Task scheduling method based on deep reinforcement learning in hierarchical edge computing environment

A task scheduling and edge computing technology, applied in the computer field, can solve problems affecting mobile application experience, long data transmission delay, etc., and achieve the effects of low overhead, reduced service delay, and improved quality

Active Publication Date: 2021-12-10
CHONGQING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, remote offloading tasks require a long data transmission delay, which affects the experience of mobile applications after task offloading, especially for delay-sensitive mobile applications, such as: voice recognition and control, video image recognition, interactive games, etc.

Method used

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  • Task scheduling method based on deep reinforcement learning in hierarchical edge computing environment
  • Task scheduling method based on deep reinforcement learning in hierarchical edge computing environment
  • Task scheduling method based on deep reinforcement learning in hierarchical edge computing environment

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Embodiment Construction

[0060] Embodiments of the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and therefore are only examples, rather than limiting the protection scope of the present invention.

[0061] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application shall have the usual meanings understood by those skilled in the art to which the present invention belongs.

[0062] like figure 1 As shown, the scenario of the present invention is applicable to the edge network scenario. The mobile application offloads its own resource-intensive tasks to the edge cloud through the nearby connected base station (Base station, BS). Understand the current available IT resources for each edge service node. After the task is offloaded to the edge cloud, CC r...

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Abstract

The invention discloses a task scheduling method based on deep reinforcement learning in a hierarchical edge computing environment. The method comprises the following steps: establishing a neural network model based on Sequence-to-Sequence (Seq2Seq), and applying the model to solve mapping from an optimal task to an edge service node to serve as a neural network structure in an intelligent agent; training the model by adopting a deep reinforcement learning method based on Monte Carlo strategy gradient, so that the model has a self-learning capability and a capability of optimizing a task scheduling decision; and deploying a task scheduling algorithm fusing neural network solution and a heuristic algorithm in the system, so that the quality of a scheduling decision can be remarkably improved, and the efficiency and the quality can be balanced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to an edge computing environment in which service nodes are deployed hierarchically according to the distance from a mobile client. In the face of task request dynamics and edge service node resource heterogeneity, a depth-based A learned approach to intelligent task scheduling. Background technique [0002] With the rapid development and large-scale deployment of cloud computing, more and more mobile applications offload their computing-intensive tasks to cloud data centers. Effectively reduce the overhead of local resources. [0003] However, remote offloading tasks require a long data transmission delay, which affects the experience of mobile applications after task offloading, especially for delay-sensitive mobile applications, such as: voice recognition and control, video image recognition, interactive games, etc. . In order to reduce latency and improve mobile application...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48G06F9/50G06N3/02G06N3/08
CPCG06F9/5072G06F9/4806G06F9/4881G06N3/02G06N3/08Y02D10/00
Inventor 陈卓卫佩宏
Owner CHONGQING UNIV OF TECH
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