Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (2): 196-206.doi: 10.11896/jsjkx.241200199

• Database & Big Data & Data Science • Previous Articles     Next Articles

Data Placement Strategy Based on Erasure Code in Data Space

LIN Bing1,3, JIANG Haiou2, TAN Xiao1, CHEN Xing3,4 , ZHENG Yuheng3,4   

  1. 1 College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China
    2 Advanced Institute of Big Data,Beijing,National Key Laboratory of Data Space Technology and System,Beijing 100195,China
    3 Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China
    4 College of Computer and Data Science/College of Software,Fuzhou University,Fuzhou 350108,China
  • Received:2024-12-30 Revised:2025-03-28 Published:2026-02-10
  • About author:LIN Bing,born in 1986,Ph.D,associate professor,postgraduate supervisor,is a member of CCF(No.83773M).His main research interests include cloud computing technology and computatio-nal intelligence.
    JIANG Haiou,born in 1987,Ph.D,associate researcher.Her main research interests include cloud computing technology,big data and service computing.
  • Supported by:
    Natural Science Foundation of China(62072108),University-Industry Cooperation of Fujian Province(2022H6024,2021H6026),Founded Projects of the National Key Laboratory of Data Space Technology and Systems(QZQC2024015-3),Fujian Provincial Special Fund for Promoting the High-Quality Development of Marine and Fishery Industries(FJHYF-ZH-2023-02) and Fujian Province Key Technology Innovation and Industrialization Projects(2024XQ004).

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Abstract: In response to the multi-objective optimization layout problem of integrated data within scientific workflows in cloud-edge environments,factors such as data reliability,workflow execution latency,and data center load balancing are considered,and a data placement based on erasure coding within the data space is proposed.Firstly,low-storage-overhead erasure code redundancy technology is proposed to provide fault tolerance in scientific workflow execution,and a data space is constructed to manage the diverse data generated by the workflow.Secondly,an Interactive Multi-Objective Evolution Algorithm(IMOEA) is designed to simultaneously optimize execution latency and datacenter load balancing.By interacting with decision-makers,the algorithm generates solutions that better align with the decision-makers’ expectations,enhancing the personalization and acceptability of the optimization results.Experimental results show that for workflows of different scales and types,compared to other algorithms such as DIST,MOGA,and RAND,IMOEA reduces spatial metrics(Space,SP) by 2.3%~36.34%,15.71%~44.01%,and 22.50%~47.64%,and improves hypervolume metrics(Hypervolume,HV) by 7.84%~38.23%,14.65%~48.4%,and 45.01%~109.45%,respectively.Additionally,IMOEA algorithm effectively responds to decision-makers’ preferences,finding satisfactory data placement solutions.

Key words: Data space, Edge-cloud environments, Scientific workflows, Data placement, Erasure code, Multi-objective optimization

CLC Number: 

  • TP338
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