9798270994747 - kubernetes scheduling: the complete guide: master pod scheduling, resource allocation, and custom schedulers. node affinity, taints, topology awareness, and gpu scheduling for modern workloads de draycott, sofia (3 resultados)
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 30,10
Envío por EUR 5,91Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 35,20
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Master Kubernetes pod placement with clear, proven practices that deliver predictable performance in real clusters.Scheduling decisions shape reliability, cost, and latency. Many teams struggle with vague rules, uneven spreading, storage surprises, or GPU contention that shows up only under…load.This guide turns the scheduler into a tool you can reason about. It explains how requests, policies, and plugins interact, then gives you repeatable labs and copy-ready manifests so you can apply the lessons in production.understand kube scheduler flow, queueing, filtering, scoring, bindingshape outcomes with profiles, extension points, and plugin weightsset requests and limits that align with qos and stable eviction behaviorsize node allocatable and pod overhead for realistic densityuse node labels, node affinity, and inter pod rules without deadlocksapply taints and tolerations for pool isolation and safe admissionspread with podtopologyspread, maxskew, and default policiesdesign pdbs, priorities, and preemption paths that prevent starvationrun storage aware scheduling with waitforfirstconsumer and csi capacityschedule gpus with device plugins, nvidia operator, mig, and time slicingadopt dra with resourceclass and resourceclaims for accelerator controltune numa policies, cpu manager, memory manager, and topology manageroperate multiple schedulers, avoid risky extenders, add safe pluginsuse the descheduler with budgets and limits to fix drift safelymonitor the metrics that matter and build practical dashboardstroubleshoot incidents like ip exhaustion, pvc flapping, and skew driftrun field labs, kube burner load tests, simulator traces, gpu labs, and gatesThis is a code heavy guide with working yaml, bash, go, and json snippets that you can use to stand up labs, tune policies, and ship changes with confidence.Grab your copy today and make Kubernetes scheduling an advantage for your team. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 33,95
Envío por EUR 43,57Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Master Kubernetes pod placement with clear, proven practices that deliver predictable performance in real clusters.Scheduling decisions shape reliability, cost, and latency. Many teams struggle with vague rules, uneven spreading, storage surprises, or GPU contention that shows up only under…load.This guide turns the scheduler into a tool you can reason about. It explains how requests, policies, and plugins interact, then gives you repeatable labs and copy-ready manifests so you can apply the lessons in production.understand kube scheduler flow, queueing, filtering, scoring, bindingshape outcomes with profiles, extension points, and plugin weightsset requests and limits that align with qos and stable eviction behaviorsize node allocatable and pod overhead for realistic densityuse node labels, node affinity, and inter pod rules without deadlocksapply taints and tolerations for pool isolation and safe admissionspread with podtopologyspread, maxskew, and default policiesdesign pdbs, priorities, and preemption paths that prevent starvationrun storage aware scheduling with waitforfirstconsumer and csi capacityschedule gpus with device plugins, nvidia operator, mig, and time slicingadopt dra with resourceclass and resourceclaims for accelerator controltune numa policies, cpu manager, memory manager, and topology manageroperate multiple schedulers, avoid risky extenders, add safe pluginsuse the descheduler with budgets and limits to fix drift safelymonitor the metrics that matter and build practical dashboardstroubleshoot incidents like ip exhaustion, pvc flapping, and skew driftrun field labs, kube burner load tests, simulator traces, gpu labs, and gatesThis is a code heavy guide with working yaml, bash, go, and json snippets that you can use to stand up labs, tune policies, and ship changes with confidence.Grab your copy today and make Kubernetes scheduling an advantage for your team. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
