RETRACTED CHAPTER: A Cooperative Placement Method for Machine Learning Workflows and Meteorological Big Data Security Protection in Cloud Computing

  • Xinzhao Jiang
  • Wei Kong
  • Xin Jin
  • Jian ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11806)


Cloud computing has proven to be a powerful paradigm in both academia and industry. A variety of meteorological applications using machine learning modeled as the workflows and meteorological big data have been accommodated in the meteorological cloud infrastructure. However, it still faces challenges to guarantee the execution enciency of the meteorological machine-learning workflows and avoid the privacy leakage of the datasets in a semi-trusted cloud. To tackle this challenge, a collaborative placement method (CPM) and a two-factor-based protection mechanism for machine-learning workflows and big data security protection is proposed. Technically, fat-tree topology is leveraged to institute the meteorological cloud infrastructure. Then, the non-dominated sorting differential evolution (NSDE) technique is employed to realize joint optimization of data access time, energy efficiency and load balance. In terms of security protection, the proposed mechanism allows data owners (DOs) to send encrypted data to users through meteorological cloud server (MCS). The DOs are required to formulate access policy and perform ciphertext-policy attribute-based encryption (CP-ABE) on data. In order to decrypt, the users need to possess two factors that a secret key and a security device (e.g., a sensor card in meteorological applications). The ciphertext can be decrypted if and only if the user gathers the secret key and the security device at the same time. Eventually, the experiment evaluates the performance of CPM.


Meteorological big data CPM Machine-learning workflows Load balancing Two-factor protection CP-ABE 


  1. 1.
    Wang, X., Yang, L.T., Liu, H., Deen, M.J.: A big data-as-a-service framework: state-of-the-art and perspectives. IEEE Trans. Big Data 4(3), 325–340 (2017)CrossRefGoogle Scholar
  2. 2.
    Yu, Z., Tian, X., Qiu-Yu, L., Zhao-Guang, P., Si-Jie, L., Qing-Lai, G.: Research on key technologies of cloud energy management for wide area integrated energy internet. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–6. IEEE (2018)Google Scholar
  3. 3.
    Herbst, J.: A machine learning approach to workflow management. In: López de Mántaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000). Scholar
  4. 4.
    Kleine Deters, J., Zalakeviciute, R., Gonzalez, M., Rybarczyk, Y.: Modeling PM2. 5 urban pollution using machine learning and selected meteorological parameters. J. Electr. Comput. Eng. 2017, 14 (2017)Google Scholar
  5. 5.
    Kim, H., Kim, Y.: An adaptive data placement strategy in scientific workflows over cloud computing environments. In: NOMS 2018–2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–5. IEEE (2018)Google Scholar
  6. 6.
    Deng, S., Huang, L., Taheri, J., Zomaya, A.Y.: Computation offloading for service workflow in mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(12), 3317–3329 (2015)CrossRefGoogle Scholar
  7. 7.
    Wang, X., Wang, W., Yang, L.T., Liao, S., Yin, D., Deen, M.J.: A distributed HOSVD method with its incremental computation for big data in cyber-physical-social systems. IEEE Trans. Comput. Soc. Syst. 5(2), 481–492 (2018)CrossRefGoogle Scholar
  8. 8.
    Ren, X., London, P., Ziani, J., Wierman, A.: Joint data purchasing and data placement in a geo-distributed data market. In: ACM SIGMETRICS Performance Evaluation Review, vol. 44, pp. 383–384. ACM (2016)Google Scholar
  9. 9.
    Teng, F., Deng, D., Yu, L., Magoulès, F.: An energy-efficient VM placement in cloud datacenter. In: 2014 IEEE International Conference on High Performance Computing and Communications, 2014 IEEE 6th International Symposium on Cyberspace Safety and Security, 2014 IEEE 11th International Conference on Embedded Software and Systems (HPCC, CSS, ICESS), pp. 173–180. IEEE (2014)Google Scholar
  10. 10.
    Shen, Z., Lee, P.P.C., Shu, J., Guo, W.: Encoding-aware data placement for efficient degraded reads in XOR-coded storage systems. In: 2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), pp. 239–248. IEEE (2016)Google Scholar
  11. 11.
    Xiong, R., Luo, J., Dong, F.: Optimizing data placement in heterogeneous hadoop clusters. Cluster Comput. 18(4), 1465–1480 (2015)CrossRefGoogle Scholar
  12. 12.
    Xiao, Y., Zhang, J., Ji, Y.; Energy efficient placement of baseband functions and mobile edge computing in 5G networks. In: 2018 Asia Communications and Photonics Conference (ACP), pp. 1–3. IEEE (2018)Google Scholar
  13. 13.
    Liu, Z., et al.: A data placement strategy for scientific workflow in hybrid cloud. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 556–563. IEEE (2018)Google Scholar
  14. 14.
    Gu, R., Huang, T., Xue, S., Ruan, F.: A big data placement method based on NSGA-III in meteorological cloud platform. EURASIP J. Wirel. Commun. Netw. 2019, 1 (2019)CrossRefGoogle Scholar
  15. 15.
    Gaggero, M., Caviglione, L.: Model predictive control for energy-efficient, quality-aware, and secure virtual machine placement. IEEE Trans. Autom. Sci. Eng. 99, 1–13 (2018)Google Scholar
  16. 16.
    Zhao, J., Mortier, R., Crowcroft, J., Wang, L.: Privacy-preserving machine learning based data analytics on edge devices. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 341–346. ACM (2018)Google Scholar
  17. 17.
    Wang, S., Liang, K., Liu, J.K., Chen, J., Yu, J., Xie, W.: Attribute-based data sharing scheme revisited in cloud computing. IEEE Trans. Inf. Forensics Secur. 11(8), 1661–1673 (2016)CrossRefGoogle Scholar
  18. 18.
    Peng, X., Qianhong, W., Wang, W., Susilo, W., Domingo-Ferrer, J., Jin, H.: Generating searchable public-key ciphertexts with hidden structures for fast keyword search. IEEE Trans. Inf. Forensics Secur. 10(9), 1993–2006 (2017)CrossRefGoogle Scholar
  19. 19.
    Liu, J.K., Liang, K., Susilo, W., Liu, J., Xiang, Y.: Two-factor data security protection mechanism for cloud storage system. IEEE Trans. Comput. 65(6), 1992–2004 (2016)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Xu, X., et al.: An IoT-oriented data placement method with privacy preservation in cloud environment. J. Netw. Comput. Appl. 124, 148–157 (2018)CrossRefGoogle Scholar
  21. 21.
    Ren, Y., Suganthan, P.N., Srikanth, N.: A novel empirical mode decomposition with support vector regression for wind speed forecasting. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1793–1798 (2014)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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