Resource scheduling method and device, electronic equipment and storage medium

A resource scheduling and scheduling method technology, applied in resource allocation, multi-program device, program control design, etc., can solve the problems of fragmented GPU allocation, high probability of task scheduling failure, and failure to consider GPU resource scheduling, etc., to reduce fragmentation The effect of optimizing the situation and improving the success rate of scheduling

Active Publication Date: 2020-11-20
NETEASE (HANGZHOU) NETWORK CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, because the existing K8S scheduling algorithm only considers CPU and memory resources, but does not consider the scheduling of GPU resources, it often leads to serious fragmentation of GPU allocation and high probability of task scheduling failure for large GPU requests.

Method used

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  • Resource scheduling method and device, electronic equipment and storage medium
  • Resource scheduling method and device, electronic equipment and storage medium
  • Resource scheduling method and device, electronic equipment and storage medium

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Embodiment Construction

[0058] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

[0059] Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art w...

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Abstract

The invention provides a resource scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of resource scheduling. The GPU resource scheduling methodcan be applied to a main node of a kubernetes cluster, wherein the kubernetes cluster comprises a plurality of main nodes and a plurality of working nodes. The GPU resource scheduling method can comprise the steps: receiving a scheduling task needing to schedule GPU resources; according to a scheduling task, screening the plurality of working nodes of which the residual resource quantity meets the scheduling task as a plurality of pre-selected nodes; calculating node scores of a plurality of pre-selected nodes by adopting a preset algorithm, and taking the pre-selected node with the highest node score as a scheduling node; due to the fact that the node score comprises the GPU resource score of the pre-selected node, GPU resources can be considered when the scheduling node is selected, fragmentation of GPU distribution is reduced to a certain extent, and the scheduling success rate of scheduling tasks with large GPU demand is increased.

Description

technical field [0001] The present application relates to the technical field of resource scheduling, and in particular to a resource scheduling method, device, electronic equipment, and storage medium. Background technique [0002] With the rapid development of deep learning, the requirements for computing power and computing mode are getting higher and higher. The traditional central processing unit (Central Processing Unit, CPU) can no longer meet the requirements, and it is usually necessary to use a graphics processing unit (Graphics Processing Unit, GPU) To complete deep learning computing, how to efficiently combine GPU and container cloud is also an increasingly important research topic. [0003] Usually, in an AI computing cluster based on kubernetes (referred to as K8s), a server usually installs a certain number of GPU cards (such as 8), and GPU is usually the most scarce and expensive resource in the server ( compared to CPU, memory). [0004] However, because ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50
CPCG06F9/5027Y02D10/00
Inventor 李想成赵增刘柏李仁杰范长杰程龙胡志鹏
Owner NETEASE (HANGZHOU) NETWORK CO LTD
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