This tutorial walks through Kubernetes initContainers, demonstrates how to create and manage static Pods, and explains pod scheduling strategies—including node labeling and nodeSelector—using step‑by‑step YAML examples and kubectl commands to illustrate each concept in practice.
This article answers ten common Kubernetes questions, covering pod scheduling strategies, OOM behavior, dynamic ConfigMap updates, pod stability, ClusterIP service load balancing, log collection, liveness probes, scaling mechanisms, exec access, and troubleshooting container restarts, providing concise explanations for each scenario.
This article explains why pod scheduling is critical in Kubernetes and walks through practical techniques such as node selectors, affinity/anti‑affinity rules, taints and tolerations, priority classes, preemption, and custom scheduling strategies, complete with real YAML examples and command‑line demos.
Learn how Kubernetes initContainers initialize pods, use static pods for automatic pod creation, and control pod scheduling with node selectors and labels, including practical YAML examples, command-line demonstrations, and troubleshooting tips to ensure containers start in the correct order and on desired nodes.
This article explains Kubernetes QoS classes—Guaranteed, Burstable, and BestEffort—detailing their resource request and limit requirements, how to configure them with YAML examples, the eviction priority order, and practical best‑practice strategies for classifying and scheduling workloads across cluster nodes.
This article explains the core concepts of Kubernetes—including its architecture, node communication, pod scheduling, data storage, service exposure, scaling, and controller coordination—through a series of clear questions and answers, helping beginners grasp why the platform feels complex.
This article introduces Kubernetes fundamentals through a series of questions and answers, covering its architecture, node communication, pod scheduling, data storage, external access, scaling mechanisms, and component coordination, all illustrated with clear diagrams.
This article breaks down the core concepts of Kubernetes—including its master‑worker architecture, pod scheduling, etcd storage, service exposure, scaling mechanisms, and controller interactions—through a series of clear questions and illustrated answers to help beginners grasp the platform’s complexity.
The session detailed how Tencent’s container experts boost Kubernetes cluster utilization by correcting pod resource requests, employing two‑level auto‑scaling, dynamic over‑commit, adaptive scheduling and eviction, and using HPA/EHPA/VPA, achieving up to 38.7% node usage and roughly 60% cost savings in real‑world workloads.
The Kubernetes scheduler continuously watches for unscheduled Pods, places them in a priority queue, filters feasible Nodes, scores and selects the best Node using built‑in and custom plugins—adjusting the node‑sampling rate for large clusters, and allowing extensibility through extenders, multiple schedulers, and the scheduler framework configuration.
This article walks through the fundamental concepts of Kubernetes, explaining how the master node communicates with workers, how pods are scheduled onto nodes, where cluster state is stored, how services expose pods externally, and how replication controllers enable dynamic scaling.
This article explains the evolution and core principles of the Kubernetes scheduler, outlines its fairness, efficiency, performance, and flexibility goals, describes the scheduling cycle and filtering‑scoring process, and introduces configuration methods for custom scheduling policies.
DaemonSets in Kubernetes automatically run a dedicated pod on each node, handling storage, logging, and monitoring tasks, while using node selectors, affinities, and tolerations to manage placement, resources, and lifecycle, with examples and YAML definitions for fluentd‑elasticsearch deployment.
This guide explains how to control Kubernetes pod placement using nodeName, nodeSelector, node and pod affinity (hard and soft), taints and tolerations, as well as pod lifecycle features such as restart policies, init containers, lifecycle hooks, and liveness/readiness probes, with concrete YAML examples and commands.
This article explains how to improve Kubernetes resource utilization by addressing node fragmentation, pod misconfiguration, and HPA settings, and then details five concrete scheduling methods—including nodeName, nodeSelector, nodeAffinity, taints, and pod priority—to optimize pod placement and reduce waste.
Learn how to use Kubernetes Descheduler to rebalance uneven pod distribution across nodes by configuring various built‑in strategies, custom policies, filtering options, and deployment methods such as Jobs and CronJobs, with detailed examples and best‑practice guidelines for production clusters.
This guide explains Kubernetes taints and tolerations, how to create, view, and remove taints, configure tolerations in pod specs, and use nodeName and nodeSelector to control pod placement, complete with practical command‑line examples and YAML manifests.
This article explains how Kubernetes Scheduler assigns Pods to nodes, covering fairness, resource efficiency, performance, and flexibility, then details the predicate and priority phases, common algorithms, node and pod affinity rules, custom scheduler configuration, and practical YAML examples with command‑line demonstrations.
This article walks through building a simple Go web service, containerizing it, pushing the image to a registry, and then exploring how Kubernetes API Server authenticates clients with mutual TLS and how the scheduler’s pre‑filter and scoring phases decide the optimal node for a pod, complete with code snippets and log analysis.
This article explains how kube-scheduler in Kubernetes orchestrates pod placement by applying pre‑selection (predicates) and scoring (priorities) strategies, discusses fairness, resource efficiency, speed, flexibility, details common predicate and priority algorithms, and demonstrates practical scenarios with YAML and command‑line examples.
