Mobile edge computing system resource optimization configuration method

A technology of edge computing and system resources, applied in computing, information technology support systems, program control devices, etc., to achieve the effects of reducing system overhead, minimizing the long-term average cost, and minimizing the long-term average cost of the MEC system

Pending Publication Date: 2022-08-09
SHANGHAI UNIV
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

The introduction of emerging smart grid technologies creates opportunities for improving the energy efficiency of MEC systems, but also brings new challenges to traditional MEC system resource optimization strategies

Method used

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  • Mobile edge computing system resource optimization configuration method
  • Mobile edge computing system resource optimization configuration method
  • Mobile edge computing system resource optimization configuration method

Examples

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Embodiment Construction

[0037] like figure 2 As shown, the application scenario involved in this embodiment, that is, a multi-user MEC system powered by a smart grid, the system includes: an MEC server and mobile users, among which: the MEC server is equipped at a small base station (Basic Station, BS), the base station has built-in energy harvesting equipment and rechargeable batteries, and collects renewable energy (such as wind energy, solar energy, etc.) from the environment through energy harvesting equipment etc.) to reduce the dependence on the smart grid, and the base station also conducts two-way energy transactions with the smart grid according to the dynamic electricity price, so as to reduce the system overhead.

[0038] In the scenario of this example, we set = 10, the energy purchase price α t Produces alpha from a folded normal distribution t =|x|, x~N(0,5), the selling price of energy satisfies β t =0.9α t , energy harvesting A sample of is generated by Weibull distributed win...

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Abstract

A mobile edge computing system resource optimization configuration method comprises the steps of combining a smart power grid with an MEC system, establishing a system model of the MEC system capable of performing bidirectional energy transaction with the power grid, taking the time average weighted sum of the transaction cost of a base station and the total energy consumption of a mobile user as the average cost of the system, and utilizing a random dual subgradient method to optimize the resource allocation of the MEC system. And a virtual energy queue is introduced, and a computing resource optimization decision, a transmission rate control decision and an energy management strategy are determined based on task unloading, resource allocation, energy management and task buffer queue constraints. According to the method, instant decisions can be dynamically made, statistical data of random processes such as channels, renewable energy sources and electricity price changes do not need to be known priori, and the long-term average cost of the MEC system can be minimized under the condition that the user time delay requirement can be guaranteed.

Description

technical field [0001] The invention relates to a technology in the field of smart grid control, in particular to a method for optimizing resource configuration of a mobile edge computing system driven by a smart grid. Background technique [0002] With the rapid evolution and development of mobile communication technology, various wearable smart terminals and corresponding applications have gradually become popular. In the future, there will be a large number of new business requirements, such as virtual reality / augmented reality, ultra-high-definition video, and autonomous driving of the Internet of Vehicles. These services have high requirements for real-time computing capabilities, computing latency, user experience, and bandwidth. To solve these challenges , Mobile Edge Computing (MEC) is considered to be the key technology to realize future 5G mobile communication scenarios. [0003] For mobile edge computing systems, with the dense deployment of small base stations e...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/445G06Q50/06
CPCG06F9/44505G06Q50/06Y02D30/70
Inventor 陈小静温涵斐张舜卿徐树公
Owner SHANGHAI UNIV
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