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Distributed computing unloading method and device based on deep reinforcement learning

A distributed computing and computing offloading technology, which is applied in the direction based on specific mathematical models, computing, computing models, etc., can solve problems such as the inability to effectively improve the model convergence speed, and achieve the effect of improving the convergence speed and reducing the complexity.

Pending Publication Date: 2022-05-06
HUNAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] 2. Definition and solution of computational unloading problem
However, the above methods still cannot effectively improve the convergence speed of the model.

Method used

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  • Distributed computing unloading method and device based on deep reinforcement learning
  • Distributed computing unloading method and device based on deep reinforcement learning
  • Distributed computing unloading method and device based on deep reinforcement learning

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Embodiment Construction

[0053] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of this application to those skilled in the art.

[0054] The terminology used in this application is for the purpose of describing particular embodiments only, and is not intended to limit the application. As used in this application and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible ...

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Abstract

The invention relates to a distributed computing unloading method and device based on deep reinforcement learning. The method comprises the steps that a calculation unloading framework is set, a communication model and a calculation model are established according to the calculation unloading framework, the communication model is used for calculating the signal-to-noise-interference ratio of terminal equipment, the calculation model is used for conducting local calculation and edge calculation on the terminal equipment, and the terminal equipment is calculated based on the calculation unloading framework, the communication model and the calculation model. And modeling a calculation unloading problem into a Markov decision process, and carrying out optimization iteration solution on the Markov decision process by utilizing a depth deterministic strategy gradient algorithm of the double-Critic network to obtain an unloading decision. Due to the fact that the depth deterministic strategy gradient algorithm of the double Critic networks is used for conducting optimization iteration solution, the double Critic networks are respectively fitted, the complexity of fitting of a single Critic network is reduced, the convergence speed of the Critic networks is improved, and the overall convergence speed of the model is greatly improved.

Description

technical field [0001] The present application relates to the technical field of computing offloading of mobile edge computing based on computing models, and in particular to a distributed computing offloading method and device based on deep reinforcement learning. Background technique [0002] With the rapid development of 5G technology, it has driven the development of the Internet of Things (IOT) and computing-intensive applications, such as smart manufacturing, virtual reality, augmented reality, and the Internet of Vehicles. [0003] Mobile Edge Computing (MEC) is the core technology in the context of 5G. This technology can provide additional elastic computing power for computing tasks and applications through wireless communication on the side close to the user scene, which can effectively reduce the computing delay and cost of computing tasks, and make computing-intensive tasks run on low computing power devices. operation becomes possible. [0004] Compared with c...

Claims

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Application Information

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IPC IPC(8): H04W28/08H04W28/14G06F9/50G06N7/00
CPCH04W28/0958H04W28/14G06F9/5072G06F2209/509G06N7/01
Inventor 陆绍飞刘伸杨贯中李军义
Owner HUNAN UNIV