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Task network access and service placement position selection method and system

A technology for network access and placement, applied in the field of mobile communications, can solve the problems of not significantly reducing the overall performance of the system, limited computing and storage resources, and single influencing factors, so as to improve task offloading rate, low delay, and energy consumption small effect

Active Publication Date: 2020-11-24
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The inventors of the present disclosure found that due to the limited computing and storage resources of the edge cloud of the system, multiple users may seize resources and network congestion may occur; while the current network access and service placement method based on tasks in the mobile edge computing scenario , the factors considered are usually relatively simple, and the final selection method of network access and service placement cannot significantly reduce the overall performance of the system

Method used

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  • Task network access and service placement position selection method and system
  • Task network access and service placement position selection method and system
  • Task network access and service placement position selection method and system

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Embodiment 1

[0034] like figure 1 As shown, the mobile edge computing system considered in this disclosure includes several mobile devices and several edge servers. The device uploads the task to the edge cloud that can serve the area where the task is located, thereby accessing the network; choose any edge cloud to place the service. The network access base station of the task and the service placement base station can be the same or different, but it should be noted that the task can only select the base station whose service scope includes the task as the network access point. After the task is executed at the edge, it is sent back to the local device through the wireless link.

[0035] Such as the Internet of Vehicles, smart cities and smart homes are typical applications in mobile edge computing scenarios. In these typical applications, smartphones, iPads, etc. can be used as terminal devices, and the tasks in them can be executed on the local device or offloaded to the edge cloud f...

Embodiment 2

[0101] Embodiment 2 of the present disclosure provides a task network access and service placement location selection system, including:

[0102] The data acquisition module is configured to: obtain the fitness function according to the acquired real-time status data of the current task and the parameter data of the edge computing system;

[0103] The random solution generation module is configured to: generate multiple random solutions for network access and service placement, each random solution includes multiple network access and service placement base stations for at least one task;

[0104] The optimal solution acquisition module is configured to: obtain the local optimal solution and the global optimal solution in each random solution according to the fitness function;

[0105] The location selection module is configured to: use the particle swarm optimization algorithm to optimize according to the obtained local optimal solution and the global optimal solution, obtain...

Embodiment 3

[0108] Embodiment 3 of the present disclosure provides a medium on which a program is stored. When the program is executed by a processor, the steps in the method for network access and service placement location selection of the task described in Embodiment 1 of the present disclosure are implemented. The above steps are:

[0109] According to the obtained real-time status data of the current task and the parameter data of the edge computing system, the fitness function is obtained;

[0110] Generate multiple random solutions for network access and service placement, each random solution includes multiple network access and service placement base stations for at least one task;

[0111] According to the fitness function, the local optimal solution and the global optimal solution in each random solution are obtained;

[0112] According to the obtained local optimal solution and global optimal solution, the particle swarm algorithm is used for optimization to obtain the optima...

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Abstract

The invention provides a task network access and service placement position selection method and a system, and the method comprises the following steps: obtaining a fitness function according to the obtained real-time state data of a current task and the parameter data of an edge calculation system; generating a plurality of random solutions of network access and service placement, wherein each random solution comprises a plurality of network access and service placement base stations of at least one task; obtaining a local optimal solution and a global optimal solution in each random solutionaccording to the fitness function; optimizing by using a particle swarm algorithm according to the obtained local optimal solution and global optimal solution to obtain optimal network access and service placement position coordinates, and obtaining a real network access and service placement position according to a preset transition probability; according to the method and the device, two attributes of delay and energy consumption can be considered at the same time, so that the time delay is lower, the energy consumption is lower, the QoS requirements of users are met, and the task unloadingrate is improved.

Description

technical field [0001] The present disclosure relates to the technical field of mobile communication, and in particular to a method and system for network access and service location selection of tasks. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] With the explosive growth of smart devices, many emerging applications, such as augmented reality, face recognition, interactive games, etc., are attracting more and more attention. Such applications not only require intensive computing resources, but also have higher requirements on latency. Due to physical size constraints, mobile devices are limited in terms of computing power and energy. While traditional cloud computing provides centralized services for applications, due to the long distance between the service hosting cloud and users, it will inevitably generate large end-to-end dela...

Claims

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Application Information

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IPC IPC(8): H04W16/18H04W28/16H04W28/24
CPCH04W16/18H04W28/16H04W28/24Y02D30/70
Inventor 翟临博马淑月宋书典杨峰赵景梅
Owner SHANDONG NORMAL UNIV
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