A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems

  • Lianyong Qi
  • Yi Chen
  • Yuan Yuan
  • Shucun Fu
  • Xuyun Zhang
  • Xiaolong XuEmail author
Part of the following topical collections:
  1. Special Issue on Smart Computing and Cyber Technology for Cyberization


Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method.


Cyber-physical systems QoS Energy conservation VM scheduling Cloud 



This research is supported by the National Science Foundation of China under grant no. 61702277 and no. 61872219.


  1. 1.
    Akkaya, I., Derler, P., Emoto, S., Lee, E.: Systems engineering for industrial cyber–physical systems using aspects. Proc. IEEE 104(5), 997–1012 (2016)Google Scholar
  2. 2.
    Alam, K., Saddik, A.: C2ps: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5, 2050–2062 (2016)Google Scholar
  3. 3.
    Canali, C., Chiaraviglio, L., Lancellotti, R., Shojafar, M.: Joint minimization of the energy costs from computing, data transmission, and migrations in cloud data centers. IEEE Trans. Green Commun. Netw. 2(2), 580–595 (2018)Google Scholar
  4. 4.
    Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(4), 974–983 (2015)Google Scholar
  5. 5.
    Chen, Y., Huang, J., Lin, C., Hu, J.: A partial selection methodology for eficient QoS-aware service composition. IEEE Trans. Serv. Comput. 8(3), 384–397 (2015)Google Scholar
  6. 6.
    Chen, Y., Huang, J., Lin, C., Shen, X.: Multi-objective service composition with QoS dependencies. IEEE Trans. Cloud Comput. (2016)Google Scholar
  7. 7.
    Chiang, Y., Ouyang, Y., Hsu, C.: An efficient green control algorithm in cloud computing for cost optimization. IEEE Trans. Cloud Comput. 3(2), 249–262 (2015)Google Scholar
  8. 8.
    Dou, W., Xu, X., Meng, S., Zhang, X., Hu, C., Yu, S., Yang, J.: An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data. Concurrency and Computation: Practice and Experience, 29(14), e3909 (2017)Google Scholar
  9. 9.
    Gai, K., Qiu, M., Zhao, H., Sun, X.: Resource management in sustainable cyber-physical systems using heterogeneous cloud computing. IEEE Transactions on Sustainable Computing 3(2), 60–72 (2018)Google Scholar
  10. 10.
    Garcia-Valls, M., Bellavista, P., Gokhale, A.: Reliable software technologies and communication middleware: a perspective and evolution directions for cyber-physical systems, mobility, and cloud computing. Futur. Gener. Comput. Syst. 71, 171–176 (2017)Google Scholar
  11. 11.
    Gravina, R, Ma, C, Pace, P, Aloi, G, Russo, W, Li, W, Fortino, G.: Cloud-based activity-aaservice cyber–physical framework for human activity. Futur. Gener. Comput. Syst. 75, 158–171 (2017)Google Scholar
  12. 12.
    Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Top. Comput. 5(1), 108–119 (2017)Google Scholar
  13. 13.
    Hasan, M., Kouki, Y., Ledoux, T., Pazat, J.: When Green SLA becomes a possible reality in cloud computing. IEEE Trans. Cloud Comput. 5(2), 249–262 (2017)Google Scholar
  14. 14.
    Hasan, M., Kouki, Y., Ledoux, T., Pazat, J.: When Green SLA becomes a possible reality in cloud computing. IEEE Trans. Cloud Comput. 5(2), 249–262 (2017)Google Scholar
  15. 15.
    Hong, H., El-Ganainy, T., Hsu, C., Harras, K., Hefeeda, M.: Disseminating multilayer multimedia content over challenged networks. IEEE Trans. Multimedia 20 (2), 345–360 (2018)Google Scholar
  16. 16.
    Hossain, M., Malhotra, J.: Cloud-supported cyber–physical localization framework for patients monitoring. IEEE Syst. J. 11(1), 118–127 (2017)Google Scholar
  17. 17.
    Jiang, W., Hu, S., Liu, Z.: Top K query for QoS-aware automatic service composition. IEEE Trans. Serv. Comput. 7(4), 681–695 (2014)Google Scholar
  18. 18.
    Kumar, N., Zeadally, S., Misra, S.: Mobile cloud networking for efficient energy management in smart grid cyber-physical systems. IEEE Wirel. Commun. 23 (5), 100–108 (2016)Google Scholar
  19. 19.
    Lee, J., Bagheri, B., Kao, H.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters 3, 18–23 (2015)Google Scholar
  20. 20.
    Liu, Y., Liu, A., Guo, S., Li, Z., Choi, Y., Sekiya, H.: Context-aware collect data with energy efficient in Cyber–physical cloud systems, Futur. Gener. Comput. Syst. (2017)Google Scholar
  21. 21.
    Lu, C., Saifullah, A., Li, B., Sha, M., Gonzalez, H., Gunatilaka, D., Wu, C., Nie, L., Chen, Y.: Real-time wireless sensor-actuator networks for industrial cyber-physical systems. Proc. IEEE 104(5), 1013–1024 (2016)Google Scholar
  22. 22.
    Nir, M., Matrawy, A., St-Hilaire, M.: Economic and energy considerations for resource augmentation in mobile cloud computing. IEEE Trans. Cloud Comput. 6(1), 99–113 (2018)Google Scholar
  23. 23.
    Nir, M., Matrawy, A., St-Hilaire, M.: Economic and energy considerations for resource augmentation in mobile cloud computing. IEEE Trans. Cloud Comput. 6(1), 99–113 (2018)Google Scholar
  24. 24.
    Qi, L., Meng, S., Zhang, X., Wang, R., Xu, X., Zhou, Z., Dou, W.: An exception handling approach for privacy-preserving service recommendation failure in a cloud environment. Sensors 18(7), 2037 (2018)Google Scholar
  25. 25.
    Rahman, N., Glisson, W., Yang, Y., Choo, K.: Forensic-by-design framework for cyber-physical cloud systems. IEEE Cloud Computing 3(1), 50–59 (2016)Google Scholar
  26. 26.
    Rodriguez-Mier, P., Mucientes, M., Lama, M.: Hybrid optimization algorithm for large-scale QoS-aware service composition. IEEE Trans. Serv. Comput. 10(4), 547–559 (2017)Google Scholar
  27. 27.
    Sadooghi, I., Martin, J., Li, T., Brandstatter, K., Maheshwari, K., Ruivo, T., Garzoglio, G., Timm, S., Zhao, Y., Raicu, I.: Understanding the performance and potential of cloud computing for scientific applications. IEEE Trans. Cloud Comput. 5(2), 358–371 (2017)Google Scholar
  28. 28.
    Shah1, T., Yavari, A., Mitra, K., Saguna, S., Jayaraman, P., Rabhi, F., Ranjan, R.: Remote health care cyber-physical system: quality of service (QoS) challenges and opportunities. IET Cyber-Physical Systems 1(1), 40–48 (2016)Google Scholar
  29. 29.
    Shu, Z., Wan, J., Zhang, D., Li, D.: Cloud-integrated cyber-physical systems for complex industrial applications. Mobile Netw. Appl. 21(5), 865–878 (2016)Google Scholar
  30. 30.
    Wang, S., Lei, T., Zhang, L., Hsu, C., Yang, F.: Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems. Futur. Gener. Comput. Syst. 61, 118–127 (2016)Google Scholar
  31. 31.
    Xu, X., Dou, W., Zhang, X., Chen, J.: Enreal: an energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans. Cloud Comput. 4(2), 166–179 (2016)Google Scholar
  32. 32.
    Xu, X., Dou, W., Zhang, X., Hu, C., Chen, J.: A traffic hotline discovery method over cloud of things using big taxi GPS data. Software: Practice and Experience 47(3), 361–377 (2017)Google Scholar
  33. 33.
    Xu, X., Zhao, X., Ruan, F., Zhang, J., Tian, W., Dou, W., Liu, A.: Data placement for privacy-aware applications over big data in hybrid clouds. Secur. Commun. Netw. 2017, 1–15 (2017)Google Scholar
  34. 34.
    Yu, X., Xue, Y.: Smart grids: a cyber–physical systems perspective. Proc. IEEE 104(5), 1058–1070 (2016)MathSciNetGoogle Scholar
  35. 35.
    Yue, X., Cai, H., Yan, H., Zou, C., Zhou, K.: Cloud-assisted industrial cyber-physical systems: an insight. Microprocess. Microsyst. 39(8), 1262–1270 (2015)Google Scholar
  36. 36.
    Zhang, Y., Qiu, M., Tsai, C., Mehedi Hassan, M., Alamri, A.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2017)Google Scholar
  37. 37.
    Zheng, J., Cai, Y., Wu, Y., Shen, X.: Dynamic computation offloading for mobile cloud computing, A stochastic game-theoretic approach. IEEE Trans. Mobile Comput. 18(4), 771–786 (2018)Google Scholar
  38. 38.
    Zhou, B., Dastjerdi, A., Calheiros, R., Srirama, S., Buyya, R.: A context-aware offloading framework for heterogeneous mobile cloud. IEEE Trans. Serv. Comput. 10(5), 797–810 (2017)Google Scholar
  39. 39.
    Zhu, X., Yang, L., Chen, H., Wang, J., Yin, Shu, Liu, X.: Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans. Cloud Comput. 2 (2), 168–180 (2014)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringQufu Normal UniversityJiningChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  4. 4.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand
  5. 5.Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina
  6. 6.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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