Mobile edge computing service placement strategy based on artificial intelligence

A technology of artificial intelligence and edge computing, which is applied in wireless communication, transmission system, electrical components, etc., can solve problems such as the inability to simultaneously optimize network selection and service placement double decision-making

Active Publication Date: 2021-07-23
CHONGQING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

Most of the existing algorithms cannot simultaneously optimize the double decision of network selection and service placement, and this invention will propose a corresponding solution to this problem

Method used

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  • Mobile edge computing service placement strategy based on artificial intelligence
  • Mobile edge computing service placement strategy based on artificial intelligence
  • Mobile edge computing service placement strategy based on artificial intelligence

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specific Embodiment approach

[0023] Such as image 3 As shown, the specific implementation process of the artificial intelligence-based mobile edge computing service placement strategy is as follows:

[0024] Step 1: Firstly, model the entire network system, which needs to establish a macro base station model, a small base station model, and a user model. This step needs to establish sets for base stations (including macro base stations and small base stations), edge clouds (including edge clouds to which macro base stations and small base stations are connected respectively), and all users, and number the sets to facilitate subsequent strategy use symbol identification. This step also needs to confirm how many discretized time slots a time period contains, so as to determine the period of a policy cycle.

[0025] Step 2: Divide the user's delay model into a handover delay model and a non-handover delay model. The switching delay model is divided into queuing delay model, transmission delay model and ca...

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Abstract

The invention relates to a mobile edge computing service placement strategy based on artificial intelligence, and the strategy comprises the following steps: carrying out the modeling of a whole network system, and needing to build a macro base station model, a small base station model, and a user model; dividing the delay model of the user into a switching delay model and a non-switching delay model, wherein the non-switching delay model is divided into a queuing delay model, a transmission delay model and a calculation delay model; establishing a queuing model of the user; after the steps are completed, calculating the total delay perceived by the user; and finally, modeling the problem by using a reinforcement learning method, and establishing a state space, an action space, a revenue signal and a Bellman equation, so that the problem can be solved through an algorithm based on artificial intelligence. The mobile edge computing service placement strategy based on artificial intelligence can provide base station selection and service placement decisions, and realizes network selection and service placement decoupling on the basis of controllable cost.

Description

technical field [0001] The present invention relates to the field of mobile edge computing service management, in particular to a mobile edge computing service placement strategy. Background technique [0002] With the advancement of society and the accelerated deployment of 5G networks, new application scenarios such as augmented reality and virtual reality continue to emerge. These applications often have low latency and large bandwidth requirements, which brings great challenges to mobile devices with insufficient computing and storage capabilities and limited power. A great challenge has come. According to Cisco's forecast, 5G users will reach 1.4 billion in 2023, and the average 5G connection speed will reach 575Mbps. Previous cloud computing scenarios can meet the needs of some scenarios such as immersive games, but cannot meet the requirements of augmented reality applications that have extremely high latency requirements. In 2013, the European Telecommunications Sta...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W16/18H04W16/22H04L29/08
CPCH04W16/18H04W16/22H04L67/60Y02D30/70
Inventor 邹虹白陈阳何鹏崔亚平吴大鹏王汝言
Owner CHONGQING UNIV OF POSTS & TELECOMM
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