Analysis of synchronized storage method for multimedia key areas based on machine learning

  • Lei ChenEmail author


Aiming at the problems of high energy consumption, small amount of stored data, large standard deviation of storage space, high failure rate of storage nodes and poor quality of data storage in existing multimedia key area synchronous storage methods, a machine learning based multimedia key area synchronous storage method is proposed. Using genetic algorithm to calculate the distance between data in multimedia critical area and cluster center, and redistribute cluster set, K-Mean is realized by M-R parallel computing model. Different amounts of data are allocated to the clustered sample data storage nodes to complete the synchronous storage of multimedia key area data. The experimental results show that compared with other methods, under the network regulation of 2800 m × 800 m, the storage energy consumption of the proposed method is in the range of 10 × 103NJ-1250 × 103NJ, and the storage energy consumption is low; The maximum number of data storage is 570, and the amount of stored data is large; the standard deviation of data storage space in the key area of multimedia changes within the range of 1.3–4, and the standard deviation of storage space is small. The proposed method lays a foundation for the further development of data storage technology.


Machine learning Multimedia key area Synchronous storage Energy consumption Data volume 



This work was supported by NSFC “The Research on Personalized APP Graph based Recommendation Model Importing user’s attention” (No. 61772156).


  1. 1.
    El-Rabiaey MA, Areed NFF, Obayya SSA (2016) Novel plasmonic data storage based on nematic liquid crystal layers. J Lightwave Technol 16(7):3726–3732CrossRefGoogle Scholar
  2. 2.
    Han JF (2016) Research on low load storage method of massive data in cloud computing environment. Comput Simul 4(3):390–394MathSciNetGoogle Scholar
  3. 3.
    Hu JW, Wu D, Liu N (2017) Research on the method of passive data storage in link layer optical Fiber network. J Inner Mongolia Normal Univ (Natural Science Edition) 3(2):456–460Google Scholar
  4. 4.
    Huang BH, Wang TJ, Jia FW (2016) Encrypting storage and query method for numeric data in database. Comput Eng 7(6):123–128Google Scholar
  5. 5.
    Li Y, Gai K, Qiu L et al (2016) Intelligent cryptography approach for secure distributed big data storage in cloud computing. Inf Sci C:103–115Google Scholar
  6. 6.
    Nobukawa T, Nomura T (2017) Digital super-resolution holographic data storage based on hermitian symmetry for achieving high areal density. Opt Express 2(1):1326CrossRefGoogle Scholar
  7. 7.
    Varan B, Yener A (2016) Delay constrained energy harvesting networks with limited energy and data storage. IEEE J Select Areas Commun 5(2):1550–1564CrossRefGoogle Scholar
  8. 8.
    Wang J, Huang CG, Wang J et al (2016) Research of distributed storage method for agricultural sci- entific data. Comput Eng Applic 11(6):248–253Google Scholar
  9. 9.
    Wu C, Yoshinaga T, Ji Y et al (2017) A reinforcement learning-based data storage scheme for vehicular ad hoc networks. IEEE Trans Veh Technol 99(6):1–1CrossRefGoogle Scholar
  10. 10.
    Xu YH, Zhu EG, Zhao R et al (2017) Unstructured data storage method for electricity information based on MongoDB index. Proc CSU-EPSA 9(1):93–97Google Scholar
  11. 11.
    Yang XY (2016) Research on key Technologies of Distributed Storage Based on Hadoop's massive data. Autom Instrum 10(3):166–167Google Scholar
  12. 12.
    Yang CT, Shih WC, Huang CL et al (2016) On construction of a distributed data storage system in cloud. Computing 12(8):93–118MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yang DR, Chen Y, Liu SY et al (2017) Research on fault tolerant strategy optimization of storage system for the healthcare big data. J Chin Acad Electron Inform Technol 5(1):546–550Google Scholar
  14. 14.
    Yazdi SMHT, Han MK, Gabrys R et al (2017) Mutually uncorrelated primers for dna-based data storage. IEEE Trans Inf Theory 99(2):1Google Scholar
  15. 15.
    Zhao D, Katsouras I, Asadi K et al (2016) Retention of intermediate polarization states in ferroelectric materials enabling memories for multi-bit data storage. Appl Phys Lett 23(5):1040–1090Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.International Business FacultyBeijing Normal UniversityZhuhaiChina
  2. 2.Management FacultyHaerbin Institute of TechnologyHaerbinChina

Personalised recommendations