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Dynamic Migration Algorithm of Virtual Network Aware Data Based on Machine Learning

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Abstract

The current data dynamic migration algorithm ignores the attribute characteristics of data in the process of data layout, which leads to more iterations of data in perceptual virtual network, longer downtime of dynamic migration and lower migration efficiency. To solve this problem, a dynamic migration algorithm of perceptual data in virtual network based on machine learning is proposed. Through machine learning algorithm to mine the attribute characteristics of virtual network perception data, Moran's I index is obtained to analyze the correlation index of perception data. By calculating the spatial location of data perception, the data center with less workload in virtual network is selected, and the data center of each data center is calculated. By determining the target node, selecting the migration sensing data and setting the migration factor as the limiting condition, the dynamic migration of sensing data is realized. Experimental results show that the proposed algorithm can effectively reduce the number of iterative replication rounds, shorten the downtime of dynamic migration, and improve the efficiency of virtual network migration in the environment of high dirty page rate and low dirty page rate.

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Correspondence to Chao Bi.

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Bi, C. Dynamic Migration Algorithm of Virtual Network Aware Data Based on Machine Learning. Mobile Netw Appl 27, 965–974 (2022). https://doi.org/10.1007/s11036-022-01915-9

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