Ze

Zero-downtime Kubernetes workload migration using CRIU

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Zero-downtime Kubernetes workload migration using CRIU

We've been working on container live migration for Kubernetes - moving running pods between nodes with zero downtime, preserving memory state, network connections, and process state. The problem: Traditional Kubernetes eviction kills pods and restarts them elsewhere. This breaks stateful workloads like databases, long-running computations, and anything that can't tolerate restarts. It's a major blocker for cluster optimization - you can't bin-pack efficiently when half your workloads are "untouchable." Our approach uses CRIU (Checkpoint/Restore In Userspace) with custom Kubernetes orchestration: >Checkpoint running containers with their full memory state >Transfer state to destination node while app keeps running >Brief pause (~100ms) for final state sync >Resume on new node with preserved TCP connections and PIDs Key technical details: >Works with multi-container pods, StatefulSets, bare pods >Preserves IP addresses via forked AWS VPC CNI >Incremental memory transfer reduces migration time >Fallback to traditional eviction if migration fails >Currently AWS EKS + Kubernetes 1.30+, expanding to other clouds The impact: We're seeing 40-60% better cluster utilization because Evictor (our bin-packing optimizer) can now move stateful workloads that were previously stuck. A Redis instance with 4GB RAM migrates in ~2-3 seconds with zero dropped connections. This is early access on AWS EKS. We're particularly interested in feedback from folks running stateful workloads that currently can't be optimized due to restart sensitivity. Detailed video: https://www.youtube.com/watch?v=WUxMYl2XWCQ Docs: https://docs.cast.ai/docs/clm-overview

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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