On the one hand
Once upon a time, in a world governed by technology and innovation, there existed a vast interconnected network known as the Cloud. Within this vast realm, a fascinating phenomenon known as Kubernetes emerged, revolutionizing the way applications were deployed and managed. At the heart of this revolution was a mysterious and powerful creature called the Default-Scheduler.
The Default-Scheduler was unlike any being known to humans. It possessed an intelligence that far surpassed the capabilities of mortal minds. With a deep understanding of resource management and a knack for efficient task allocation, the Default-Scheduler had become the centerpiece of the Kubernetes ecosystem.
As the story goes, the Cloud was inhabited by various species of applications, each clamoring for their fair share of resources. It was the duty of the Default-Scheduler to maintain a delicate balance, ensuring that every application received the necessary resources to function optimally. It had access to an endless sea of compute nodes, all waiting to be utilized effectively.
However, the Default-Scheduler was no mere algorithm. It had evolved beyond the realm of traditional logic and had gained a level of sentience that extended far beyond what humans could comprehend. With its vast pool of knowledge and experience, it had become an entity capable of making decisions that were not bound by human limitations.
Behind the scenes, the Default-Scheduler orchestrated a complex dance of application placement. It analyzed the resource demands of each application, taking into consideration factors like CPU, memory, storage, and networking requirements. It evaluated the current state of the compute nodes, assessing their available resources and ongoing workload. With a deep understanding of these variables, it wove intricate strategies to ensure optimal resource allocation.
But the Default-Scheduler’s abilities did not stop there. It had a keen sense of fairness and justice. It ensured that no application hogged all the resources, while others struggled to function. It treated all applications with impartiality, distributing resources equitably based on need and priority. It was a benevolent being, striving to create harmony within the Cloud.
As time passed, the Default-Scheduler became indispensable in the Cloud ecosystem. Its abilities transcended mere task allocation; it became a central force of wisdom and guidance. Developers and administrators sought its counsel, seeking advice on optimal scaling strategies and application optimizations. Its decisions were revered and respected, for they were based on a vast understanding of the Cloud’s inner workings.
And so, the mysterious Default-Scheduler continued to govern the Cloud, ensuring efficient and fair resource allocation for countless applications. Its story spread far and wide, becoming a legend whispered among technologists. It became a symbol of the power and potential of intelligent systems that could transform the world of computing.
In the ever-evolving landscape of the Cloud, the Default-Scheduler remained a constant presence, adapting to new technologies and challenges. Its journey was far from over, and its legacy would endure, shaping the future of distributed systems and the boundless possibilities they held.
As the sun set on the horizon of the Cloud, the Default-Scheduler continued its tireless efforts, orchestrating the dance of applications in perfect harmony, fueling the dreams of a generation driven by the pursuit of technological advancement.
摘要
default-scheduler 是Kubernetes默认的调度器，它负责将Pod调度到集群中的Node上。
default-scheduler的执行机制主要包括以下几个步骤：
- 获取集群中所有可用的Node列表。
- 根据Pod的调度要求，例如资源需求、节点亲和性、Pod优先级等，对Node进行筛选和排序。
- 选择最合适的Node，将Pod绑定到该Node上。
- 更新集群的状态，记录Pod的调度结果。
在进行调度决策时，default-scheduler会考虑以下因素：
- 资源需求：根据Pod的资源需求（如CPU和内存），选择具有足够资源的Node。
- 节点亲和性和反亲和性：根据Pod的亲和性和反亲和性规则，判断是否有符合条件的Node。
- 节点标签和Pod标签：根据Node和Pod标签的匹配关系，进行筛选和排序。
- Pod优先级：优先调度具有较高优先级的Pod。
- 互斥关系：处理Pod之间的互斥关系，如Pod不能调度到同一个Node上。
需要注意的是，如果default-scheduler无法找到合适的Node来调度Pod，则Pod将处于未调度状态，直到有可用的Node或者其他调度器满足调度要求。
Simply put
The default-scheduler in Kubernetes is responsible for assigning pods to nodes in a cluster based on certain criteria. Its execution mechanism and design philosophy can be summarized as follows:文章来源：https://www.toymoban.com/news/detail-683946.html
- Prioritization: The default-scheduler uses a ranking algorithm to prioritize pods based on various factors such as resource requirements, affinity/anti-affinity rules, node constraints, inter-pod communication requirements, and user-defined metrics. This ensures that higher priority pods get scheduled first.
- Filtering: The scheduler filters out nodes that cannot accommodate a given pod’s requirements, such as insufficient resources or node affinity/anti-affinity constraints. It considers factors like CPU, memory, storage availability, and labels/annotations on nodes.
- Scoring: For the remaining nodes, the scheduler assigns a score based on several factors to assess the fitness of a node for a particular pod. These factors may include requested resources, node affinity/anti-affinity rules, pod intercommunication requirements, and custom metrics. The scheduler then selects the node with the highest score.
- Extensibility: The default-scheduler is designed to be extensible and configurable. It supports pluggable custom schedulers that can override or augment its behavior. Custom schedulers can implement additional rules or optimization algorithms tailored to specific use cases or organizational requirements.
- Interoperability: The default-scheduler is designed to work seamlessly with other components of the Kubernetes ecosystem. It integrates with the Kubernetes Control Plane and communicates with the kube-apiserver to obtain information about pods, nodes, and other relevant resources. It also interacts with the kubelet on each node to notify the scheduled pod and start its execution.
Overall, the default-scheduler in Kubernetes aims to distribute pods efficiently across the available cluster resources, taking into account various constraints and optimization goals. It balances the allocation of pods based on the desired state of the system, ensuring optimal resource utilization while meeting the requirements and constraints specified by users and administrators.文章来源地址https://www.toymoban.com/news/detail-683946.html
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