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Replication management in peer-to-peer cloud storage systems

Abstract

Data availability represents one of the primary functionalities of any cloud storage system since it ensures uninterrupted access to data. A common solution used by service providers that increase data availability and improve cloud performance is data replication. In this paper, we present a dynamic data replication strategy that is based on a hybrid peer-to-peer cloud architecture. Our proposed strategy selects the most popular data for replication. To determine the proper nodes for storing popular data, we employ not only the feature specifications of storage nodes, but also the relevant structural positions in the cloud network. Our simulation results show the impact of using features such as data popularity, and structural characteristics in improving network performance and balancing the storage nodes, and reducing user response time.

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References

  1. 1.

    Nachiappan, R., Javadi, B., Calheiros, R.N., Matawie, K.M.: Cloud storage reliability for Big Data applications: A state of the art survey. J. Netw. Comput. Appl. 97, 35–47 (2017)

    Article  Google Scholar 

  2. 2.

    W. Li, Y. Yang and D. Yuan, "Literature Review," in Reliability Assurance of Big Data in the Cloud: Cost-Effective Replication-based Storage, Melbourne, Australia, School of Software and Electrical Engineering Swinburne University of Technology Hawthorn, 2015, pp. 9–17.

  3. 3.

    A. S. Tanenbaum and M. Van Steen, Distributed Systems: Principles and Paradigms, Prentice-Hall, 2007.

  4. 4.

    Hanen, J., Kechaou, Z., Ayed, M.B.: An enhanced healthcare system in mobile cloud computing environment. Vietnam Journal of Computer Science 3(4), 267–277 (2016)

    Article  Google Scholar 

  5. 5.

    M. R. Mesbahi, A. M. Rahmani and M. Hosseinzadeh, "Reliability and high availability in cloud computing environments: a reference roadmap," Human-centric Computing and Information Sciences, vol. 8, no. 20, 2018.

  6. 6.

    Q. Wei, B. Veeravalli, B. Gong, L. Zeng and D. Feng, "CDRM: A Cost-effective dynamic replication management scheme for cloud," in IEEE International Conference on Cluster Computing, Crete, Greece, 2010.

  7. 7.

    Yang, J.-P.: Efficient load balancing using active replica management in a storage system. Math. Probl. Eng. 2016(1), 1–9 (2016)

    Google Scholar 

  8. 8.

    J. A. Hoxmeier and C. Dicesare, "System Response Time and User Satisfaction: An Experimental Study of Browser-based Applications," AMCIS 2000 Proceedings, 2000.

  9. 9.

    Tos, U., Mokadem, R., Hameurlain, A., Ayav, T., Bora, S.: Ensuring performance and provider profit through data replication. Clust. Comput. 21(3), 1479–1492 (2018)

    Article  Google Scholar 

  10. 10.

    A. Shakarami, M. Ghobaei-Arani, A. Shahidinejad, M. Masdari and H. Shakarami, "Data replication schemes in cloud computing: a survey," Cluster Comput, 2021.

  11. 11.

    B. Alami Milani and N. Jafari Navimipour, "A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions," Journal of Network and Computer Applications, 64, 229–238, 2016.

  12. 12.

    B. Tomas and B. Vuksic, "Peer to peer distributed storage and computing cloud system," in 34th International Confrence on Information Technology Interface, Cavtat, 2012.

  13. 13.

    T. Amrit, "Platform architecture," in Platform ecosystems: aligning architecture, governance, and strategy, Newnes, 2013, pp. 73–117.

  14. 14.

    Jafari Navimipour, N., Sharifi Milani, F.: A comprehensive study of the resource discovery techniques in Peer-to-Peer networks. Peer-to-Peer Netw Appl 8, 474–492 (2015)

    Article  Google Scholar 

  15. 15.

    Barabási, A.L.: Network Science. Cambridge University Press, Cambridge (2016)

    MATH  Google Scholar 

  16. 16.

    Newman, M.: The large-scale structure of networks. In: Newman, M. (ed.) Networks: An Introduction, pp. 235–270. Oxford University Press, Oxford (2010)

    Chapter  Google Scholar 

  17. 17.

    Meng, X.: A churn-aware durable data storage scheme in hybrid P2P networks. J. Supercomput. 74, 183–204 (2018)

    Article  Google Scholar 

  18. 18.

    Mohammadi, B., Jafari Navimipour, N.: Data replication mechanisms in the peer-to-peer networks. Int. J. Commun Syst 32, e3996 (2019)

    Article  Google Scholar 

  19. 19.

    Cherbal, S., Barouchi, I.: ZRR-P2P: zone-based mechanism for data replication and research optimization in unstructured P2P Systems. International Information and Engineering Technology Association 26, 23–32 (2021)

    Google Scholar 

  20. 20.

    R. Upra, R. Chaisricharoen and M. Yaibuates, "Personal Cloud P2P," Wireless Personal Communications, 2021.

  21. 21.

    Shen, H., Liu, G.: A lightweight and cooperative multifactor considered file replication method in structured P2P systems. IEEE Trans. Comput. 62(11), 2115–2130 (2013)

    MathSciNet  Article  Google Scholar 

  22. 22.

    Hassanzadeh-Nazarabadi, Y., Küpçü, A., Özkasap, Ö.: Decentralized and locality aware replication method for DHT-based P2P storage systems. Futur. Gener. Comput. Syst. 84, 32–46 (2018)

    Article  Google Scholar 

  23. 23.

    Shen, H., Liu, G., Chandler, H.: Swarm intelligence based file replication and consistency maintenance in structured P2P file sharing systems. IEEE Trans. Comput. 64(10), 2953–2967 (2015)

    MathSciNet  Article  Google Scholar 

  24. 24.

    M. Rahmani and M. Benchaïba, "An efficient replication scheme to increase file availability in mobile P2P systems," in International Symposium on Networks, Computers and Communications (ISNCC), 2017.

  25. 25.

    M. Ali, K. Bilal, S. U. Khan, B. Veeravalli, K. Li and A. Y. Zomaya, "DROPS: Division and Replication of data in cloud for Optimal Performance and Security," in IEEE Transactions on Cloud Computing, 2018.

  26. 26.

    Sun, S.Y., Yao, W.B., Li, X.Y.: DARS: A dynamic adaptive replica strategy under high load Cloud-P2P. Futur. Gener. Comput. Syst. 78, 31–40 (2018)

    Article  Google Scholar 

  27. 27.

    L. Metcalf and . W. Casey, "Graph Theory," in Cybersecurity and Applied Mathematics, Rockland, Massachusetts, Syngress, 2016, pp. 67–94.

  28. 28.

    Parau, P., Lemnaru, C., Dinsoreanu, M., Potolea, R.: “Opinion Leader Detection,” in Sentiment Analysis in Social Networks, pp. 157–170. Morgan Kaufmann, Romania (2017)

    Book  Google Scholar 

  29. 29.

    Wei, D., Deng, X., Zhang, X., Deng, Y., Mahadevan, S.: Identifying influential nodes in weighted networks based on evidence theory. Phys. A 392(10), 2564–2575 (2013)

    Article  Google Scholar 

  30. 30.

    S. K. Patro and K. K. Sahu, "Normalization: {A} Preprocessing Stage," Computer Research Repository, 2015.

  31. 31.

    Chen, G., Wang, X., Li, X.: “Preliminaries,” in Fundamentals of Complex Networks: Models, pp. 15–90. Wiley, Structures and Dynamics (2015)

    Google Scholar 

  32. 32.

    X. Bai, H. Jin, X. Liao, X. Shi and Z. Shao, "RTRM: A response time-based replica management strategy for cloud storage system," in International Conference on Grid and Pervasive Computing, Berlin, 2013.

  33. 33.

    Gao, G., Li, R., Wen, K., Gu, X.: Proactive replication for rare objects in unstructured peer-to-peer networks. J. Netw. Comput. Appl. 35(1), 85–96 (2012)

    Article  Google Scholar 

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Correspondence to Fatemeh Raji.

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Majed, A., Raji, F. & Miri, A. Replication management in peer-to-peer cloud storage systems. Cluster Comput (2021). https://doi.org/10.1007/s10586-021-03395-0

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Keywords

  • P2P cloud storage systems
  • Data replication
  • Data availability
  • Load balancing
  • Response time