The Making of Flux: The Origin, a KubeFM Original Series
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.SponsorJoin the Flux maintainers and community at FluxCon, November 11th in Salt Lake City—register hereMore infoFind all the links and info for this episode here: https://ku.bz/5Sf5wpd8yInterested in sponsoring an episode? Learn more.
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Predictive vs Reactive: A Journey to Smarter Kubernetes Scaling, with Jorrick Stempher
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.
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Solving Cold Starts: Uses Istio to Warm Up Java Pods, with Frédéric Gaudet
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.
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Teaching Kubernetes to Scale with a MacBook Screen Lock, with Brian Donelan
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.
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Building a Carbon and Price-Aware Kubernetes Scheduler, with Dave Masselink
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.
Discover all the great things happening in the world of Kubernetes, learn (controversial) opinions from the experts and explore the successes (and failures) of running Kubernetes at scale.
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