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Intelligent hybrid optimization method for service placement in mobile edge computing

A technology of edge computing and optimization methods, applied in computing, computing models, energy-saving computing, etc., can solve problems such as error, model cost, equipment, and server energy consumption.

Active Publication Date: 2021-02-23
HUAQIAO UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

First, there is still a gap between the prediction or assumption of the mobility of mobile terminal equipment and the actual application scenario, which will cause large errors
Second, the model cost of online learning and the energy consumption of equipment and servers are relatively large

Method used

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  • Intelligent hybrid optimization method for service placement in mobile edge computing
  • Intelligent hybrid optimization method for service placement in mobile edge computing
  • Intelligent hybrid optimization method for service placement in mobile edge computing

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Embodiment Construction

[0056] The present invention will be further described below through specific embodiments.

[0057] An embodiment of the present invention provides an intelligent hybrid optimization method for service placement in mobile edge computing, including the following steps:

[0058] S1: Transform the service placement problem in mobile edge computing into an optimization problem model through the particle swarm optimization model, and initialize it;

[0059] Intelligent hybrid optimization method initialization; transform the problem into an optimization problem model through PSO, initialize the number of particle swarms, the number of MEC servers, the number of iterations, and the maximum energy consumption constraint, and randomly give each particle a random position and speed.

[0060] S2: The model iterates and uses the shrinkage factor to adjust the speed of the particles;

[0061] Update the speed and position of the particle; each time the particle updates its position, cons...

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Abstract

The invention provides an intelligent hybrid optimization method for service placement in mobile edge computing, which comprises the following steps of: converting a service placement problem in mobile edge computing into an optimization problem model through a particle swarm optimization model, and initializing the optimization problem model; iterating the model and adjusting the speed of the particles by utilizing a contraction factor; calculating the transmission delay, the calculation delay, the calculation energy consumption, the transmission energy consumption and the total service costof the system; calculating the corresponding total service cost of the system according to the speed and the position of the particle of each iteration, and updating the global optimal solution; further updating the globally optimal solution by adopting a simulated annealing algorithm; according to the method provided by the invention, the system performance of service placement can be remarkablyimproved, the total service cost of the system is reduced, and the system service delay is reduced.

Description

technical field [0001] The invention relates to the field of service placement algorithm optimization in mobile edge computing, in particular to an intelligent hybrid optimization method for service placement in mobile edge computing. Background technique [0002] With the rapid development of the Internet of Things, the number of Internet of Things terminal devices has increased rapidly. IoT Analytics, a research institution focusing on IoT, Machine to Machine (M2M) and Industry 4.0, released an IoT platform report in December 2019: the number of active IoT devices in the world will reach 10 billion in 2020, and it is predicted that it will reach 220 in 2025. billion units. These mobile terminal devices will generate massive data and computing tasks, but due to the limitations of computing power and storage capacity of the underlying mobile terminal devices, these underlying mobile terminal devices are not enough to support themselves to complete computing tasks. Undoubte...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06N3/00
CPCG06F9/5027G06F9/5072G06N3/006G06F2209/5012Y02D10/00
Inventor 王田张依琳孙兵吴尤可张亿文高振国
Owner HUAQIAO UNIVERSITY
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