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Virtual machine scheduling strategy based on machine learning algorithms for load balancing

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Figure

Fig. 1 Flow chart of support vector regression optimized by genetic algorithms
Fig. 2 Server load prediction comparison
Fig. 3 Comparison of server load prediction errors
Figure 9of the performance interference between ESA_DE andIQR_MC, LRR_MMT, and MAD_RS at different time.It could be clearly seen from the graph that in theinitial stage, the performance interference of the fouralgorithms was very large, which was caused by morevirtual machine migration in the initial stage; with thegradual reduction of the migration number, the per-formance interference between virtual machines alsodecreased; and the performance interference of theESA_DE algorithm proposed in this paper was the is an experimental comparison and analysissmallest among the four algorithms.
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