Overview

Master Kubernetes scheduler internals and build custom scheduling solutions that optimize resource utilization, reduce costs, and meet complex workload requirements at scale. The default Kubernetes scheduler works well for basic workloads, but production clusters running AI, batch processing, or latency-sensitive applications need more. You need topology-aware placement, dynamic GPU allocation, load-based bin packing, and custom scheduling logic that matches your infrastructure reality. This book gives you the deep architectural knowledge and practical code to extend the scheduler, write plugins, and deploy production-grade scheduling strategies. Written for platform engineers, SREs, and architects managing large Kubernetes clusters, this guide takes you inside the scheduler's control plane flow, from the scheduling cycle through binding. You'll understand extension points, build custom plugins with proper state management, configure multiple scheduler profiles for different workload classes, and implement topology-aware placement that respects NUMA boundaries, storage locality, and zone distribution. What You'll Learn: Scheduler architecture internals: control plane flow, scheduling and binding cycles, queues, cache behavior, requeue hints, and how unschedulable pods move through the system Writing scheduler plugins: minimal plugin skeleton, registration, plugin arguments, using CycleState to share data across phases with cost control, testing with fake client, and profiling with pprof KubeSchedulerConfiguration v1 with safe defaults, multiple profiles with schedulerName, zero-downtime migration, and performance tuning including percentageOfNodesToScore and API QPS limits Built-in plugin behavior: NodeResourcesFit and RequestedToCapacityRatio shapes, affinity and anti-affinity defaults, topology spread constraints, and load-aware scoring with Trimaran TargetLoadPacking Dynamic Resource Allocation end to end: DRA objects (DeviceClass, ResourceClaim, ResourceSlice), scheduler integration, GPU examples with NVIDIA DRA driver, and migration from device plugins with quotas Node resource managers and topology: CPU Manager, Memory Manager, PSI, Memory QoS tuning, Topology Manager policies for single NUMA alignment, SMT pitfalls, and Node Resource Topology plugin with RTE or NFD Topology-aware placement: pod topology spread constraints with cluster defaults, batch and AI scheduling with Kueue, and colocation with Koordinator for interference control and NUMA hints Storage-aware scheduling: WaitForFirstConsumer mode, PV NodeAffinity, CSI storage capacity tracking with scheduler hints, and StatefulSets with zone-aware placement for disaster readiness Scheduler observability: throughput and latency SLOs, key metrics and alert rules, load generation with ClusterLoader2 and kube-burner for repeatable performance labs Production operations: priority and preemption, fairness, quotas, batch queues, Descheduler strategies for safe rebalancing with PDBs, and managed Kubernetes caveats for EKS, GKE, and AKS The book includes working code illustrations for plugin development, configuration examples, and performance tuning playbooks. You'll see complete plugin implementations, multi-profile configurations, DRA integration code, and observability setups ready for your cluster. Take control of Kubernetes scheduling today and optimize your cluster for the workloads that matter most.

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