Edge computing time slice scheduling method based on deep reinforcement learning

A reinforcement learning and edge computing technology, applied in the field of edge computing, can solve problems such as instability and dependence on developer experience, and achieve the effect of solving long execution time, increasing generalization performance, and ensuring time efficiency

Pending Publication Date: 2020-08-07
BEIJING JIAOTONG UNIV
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  • Claims
  • Application Information

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Problems solved by technology

The heuristic algorithm can indeed provide a better feasible solution for the resource scheduling problem, but its disadvantage is that it is unstable and extremely dependent on the developer's experi

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  • Edge computing time slice scheduling method based on deep reinforcement learning
  • Edge computing time slice scheduling method based on deep reinforcement learning
  • Edge computing time slice scheduling method based on deep reinforcement learning

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Embodiment Construction

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0039] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof. It will be understoo...

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Abstract

The invention provides an edge computing time slice scheduling method based on deep reinforcement learning, which comprises the following steps: acquiring a plurality of task queues uploaded by terminal equipment, and taking the plurality of task queues as a sample pool; carrying out Markov decision process modeling on each task queue to generate a task state set and a corresponding action set; based on the task state set and the corresponding action set, obtaining a value of a neural network parameter matrix at a certain task execution moment through a neural network training method; judgingwhether the task state set is completely substituted into a neural network training method or not; if so, outputting the value of the neural network parameter matrix at the task execution moment to asample pool as a time slice scheduling result at the task execution moment; otherwise, continuing to execute Markov decision process modeling. According to the method provided by the invention, the time efficiency of the algorithm is accelerated and ensured, and the generalization performance of the algorithm is improved, so that a scheduling machine can autonomously learn a scheduling strategy according to actual scene features.

Description

technical field [0001] The present invention relates to the technical field of edge computing, in particular to an edge computing time slice scheduling method based on deep reinforcement learning. Background technique [0002] Edge computing is at the edge of the network close to the source of objects or data, such as image 3 As shown in the figure, the distributed open platform that integrates network, computing, storage, and application core capabilities provides edge intelligent services for terminal devices nearby, meeting the needs of industry digitization in terms of agile connection, real-time business, data optimization, application intelligence, security, and privacy protection. key needs. It can serve as a bridge connecting the physical and digital worlds, enabling smart assets, smart gateways and smart services. [0003] On the problem of resource scheduling, the traditional solution method is to find an effective heuristic algorithm under certain conditions, an...

Claims

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Application Information

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IPC IPC(8): G06F9/48G06N3/08
CPCG06F9/4881G06N3/08
Inventor 张振江李英龙沈波赵颖斯孙枫朱凯歌
Owner BEIJING JIAOTONG UNIV
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