Task unloading optimization method for end-edge-cloud collaborative computing

An optimization method and edge computing technology, applied in computing, energy-saving computing, program control design, etc., can solve problems such as lowering user experience quality, overloading of edge servers, and increased processing time of computing tasks, achieving a wide range of applications, good efficiency and Accuracy, the effect of maximizing network revenue

Active Publication Date: 2020-06-05
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The currently commonly used optimization method is to offload the computing tasks of the mobile terminal to the edge server, but there are some difficult problems in this optimization method: first, when the mobile edge computing system contains a large number of mobile devices, the edge server will therefore And overload, which will lead to a significant increase in the processing time of computing tasks, reducing the quality of user experience
Second, this method does not fully consider the collaboration capabilities of mobile terminals, edge servers, and cloud servers. It only combines a single edge server and multiple mobile terminals and ignores the service capabilities of cloud servers, resulting in inefficiency of the entire mobile edge computing system.

Method used

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  • Task unloading optimization method for end-edge-cloud collaborative computing
  • Task unloading optimization method for end-edge-cloud collaborative computing
  • Task unloading optimization method for end-edge-cloud collaborative computing

Examples

Experimental program
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Effect test

Embodiment 1

[0048] see Figure 1 to Figure 2 , a task offload optimization method for end-edge-cloud collaborative computing, mainly including the following steps:

[0049] 1) Obtain the information data of all mobile devices, edge servers, cloud servers and tasks in the current mobile network at the same time.

[0050] The information data of the mobile device and the task include the transmission power Φ of the mobile device i , wireless channel gain g i , the computing power of the mobile device f i L and task size S i . The information data of the edge server includes the computing power F of the edge server E , cache size S E and bandwidth size B E . The information data of the cloud server includes the average computing power F of the cloud server C .

[0051] 2) Establish a mobile edge computing system model.

[0052] The mobile edge computing system model includes a remote cloud server, a local edge server and several different mobile devices.

[0053] 3) Initialize t...

Embodiment 2

[0086] A method for optimizing task offloading of end-edge-cloud collaborative computing, mainly comprising the following steps:

[0087] 1) Obtain the information data of all mobile devices, edge servers, cloud servers and tasks in the current mobile network at the same time.

[0088] 2) Establish a mobile edge computing system model.

[0089] 3) Initialize the parameters of the mobile edge computing system and start the iterative operation.

[0090] 4) Determine the task offloading strategy Ω under the current iteration round j i .

[0091] 5) Determine the resource allocation strategy under the current iteration round j, including the computing power f allocated by the edge server to the mobile device i E and mobile edge computing system allocated bandwidth resources for mobile devices

[0092] 6) Quantify the weighted sum of energy consumption and transmission delay in the mobile edge computing system, and preserve task offloading and resource allocation strategies....

Embodiment 3

[0096] A method for optimizing task offloading of end-edge-cloud collaborative computing, the main steps of which are shown in Embodiment 2, wherein the task offloading strategy Ω under the current iteration round j is determined i The main steps are as follows:

[0097] 1) Calculate the computing power C of the mobile device i , Transmission power Φ i and wireless channel gain g i , and sort the product results in descending order.

[0098] 2) Select the tasks corresponding to the first K product results to unload, upload the selected tasks to be unloaded to the edge server, and sort them in ascending order according to the size of the tasks.

[0099] 3) Calculate the task size S offloaded to the edge server i Does the sum exceed the cache size S of the edge server E . If it exceeds, the tasks are offloaded to the cloud server one by one according to the sorting results. Until the task size in the edge server satisfies the edge server cache.

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Abstract

The invention discloses a task unloading optimization method for end-edge-cloud collaborative computing. The task unloading optimization method mainly comprises the steps of: 1) establishing a mobileedge computing system model; 2) determining a task unloading strategy omega i under a current iteration round j; 3) determining a resource allocation strategy under the current iteration round j; 4) quantifying a weighted sum of energy consumption and transmission delay in a mobile edge computing system, and storing the task unloading strategy and the resource allocation strategy; 5) and outputting an optimal task unloading strategy and an optimal resource allocation strategy in the end-edge-cloud collaborative computing mobile edge computing system. According to the task unloading optimization method, user comprehensive factors such as the task priority and the remaining power of the equipment are comprehensively considered, the optimal unloading position of the task is given, meanwhile,the resource allocation strategy is given through adopting a Cauchy method, and the QOE of the user is improved to a great extent.

Description

technical field [0001] The invention relates to edge computing technology, in particular to an optimization method for task offloading of end-edge-cloud collaborative computing. Background technique [0002] In recent years, due to the increasing number of mobile devices connected to the network and the rapid development of mobile network technology, the development of a series of emerging services such as virtual reality and augmented reality has gradually been promoted. task requirements. As a new computing model, mobile edge computing improves service quality by deploying servers with computing power and computing resources to the edge of the network. In a mobile edge computing network, edge servers can be deployed at or near base stations. Mobile edge computing systems reduce time delay or energy consumption by offloading computing tasks from mobile devices to cloud servers or edge servers. [0003] In the current mobile edge computing system, how to optimize the offl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F8/61G06F9/50
CPCG06F8/62G06F9/5083Y02D10/00
Inventor 李秀华李辉孙川文俊浩熊庆宇范琪琳王悦阳毛玉星李剑
Owner CHONGQING UNIV
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