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Blockchain-assisted caching optimization and data storage methods in edge environment

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Abstract

With the rapid advancement of communication technology in the Internet of Things, a slew of new technologies and applications, such as virtual reality and augmented reality, are developing, placing greater demands on transmission latency and storage capacity. As a newly developed compute architecture, edge computing can serve applications that require low latency and high bandwidth better. By sinking cloud computing capabilities to the user side, edge computing collaborates with the cloud and terminals to achieve controlled processing of massive data. Therefore, in order to cache data that meets user requirements better, this paper proposed a blockchain-assisted caching optimization model and data storage method in the edge environment. In this model, factors such as base station location selection and cache content prediction are considered, with the aim of maximizing the quality of service and user interest. During the experiments of caching optimization, when Zipf is 0.5 and other factors remain constant, the proposed algorithm has an average cache hit rate of 4.22%, 11.03%, 19.34%, and 32.35% higher than the JSCCO algorithm, EETCO algorithm, DPCP algorithm, and RR algorithm, respectively. In terms of data storage, when the storage size of the file is 32 MB and other aspects stay constant, the storage time of the proposed method is 16.26%, 16.94%, and 31.56% lower than the IDFS method, EDDS method, and IISM method, respectively.

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References

  1. Li C, Cai Q, Youlong L (2022) Low-latency edge cooperation caching based on base station cooperation in SDN based MEC. Expert Syst Appl 191:116252

    Article  Google Scholar 

  2. Li C, Liang SongYu, Zhang J, Wang Q-e, Luo Y (2022) Blockchain-based data trading in edge-cloud computing environment. Inf Process Manage 59(1):102786

    Article  Google Scholar 

  3. Li C, Zhang Y, Luo Y (2022) Intermediate data placement and cache replacement strategy under Spark platform. J Parallel Distrib Comput

  4. Jiang Y, Ma M, Bennis M, Zheng F-C, You X (2019) User preference learning-based edge caching for fog radio access network. IEEE Trans Commun 67(2):1268–1283

    Article  Google Scholar 

  5. Mehrizi S, Tsakmalis A, Chatzinotas S, Ottersten B (2019) A feature-based Bayesian method for content popularity prediction in edge-caching networks. In: Proceedings of the IEEE wireless communications and networking conference (WCNC), pp 1–6

  6. Yang P, Zhang N, Zhang S, Yu L, Zhang J, Shen X (2019) Content popularity prediction towards location-aware mobile edge caching. IEEE Trans Multimedia 21(4):915–929

    Article  Google Scholar 

  7. Chen W, Poor HV (2017) Content pushing with request delay information. IEEE Trans Commun 65(3):1146–1161

    Article  Google Scholar 

  8. Wu P, Li J, Shi L, Ding M, Cai K, Yang F (2019) Dynamic content update for wireless edge caching via deep reinforcement learning. IEEE Commun Lett 23(10):1773–1777

    Article  Google Scholar 

  9. Yang Z, Liu Y, Chen Y, Jiao L (2020) Learning automata based Q-learning for content placement in cooperative caching. IEEE Trans Commun 68(6):3667–3680

    Article  Google Scholar 

  10. Somuyiwa SO, Gyorgy A, Gunduz D (2018) A reinforcement-learning approach to proactive caching in wireless networks. IEEE J Sel Areas Commun 36(6):1331–1344

    Article  Google Scholar 

  11. Yu M, Li R (2018) Dynamic popularity-based caching permission strategy for named data networking. In: IEEE international conference on computer supported cooperative work in design. Nanjing: IEEE Press

  12. Fan Q, Lin J, Feng G, Gao Z, Wang H, Li Y (2020) Joint service caching and computation offloading to maximize system profits in mobile edge-cloud computing. In: 2020 16th international conference on mobility, sensing and networking (MSN), pp 244–251. https://doi.org/10.1109/MSN50589.2020.00050

  13. Hao Y, Chen M, Hu L et al (2018) Energy efficient task caching and offloading for mobile edge computing. IEEE Access 6:11365–11373

    Article  Google Scholar 

  14. Li C, Cai Q, Youlong L (2022) Optimal data placement strategy considering capacity limitation and load balancing in geographically distributed cloud. Futur Gener Comput Syst 127:142–159

    Article  Google Scholar 

  15. Liang W, Fan Y, Li K-C, Zhang D, Gaudiot J-L (2020) Secure data storage and recovery in industrial blockchain network environments. IEEE Trans Industr Inf 16(10):6543–6552

    Article  Google Scholar 

  16. Liang W, Tang M, Long J, Peng X, Xu J, Li K (2019) A Secure FaBric blockchain-based data transmission technique for industrial internet-of-things. IEEE Trans Industr Inf 15(6):3582–3592

    Article  Google Scholar 

  17. Dinh TTA, Wang J, Chen G, Rui L, Tan K-L, Ooi BC (2017) BLOCKBENCH: A benchmarking framework for analyzing private blockchains. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 1085–1100

  18. Shah M, Shaikh M, Mishra V, Tuscano G (2020) Decentralized cloud storage using blockchain. In: 2020 4th International conference on trends in electronics and informatics (ICOEI) (48184), pp 384–389

  19. ul Haque A, Ghani MS, Mahmood T (2020) Decentralized transfer learning using blockchain & IPFS for deep learning. In: 2020 International Conference on Information Networking (ICOIN), pp 170–177

  20. Sun J, Yao X, Wang S, Wu Y (2020) Non-repudiation storage and access control scheme of insurance data based on blockchain in IPFS. IEEE Access 8:155145–155155

    Article  Google Scholar 

  21. Li D, Du R, Fu Y, Au MH (2019) Meta-key: a secure data-sharing protocol under blockchain-based decentralized storage architecture. IEEE Netw Lett 1(1):30–33. https://doi.org/10.1109/LNET.2019.2891998

    Article  Google Scholar 

  22. Zheng Q, Li Y, Chen P, Dong X (2018) An innovative IPFS-based storage model for Blockchain. In: 2018 IEEE/WIC/ACM international conference on web intelligence (WI), pp 704-708, https://doi.org/10.1109/WI.2018.000-8

  23. Kumar R, Tripathi R (2019) Implementation of distributed file storage and access framework using IPFS and Blockchain. In: 2019 Fifth international conference on image information processing (ICIIP), pp 246–251. https://doi.org/10.1109/ICIIP47207.2019.8985677

  24. Alizadeh M, Andersson K, Schelén O (2020) Efficient decentralized data storage based on public Blockchain and IPFS. In: 2020 IEEE Asia-Pacific conference on computer science and data engineering (CSDE), pp 1–8. https://doi.org/10.1109/CSDE50874.2020.9411599

  25. http://bdd-data.berkeley.edu/index.html

  26. Dai M, Su Z, Xu Q, Chen W (2019) A Q-learning based scheme to securely cache content in edge-enabled heterogeneous networks. IEEE Access, 7:163 898–163 911

  27. Li C, Liu J, Wang M, Luo Y (May 2022) Fault-tolerant scheduling and data placement for scientific workflow processing in geo-distributed clouds. J Syst Softw 187:111227

    Article  Google Scholar 

  28. Li C, Zhang Y, Gao X, Luo Y (2022) Energy-latency tradeoffs for edge caching and dynamic service migration based on dqn in mobile edge computing. JParallel Distrib Comput 166:15–31

    Article  Google Scholar 

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Acknowledgments

The work was supported by open project of Open Research Fund of Energy Internet Engineering Research Center of Anhui Provincial Department of Education, Anhui Polytechnic University. CAAC Key Laboratory of Civil Aviation Wide Survellence and Safety Operation Management & Control Technology, Civil Aviation University of China (No. 202001). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

Funding

The Fundamental Research Funds for the Central Universities (WUT:2022IVB006).

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Authors and Affiliations

  1. CAAC Key Laboratory of Civil Aviation Wide Surveillance and Safety Operation Management & Control Technology, Civil Aviation University of China, TianJin, People’s Republic of China

    Chunlin Li

  2. College of Power Engineering, Naval University of Engineering, Wuhan, 430033, People’s Republic of China

    Jingjing Guo

  3. Energy Internet Engineering Research Center of Anhui Provincial Department of Education, Anhui Polytechnic University, Wuhu, People’s Republic of China

    Youlong Luo

  4. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li & Youlong Luo

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  1. Jingjing Guo
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  2. Chunlin Li
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Correspondence to Youlong Luo.

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Guo, J., Li, C. & Luo, Y. Blockchain-assisted caching optimization and data storage methods in edge environment. J Supercomput 78, 18225–18257 (2022). https://doi.org/10.1007/s11227-022-04583-4

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