Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (11): 3194-3200.doi: 10.13229/j.cnki.jdxbgxb.20220886

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Trusted cloud computing platform poly source big data time sequence scheduling algorithm

Rui-shan DU1,2(),Yu-xin CHEN1,Ling-dong MENG2   

  1. 1.School of Computer and Information Technology，Northeast Petroleum University，Daqing 163318，China
    2.Key Laboratory of Oil & Gas Reservoir and Underground Gas Storage Integrity Evaluation of Heilongjiang Province，Northeast Petroleum University，Daqing 163318，China
  • Received:2022-07-13 Online:2023-11-01 Published:2023-12-06

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Abstract:

The poor performance of time series scheduling for multi-source big data on a trusted cloud computing platform can increase platform transmission energy consumption and operating costs， and decrease the utilization rate of multi-source big data. In order to enable the data within the platform to be reasonably scheduled according to task objectives， a trusted cloud computing platform multi-source big data time series scheduling algorithm is proposed. This method first constructs a chaotic time series model to mine the multi-source big data on the trusted cloud computing platform， and then optimizes the data using the wavelet threshold denoising method. The optimized multi-source big data is then combined with the massive parallel Bayesian factorization decomposition method. Based on the time series scheduling strategy output by this method， the time series scheduling of multi-source big data on a trusted cloud computing platform is realized. Experimental results show that the maximum acceleration ratio achieved by this method is 97.2%， the total power of resource scheduling is only 2300 kW， and the load balance deviation does not exceed 0.2.

Key words: trusted cloud computing platform, multi-source big data, chaotic time series model, wavelet threshold denoising, bayesian algorithm, scheduling strategy

CLC Number: 

  • TP39

Fig.1

Acceleration ratio of different methods"

Table 1

Average acceleration ratio of different methods %"

方法8-1加速比8-2加速比8-4加速比
本文38.5068.597.2
文献［3］30.1044.368.9
文献［4］22.4042.672.8

Table 2

Total power of resource scheduling by different methods"

方法资源调度总功率/kW
本文2300
文献［3］6400
文献［4］8000

Fig.2

Load balancing deviation of different methods"

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