Micro-cloud node placement method based on wireless metropolitan area network

Abstract

The invention relates to a micro-cloud node placement method based on a wireless metropolitan area network. The method comprises steps of selecting K access points from a wireless metropolitan area network to place micro cloud nodes; directly encoding the particles into access point serial numbers; optimizing algorithm based on particle swarm, firstly, initializing a batch of particles randomly,calculating the fitness value of each particle through a fitness function, recording the optimal value as the particle optimal value of the particle; meanwhile, selecting the individual with the optimal fitness value as a population optimal particle; recording the fitness value of the particle as the optimal value of the population particle; and performing mutation operation and excellent individual crossover operation on the particles during each iteration to obtain new particles, selecting an optimal value of each particle and an optimal value of population particles, entering the next generation, and repeating iteration to a preset threshold number of times to complete placement search of the micro-cloud nodes. By optimizing the placement of the micro-cloud nodes, the average response time of user tasks is better reduced, and high-efficiency and low-delay services are obtained.

Description

technical field [0001] The invention relates to a micro-cloud node placement method based on a wireless metropolitan area network. Background technique [0002] With the advent of the Internet of Everything, network connection objects are expanding from people to things. Smart devices such as mobile phones and pads play an increasingly important role in people's daily life, study, social interaction, work and other fields. At the same time, various sensors, smart meters, and smart cameras are widely used in industries such as industry and agriculture, medical care, education, transportation, smart home, and environmental protection. According to statistics, as of 2017, mobile cloud traffic has accounted for 84% of the entire mobile traffic. Moreover, according to the forecast of IDC, the total amount of global mobile data will reach 40,000 exabytes in 2020, with a compound annual growth rate of 36%, and mobile cloud traffic will account for 94% of the entire mobile traffic...

AI Technical Summary

Problems solved by technology

However, compared with the cloud computing center, the micro-cloud has the following disadvantages: the micro-cloud can only be accessed through a small-coverage Wi-Fi access point, and compared with the cloud data center, the computing resources are obviously insufficient.

[0006] Although there h

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