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Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm

Author

Listed:
  • Qiang Wang

    (Information Construction and Management Center, Jinling Institute of Technology, Nanjing 211169, China
    These authors contributed equally to this work.)

  • Dong Yu

    (School of Electrical Engineering, Southeast University, Sipailou No. 2, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Jinyu Zhou

    (Information Construction and Management Center, Jinling Institute of Technology, Nanjing 211169, China)

  • Chaowu Jin

    (Institute of Electrical and Mechanical, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

Since there is a longitudinal and horizontal penetration problem between multi-level data centers in the smart grid information transmission network. Based on the improved Simulated Annealing algorithm, this paper proposes a data storage optimization model for smart grids based on Hadoop architecture. Combining the characteristics of distributed storage in cloud computing, the smart grid data are equivalent to a task-oriented data set. The smart grid information platform is flattened, equal to a collection of multiple distributed data centers. The smart grid data over time were counted to derive the dependencies between task sets and data sets. According to the dependency between task sets and data sets, the mathematical model was established in combination with the actual data transmission of the power grid. The optimal transmission correspondence between each data set and the data center was calculated. An improved Simulated Annealing algorithm solves the longitudinal and horizontal penetration problem between multi-level data centers. When generating a new solution, the Grey Wolf algorithm provides direction for finding the optimal solution. This paper integrated the existing business data and computational storage resources in the smart grid to establish a mathematical model of the affiliation between data centers and data sets. The optimal distribution of the data set was calculated, and the optimally distributed data set was stored in a distributed physical disk. Arithmetic examples were used to analyze the efficiency and stability of several algorithms to verify the improved algorithm’s advantages, and the improved algorithms’ effectiveness was confirmed by simulation.

Suggested Citation

  • Qiang Wang & Dong Yu & Jinyu Zhou & Chaowu Jin, 2023. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7388-:d:1136138
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