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Resource Scheduling Algorithm Based on Evolutionary Computation in Dynamic Cloud Environment

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

The growing types of resources and changing user requirements in the cloud environment bring great challenges to the resource scheduling problem. In order to solve the problem of resource scheduling in dynamic cloud environment, this paper constructs a virtual dynamic cloud environment resource scheduling model, which realizes resource scheduling by means of virtual machine migration. In this model, the energy consumption of the cloud environment and the quality of service of the cloud environment after virtual machine migration are taken as two optimization objectives. At the same time, we propose a new dynamic multi-objective optimization algorithm (called DCRS-EA) to solve the resource scheduling problem in dynamic cloud environment. DCRS-EA not only detects whether the environment changes, but also estimates the types of changes, in which different types of change are solved by different response strategies. Finally, the experiments show the superior performance of DCRS-EA when comparing to other dynamic strategies on the built optimization model.

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References

  1. Fox, A.M.: Above the clouds: a berkeley view of cloud computing. Eecs Depart. Univ. Calif. Berkeley 53(4), 50–58 (2009)

    Google Scholar 

  2. Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber - physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)

    Article  Google Scholar 

  3. Yang, Z., Liu, M., Xiu, J., Liu, C.: Study on cloud resource allocation strategy based on particle swarm ant colony optimization algorithm. In: IEEE International Conference on Cloud Computing & Intelligence Systems, Hangzhou, pp. 488–491(2012)

    Google Scholar 

  4. Ding, S., Chen, S.P.: Multi-objective ant colony resource allocation algorithm based on packet cluster mapping in cloud computing. Software 39(11), 9–14 (2018)

    Google Scholar 

  5. Mezmaz, M., Melab, N., Kessaci, Y.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71(11), 1497–1508 (2011)

    Article  Google Scholar 

  6. Lee, Y.C., Zoomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)

    Article  Google Scholar 

  7. Lioyd, W.J., et al.: Demystifying the clouds: harnessing resource utilization models for cost effective infrastructure alternatives. IEEE Trans. Cloud Comput. 5(4), 667–680 (2017)

    Article  Google Scholar 

  8. Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, T.: Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. 7(2), 524–536 (2019)

    Article  Google Scholar 

  9. Gong, S.Q., Yin, B.B., Zheng, Z., Cai, K.Y.: Adaptive multivariable control for multiple resource allocation of service-based systems in cloud computing. IEEE Access 7, 13817–13831 (2019)

    Article  Google Scholar 

  10. Zheng, B.: A new dynamic multi-objective optimization evolutionary algorithm. In: Third International Conference on Natural Computation, ICNC 2007, vol. 5, pp. 565–570. IEEE (2007)

    Google Scholar 

  11. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, North Holland, vol. 2, pp. 137–144 (1992)

    Google Scholar 

  12. Zhang, Z., Qian, S.: Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft. Comput. 15(7), 1333–1349 (2011)

    Article  Google Scholar 

  13. Wang, Y., Li, B.: Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment. In: 2009 IEEE Congress on Evolutionary Computation, pp. 630–637. IEEE (2009)

    Google Scholar 

  14. Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1201–1208. ACM (2006)

    Google Scholar 

  15. Peng, Z., Zheng, J.H., Zou, J., Liu, M.: Novel prediction and memory strategies for dynamic multi-objective optimization. Soft. Comput. 19(9), 2633–2653 (2015)

    Article  Google Scholar 

  16. Fan, X., Weber, W.D., Barroso, L.A.: Power Provisioning for a Warehouse-sized computer. In: 34th ACM International Symposium on Computer Architecture, [S.l.]: [s.n.] (2007)

    Google Scholar 

  17. Lin, Q., Jin, G., Ma, Y., Wang, K.C.: A diversity-enhanced resource allocation strategy for decomposition-based multi-objective evolutionary algorithm. IEEE Trans. Cybern. 48(8), 2388–2401 (2018)

    Article  Google Scholar 

  18. Tantar, E., Tantar, A.-A., Bouvry, O.: On dynamic multi-objective optimization, classification and performance measures. In: Proceedings of IEEE CEC, pp. 2759–2766 (2011)

    Google Scholar 

  19. Deb, K., Rao N., U.B., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70928-2_60

    Chapter  Google Scholar 

  20. Ji, L.Q.: Resource optimization for dynamic migration of virtual machines in cloud environment. Shenzhen University (2018)

    Google Scholar 

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Correspondence to Qiyuan Yu .

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Yu, Q., Zhong, S., Luo, N., Huang, P. (2020). Resource Scheduling Algorithm Based on Evolutionary Computation in Dynamic Cloud Environment. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_52

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60801-9

  • Online ISBN: 978-3-030-60802-6

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