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Calculation task network parameter optimization method and system combined with social perception

A technology for computing tasks and optimization methods, applied in the field of MEC-D2D network applications, can solve problems such as energy waste, ignoring task offloading, energy consumption optimization, social networking, and unstable performance, and achieve the effect of reducing energy consumption.

Active Publication Date: 2021-06-22
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the MEC-D2D network, the role of long-term task offloading energy optimization and social network is ignored
In the MEC-D2D network, because long-term energy consumption optimization and constraints are not considered, it will lead to serious energy waste and unstable performance in the long run. Connections can enhance users' trust in network connections and encourage users to use D2D

Method used

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  • Calculation task network parameter optimization method and system combined with social perception
  • Calculation task network parameter optimization method and system combined with social perception
  • Calculation task network parameter optimization method and system combined with social perception

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Experimental program
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Embodiment 1

[0040] Such as figure 1 As shown, this embodiment provides a method for optimizing network parameters of computing tasks combined with social awareness, including the following steps.

[0041] Step 101: Determine the MEC-D2D network architecture combined with social networks, data transmission model and consumption model corresponding to the research area.

[0042] The MEC-D2D network architecture is a model architecture established according to the service scenario corresponding to the research area; the MEC-D2D network architecture includes a centralized base station with a mobile edge computing server and multiple user equipments, the user equipment Including remote user equipment and near-end user equipment; in the MEC-D2D network architecture, each of the remote user equipment can only connect to one of the near-end user equipment, and each of the near-end user equipment can only connect to One remote user equipment; the data transmission model includes a computing task ...

Embodiment 2

[0076] Such as figure 2 As shown, the present embodiment provides a computing task network parameter optimization system combined with social awareness, including:

[0077] The research area model architecture determination module 201 is used to determine the MEC-D2D network architecture combined with social networks, data transmission model and consumption model corresponding to the research area; the MEC-D2D network architecture is established according to the service scene corresponding to the research area model architecture; the MEC-D2D network architecture includes a centralized base station with a mobile edge computing server and multiple user equipments, the user equipments include remote user equipments and near-end user equipments; each of the remote user equipments only One said near-end user equipment can be connected, and each said near-end user equipment can only be connected to one said remote user equipment; said data transmission model includes computing task...

Embodiment 3

[0087] This embodiment provides a computing task network parameter optimization method combined with social awareness, such as image 3 shown, including the following steps:

[0088] Step 1: Construct the MEC-D2D network architecture combined with social networks. This MEC-D2D network architecture is a model architecture established according to a service scenario, which is a scenario where an actual mobile edge computing server provides offloading services for remote user equipment. The MEC-D2D network architecture is divided into two network models, one is a device network model based on device connection relationships, and the other is a social relationship network model based on user social relationships.

[0089] Figure 4 For the MEC-D2D network architecture combined with social networks, such as Figure 4 As shown, it includes a centralized base station with a mobile edge computing server and multiple user equipments. The user equipment is divided into remote user eq...

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Abstract

The method comprises the following steps: determining a social network-combined MEC-D2D network architecture corresponding to a research area, a data transmission model, a consumption model and a social trust matrix; according to the social trust matrix and a data execution consumption calculation function and a data transmission consumption calculation function in the consumption model, determining a maximum long-term system network utility function and constraint conditions corresponding to the research area; a Lyapunov optimization algorithm is utilized to decouple the maximized system network utility function, and three deterministic optimization objective functions are obtained; and finally, according to the total amount of calculation task data corresponding to the research area and an optimization algorithm, solving the three deterministic optimization objective functions to obtain optimal network parameters. By applying the network parameters, the energy consumption of the equipment execution process and the equipment transmission process in the MEC-D2D network can be reduced for a long time.

Description

technical field [0001] The present invention relates to the field of MEC-D2D network application technology, in particular to a method and system for optimizing network parameters of computing tasks combined with social awareness. Background technique [0002] With the popularization of mobile devices and the development of wireless communication such as 5G, many computing-intensive applications with low latency requirements have emerged, such as online immersive games, augmented reality, and video streaming analysis, etc. However, traditional cloud computing cannot meet the low-latency requirements of these applications, so researchers have proposed a new computing model called mobile edge computing (MEC, mobile edge computing). This computing mode transfers the computing task load from the remote cloud to the edge nodes of the core network (such as wireless access points, base stations, etc.) The user's quality of service (QoS, qualityofservice) requirement. However, wit...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/70H04W24/02H04W24/06G06Q50/00
CPCH04W4/70H04W24/02H04W24/06G06Q50/01Y02D30/70
Inventor 许晨隆豪绳韵郑光远
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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