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Distributed DNN-based mobile fog computing loss joint optimization system and method

A joint optimization and fog computing technology, applied in transmission systems, digital transmission systems, neural learning methods, etc., can solve problems such as severe usage scenarios and unsuitable uninstallation.

Active Publication Date: 2021-06-04
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0028] The algorithm has strict requirements on usage scenarios and is not suitable for uninstallation in multi-user and multi-task scenarios

Method used

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  • Distributed DNN-based mobile fog computing loss joint optimization system and method
  • Distributed DNN-based mobile fog computing loss joint optimization system and method
  • Distributed DNN-based mobile fog computing loss joint optimization system and method

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Embodiment Construction

[0104] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0105] like figure 1 As shown, the MFC network architecture consists of a cloud computing center, multiple distributed fog servers, and multiple users. Both the fog server and the cloud computing center can provide the required computing resources for users' computing tasks. In particular, each F-AP (Fog Access Point) is connected to the fog server, and each fog server is connected to the cloud computing center through a wired backhaul link. Each user is not related to each other, but there are multiple tasks that need to be processed locally or offloaded, and associated with the same F-AP for transmission.

[0106] like figure 2 As shown, in the hierarchical offloading model of the present invention, the calculation offloading modes of the MFC are a...

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Abstract

The invention discloses a distributed DNN-based mobile fog computing loss joint optimization system and method. The system comprises a local computing layer, a fog computing layer and a cloud computing layer. The local computing layer computes a task through a user device. And the fog computing layer is used for providing fog computing service for the unloading task so as to reduce time delay and energy consumption of computing of the user equipment. The cloud computing layer is used for processing large-scale computing and high-complexity computing. An unloading task is firstly sent to a fog receiving node away from a local layer through a wireless network, uploaded to a fog computing layer through the fog receiving node and finally uploaded to a cloud computing layer through the fog computing layer, and a user independently decides whether to unload the task to a fog server for computing or not. The fog server can decide whether to unload the task to the cloud server on the upper layer again for calculation or not. The system has the advantages that the optimal unloading decision of each unloading task is given in a short time, the average accuracy of unloading is high, each neural network model is optimized, and the convergence state can be achieved more quickly.

Description

technical field [0001] The present invention relates to the field of decision-making and decision-making of mobile fog computing and the technical field of joint optimization of overall efficiency, and in particular to a joint optimization system and method for computing offloading, decision-making and judgment of offloading, time delay and energy consumption in distributed DNN-based mobile fog computing. Background technique [0002] With the increasing popularity of smart devices, smart applications have strict requirements on computing power while providing rich user experience. Due to the limitations of mobile devices, how to maintain the quality of service of legacy devices becomes a challenge when faced with heavy computing demands. To solve this problem, mobile cloud computing (MCC) is proposed to make up for the lack of computing power of local devices by offloading computing tasks to remote cloud servers with powerful computing power. [0003] Because users are mor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04L29/08G06N3/08
CPCH04L41/044H04L41/042H04L41/0823H04L41/145H04L67/025H04L67/1004H04L67/1097G06N3/08Y02D10/00
Inventor 白文乐杨忠骏马子扬韩宇龙张键红武梦龙刘文楷何建杰王卓琪杨一夫冯良黄明
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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