by StormForge by CloudBolt — automatically rightsize your Kubernetes workloads with ML-powered optimizationMore infoFind all the links and info for this episode here: https://ku.bz/zDThxGQsPInterested in sponsoring an episode? Learn more.

Andrew Jeffree from SafetyCulture walks through their complete migration of 250+ microservices from a fragile Helm-based setup to GitOps with ArgoCD, all without any downtime. He explains how they replaced YAML configurations with a domain-specific language built in CUE, creating a better developer experience while adding stronger validation and reducing operational pain points.You will learn:Zero-downtime migration techniques using temporary deployments with prune-last sync options to ensure healthy services before removing legacy onesHow CUE lang improves on YAML by providing schema validation, early error detection, and a cleaner interface for developersHuman-centric platform engineering approaches that prioritize developer experience and reduce on-call burden through empathy-driven design decisionsSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/Xvyp1_QcvInterested in sponsoring an episode? Learn more.

Mai Nishitani, Director of Enterprise Architecture at NTT Data and AWS Community Builder, demonstrates how Model Context Protocol (MCP) enables Claude to directly interact with Kubernetes clusters through natural language commands.You will learn:How MCP servers work and why they're significant for standardizing AI integration with DevOps tools, moving beyond custom integrations to a universal protocolThe practical capabilities and critical limitations of AI in Kubernetes operationsWhy fundamental troubleshooting skills matter more than ever as AI abstractions can fail in unexpected ways, especially during crisis scenarios and complex system failuresHow DevOps roles are evolving from manual administration toward strategic architecture and orchestrationSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/3hWvQjXxpInterested in sponsoring an episode? Learn more.

Niels Claeys shares how his team at Dataminded built Conveyor, a data platform processing up to 1.5 million core hours monthly. He explains the specific optimizations they discovered through production experience, from scheduler changes that immediately reduce costs by 10-15% to achieving 97% spot instance usage without reliability issues.You will learn:Why the default Kubernetes scheduler wastes money on batch workloads and how switching from "least allocated" to "most allocated" scheduling enables faster scale-down and better resource utilizationHow to achieve 97% spot instance adoption through strategic instance type diversification, region selection, and Spark-specific techniquesNode pool design principles that balance Kubernetes overhead with workload efficiencyPlatform-specific gotchas like AWS cross-AZ data transfer costs that can spike bills unexpectedlySponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/hGRfkzDJWInterested in sponsoring an episode? Learn more.

Vitalii Horbachov explains how Agoda built macOS VZ Kubelet, a custom solution that registers macOS hosts as Kubernetes nodes and spins up macOS VMs using Apple's native virtualization framework. He details their journey from managing 200 Mac minis with bash scripts to a Kubernetes-native approach that handles 20,000 iOS tests at scale.You will learn:How to build hybrid runtime pods that combine macOS VMs with Docker sidecar containers for complex CI/CD workflowsCustom OCI image format implementation for managing 55-60GB macOS VM images with layered copy-on-write disks and digest validationNetworking and security challenges including Apple entitlements, direct NIC access, and implementing kubectl exec over SSHReal-world adoption considerations including MDM-based host lifecycle management and the build vs. buy decision for Apple infrastructure at scaleSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/q_JS76SvMInterested in sponsoring an episode? Learn more.

Ben Poland walks through Faire's complete CI transformation, from a single Jenkins instance struggling with thousands of lines of Groovy to a distributed Buildkite system running across multiple Kubernetes clusters.He details the technical challenges of running CI workloads at scale, including API rate limiting, etcd pressure points, and the trade-offs of splitting monolithic pipelines into service-scoped ones.You will learn:How to architect CI systems that match team ownership and eliminate shared failure points across servicesKubernetes scaling patterns for CI workloads, including multi-cluster strategies, predictive node provisioning, and handling API throttlingPerformance optimization techniques like Git mirroring, node-level caching, and spot instance management for variable CI demandsMigration strategies and lessons learned from moving away from monolithic CI, including proof-of-concept approaches and avoiding the sunk cost fallacySponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/klBmzMY5-Interested in sponsoring an episode? Learn more.

Danyl Novhorodov, a veteran .NET engineer and architect at Eneco, presents his controversial thesis that 90% of teams don't actually need Kubernetes. He walks through practical decision-making frameworks, explores powerful alternatives like BEAM runtimes and Actor models, and explains why starting with modular monoliths often beats premature microservices adoption.You will learn:The COST decision framework - How to evaluate infrastructure choices based on Complexity, Ownership, Skills, and Time rather than industry hypePlatform engineering vs. managed services - How to honestly assess whether your team can compete with AWS, Azure, and Google's managed container platformsEvolutionary architecture approach - Why modular monoliths with clear boundaries often provide better foundations than distributed systems from day oneSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/BYhFw8RwWInterested in sponsoring an episode? Learn more.

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 failsHidden 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 chartsMonitoring strategies for autoscaling - What metrics to track for webhook latency and pod eviction rates to catch similar issues before they become criticalSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/rf1pbWXdNInterested 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 patternsThe Node Ranking Index (NRI) - a unified metric that combines CPU, RAM, and request data into a single comparable number for efficient scaling decisionsReal-world implementation challenges, including data validation, node startup timing constraints, load testing strategies, and the importance of proper research before building complex scaling solutionsSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/clbDWqPYpInterested 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 environmentsHow Istio's warm-up feature works to gradually ramp up traffic to new podsWhy other common solutions fail, including resource over-provisioning, init containers, and tools like GraalVMReal production impact from implementing this solution, including dramatic improvements in message moderation SLOs at BlaBlaCar's scale of 4,000 podsSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/grxcypt9jInterested 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 scalersThe technical architecture connecting macOS notifications through CloudWatch to trigger Kubernetes autoscaling using Swift, AWS SDKs, and custom metricsCost optimization strategies including how to calculate actual savings, account for API costs, and identify leading indicators of compute demandCreative approaches to autoscaling signals beyond CPU and memory, including examples from financial services and e-commerce that could revolutionize workload managementSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/sFd8TL1cSInterested 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 consumptionTechnical implementation details - Building custom Kubernetes schedulers using the scheduler plugin framework, including pre-filter and filter stages for carbon and time-of-use pricing optimizationEnergy measurement strategies - Approaches for tracking power consumption across CPUs, memory, and GPUsSponsorThis 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.ioMore infoFind all the links and info for this episode here: https://ku.bz/zk2xM1lfWInterested in sponsoring an episode? Learn more.