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Joint optimization method and system for mobile edge computing

An edge computing and joint optimization technology, applied in the field of mobile communications, can solve problems such as limited computing resources and inability to process tasks at the same time, achieving the effect of low complexity, good applicability, and improved completion rate

Pending Publication Date: 2022-01-11
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the computing resources of the edge cloud server are limited. When the mobile device has many tasks, it cannot handle all tasks within its coverage area at the same time.

Method used

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  • Joint optimization method and system for mobile edge computing
  • Joint optimization method and system for mobile edge computing
  • Joint optimization method and system for mobile edge computing

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Experimental program
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Effect test

Embodiment 1

[0042] Embodiment 1 of the present disclosure introduces a joint optimization method for mobile edge computing.

[0043] Such as image 3 A joint optimization method for mobile edge computing is shown, including the following steps:

[0044] Step S01: system initialization, obtaining real-time information of each task;

[0045] Step S02: Determine the fitness function according to the current system information;

[0046] Step S03: Priority queue sorting, according to the completion time of each task, arrange the tasks initiated by different users in a priority queue;

[0047] Step S04: Use the AHP to calculate the probability of each possible solution, and calculate the probability of each task selecting its feasible access to the edge cloud and executing the edge cloud with service data according to the order of tasks in the priority queue;

[0048] Step S05: reassign, reassign the tasks according to the redundant time of the tasks, so as to maximize the completion rate.

...

Embodiment 2

[0136] Embodiment 2 of the present disclosure introduces a joint optimization system for mobile edge computing.

[0137] Such as Figure 4 The shown joint optimization system for mobile edge computing adopts the joint optimization method for mobile edge computing introduced in Embodiment 1, including:

[0138] Obtain an information module, which is used to obtain task information of mobile edge computing;

[0139] The calculation probability module is used to obtain the priority queue of the task information according to the acquired task information and the preset fitness function, and calculate the probability of each task in the task information accessing the edge cloud and placing the edge cloud by the service;

[0140] The redistribution module is used to redistribute the task information according to the obtained probability, and obtain the access edge cloud and service placement edge cloud when the preset task information completion rate is reached;

[0141] Wherein, ...

Embodiment 3

[0144] Embodiment 3 of the present disclosure provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the steps in the joint optimization method for mobile edge computing as described in Embodiment 1 of the present disclosure are implemented.

[0145] The detailed steps are the same as the joint optimization method for mobile edge computing provided in Embodiment 1, and will not be repeated here.

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Abstract

The invention provides a joint optimization method and system for mobile edge computing. The joint optimization method comprises the following steps: acquiring task information of mobile edge computing; obtaining a priority queue of the task information according to the obtained task information and a preset fitness function, and calculating the probability that each task accesses the edge cloud and the probability that each service places the edge cloud in the task information; performing redistribution of the task information according to the obtained probability to obtain an access edge cloud and a service placement edge cloud when a preset task information completion rate is reached, wherein the task information is redistributed according to the redundant time of the task information, and the task information is redistributed by taking a preset task information completion rate as a target. According to the method, influences of factors such as storage space, calculation capacity and communication delay are considered at the same time, and the total completion rate of the tasks is maximized within the completion time of each task; therefore, available resources of the edge cloud can be effectively utilized, the completion rate of tasks is improved to the maximum extent, the complexity is low, and the applicability is good.

Description

technical field [0001] The disclosure belongs to the technical field of mobile communication, and in particular relates to a joint optimization method and system for mobile edge computing. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] With the development of mobile devices such as smartphones and tablets, computing-intensive applications (augmented reality, Internet of Vehicles, etc.) emerge as the times require. Such applications usually require large computing resources and low latency, which has led to The demands on data storage, processing and transmission are growing exponentially. These requirements are difficult to meet due to the inherent limitations of mobile devices, such as limited computing resources and battery capacity. Remote cloud servers have unlimited computing and storage capabilities, which can make up for the sho...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06F9/445G06F9/54
CPCG06F9/5072G06F9/5005G06F9/44594G06F9/546G06F2209/548
Inventor 翟临博马淑月宋书典杨峰赵景梅
Owner SHANDONG NORMAL UNIV
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