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Resource scheduling strategy optimization method based on Kubernetes cluster

A resource scheduling and optimization method technology, applied in resource allocation, program startup/switching, program control design, etc., can solve problems such as redundant computing, a large number of resource fragments, and no Pod resource computing, so as to reduce redundant computing , Reduce the possible effect of downtime

Pending Publication Date: 2022-01-28
WUHAN TEXTILE UNIV +1
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Problems solved by technology

[0005] 1) In the preferred solution, the default scheduler only considers the remaining amount or proportion of resources (generally CPU resources and memory resources) on the node, and does not include the resources required by the Pod itself, resulting in a large number of resource fragments on each node , current research does not address this issue well
[0006] 2) When the same resource scheduler schedules different Pod copies, each time a Pod copy is scheduled, a preselection and optimal calculation will be performed, resulting in a large number of redundant calculations

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  • Resource scheduling strategy optimization method based on Kubernetes cluster

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Embodiment Construction

[0026] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0027] The scheduling process of Kubernetes is divided into the following three steps:

[0028] (1) Pre-selection scheduling: Check all available nodes and screen out nodes that meet Pod requirements. These nodes become candidates for the next process.

[0029] (2) Optimal scheduling: further screening is carried out among the nodes screened out in the process (1). The optimal strategy is used to calculate the score of each available node, and the node with the highest score is the most suitable node to be scheduled.

[0030] (3) Selection: If there are two or more nodes with the highest score in the score results calculated by the optimal scheduling strategy, then a random algorithm is used to randomly select one...

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Abstract

The invention relates to a resource scheduling strategy optimization method based on a Kubernetes cluster, in particular to a whole scheduling process of Kubernetes resource scheduling, which comprises optimization of a scheduling algorithm and optimization of a scheduling mode, and is mainly used for automatic deployment of a Docker container by the Kubernetes cluster. The method specifically refers to optimization of an optimization algorithm involved when a resource scheduler of Kubernetes allocates resources to each node and optimization of a scheduling mode when different copies under the same resource controller are scheduled, and the problems of resource scheduling imbalance and redundant calculation are solved.

Description

technical field [0001] The present invention relates to the field of resource scheduling methods, in particular to a resource scheduling strategy optimization method based on Kubernetes clusters. Background technique [0002] With the emergence and rapid development of container technology, container technology represented by Docker has attracted more and more attention. Compared with traditional virtual machine technology, it can provide a lighter and easier-to-use application deployment solution. Docker is currently a containerization technology widely used in the industry. Running a Docker container on a single machine cannot maximize its performance. It has become inevitable to move from a single machine to a cluster. Only by building a cluster can the advantages of container isolation, resource allocation, and orchestration management be fully utilized. [0003] The open source Kubernetes has excellent container scheduling and management capabilities, and has now beco...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48G06F9/50
CPCG06F9/4881G06F9/5016G06F9/5027
Inventor 史爱武刘可张义欣罗干韩超
Owner WUHAN TEXTILE UNIV