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VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm

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Abstract

A huge cloud data center makes it possible to offer computing as a utility to customers. However, the main challenge is to fulfill the customer’s dynamic workload requirement seamlessly. Additionally, cloud data center consumes an enormous amount of power due to improper scheduling of virtual machines over the physical machines, which lead to inefficient usage of heterogeneous computing resources. So, to minimize energy consumption in the cloud data center, virtual machines should be scheduled in an energy-efficient way. In this paper, an Artificial Immune System based Virtual Machine Scheduling using Modified Clonal Selection Algorithm (VMS-MCSA) is proposed to schedule virtual machines energy efficiently. The classical Clonal Selection Algorithm(CSA) operators are modified such that they can be applied to the discrete optimization dynamic virtual machine scheduling problem. The randomized mutation operator is proposed, which reschedule VMs at each scheduling interval to handle the dynamicity of workload with minimum virtual machine migrations. Additionally, the VM-consolidation model was proposed for constraint-based virtual machine migration. The proposed VMS-MCSA algorithm is implemented on a cloudsim simulator, and the results show that the VM scheduling using VMS-MCSA is energy-efficient compared to other recent approaches.

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References

  1. Abdelsamea, A., El-Moursy, A.A., Hemayed, E.E., Eldeeb, H.: Virtual machine consolidation enhancement using hybrid regression algorithms. Egypt. Inf. J. 18(3), 161–170 (2017)

    Google Scholar 

  2. Abdessamia, F., Zhang, W.Z., Tian, Y.C.: Energy-efficiency virtual machine placement based on binary gravitational search algorithm. Clust. Comput., pp. 1–12 (2019)

  3. Abualigah, L., Diabat, A.: A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust. Comput., pp. 1–19 (2020)

  4. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  5. Asad, Z., Chaudhry, M.A.R.: A two-way street: green big data processing for a greener smart grid. IEEE Syst. J. 11(2), 784–795 (2016)

    Article  Google Scholar 

  6. Azizi, S., Li, D., et al.: An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Clust. Comput., pp. 1–14 (2020)

  7. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. ACM SIGOPS Oper. Syst. Rev. 37(5), 164–177 (2003)

    Article  Google Scholar 

  8. Barlaskar, E., Singh, Y.J., Issac, B.: Energy-efficient virtual machine placement using enhanced firefly algorithm. Multiagent Grid Syst. 12(3), 167–198 (2016)

    Article  Google Scholar 

  9. Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)

    Article  Google Scholar 

  10. Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concur. Comput. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  11. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  12. Chen, Y.H., Chen, C.Y.: Service oriented cloud vm placement strategy for internet of things. IEEE Access 5, 25396–25407 (2017)

    Article  Google Scholar 

  13. Chu, W.X., Wang, C.C.: A review on airflow management in data centers. Appl. Energy 240, 84–119 (2019)

    Article  Google Scholar 

  14. Cutello, V., Nicosia, G.: The clonal selection principle for in silico and in vitro computing. In: Recent developments in biologically inspired computing, pp. 140–147. IGI Global (2005)

  15. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  16. Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput. Archit. News 35(2), 13–23 (2007)

    Article  Google Scholar 

  17. Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, H.: Energy-aware vm consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. 7(2), 524–536 (2016)

    Article  Google Scholar 

  18. Fard, S.Y.Z., Ahmadi, M.R., Adabi, S.: A dynamic vm consolidation technique for qos and energy consumption in cloud environment. J. Supercomput. 73(10), 4347–4368 (2017)

    Article  Google Scholar 

  19. Fu, X., Zhou, C.: Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Front. Comput. Sci. 9(2), 322–330 (2015)

    Article  MathSciNet  Google Scholar 

  20. Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)

    Article  MathSciNet  Google Scholar 

  21. Hallawi, H., Mehnen, J., He, H.: Multi-capacity combinatorial ordering ga in application to cloud resources allocation and efficient virtual machines consolidation. Future Gener. Comput. Syst. 69, 1–10 (2017)

    Article  Google Scholar 

  22. Hart, E., McEwan, C., Timmis, J., Hone, A.: Advances in artificial immune systems (2011)

  23. Ibrahim, A., Noshy, M., Ali, H.A., Badawy, M.: Papso: a power-aware vm placement technique based on particle swarm optimization. IEEE Access 8, 81747–81764 (2020)

    Article  Google Scholar 

  24. Ilager, S., Ramamohanarao, K., Buyya, R.: Etas: energy and thermal-aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation. Concur. Comput. 31(17), e5221 (2019)

    Article  Google Scholar 

  25. Kaur, G., Bala, A.: Opsa: an optimized prediction based scheduling approach for scientific applications in cloud environment. Clust. Comput., pp. 1–20 (2021)

  26. Kusic, D., Kephart, J.O., Hanson, J.E., Kandasamy, N., Jiang, G.: Power and performance management of virtualized computing environments via lookahead control. Clust. Comput. 12(1), 1–15 (2009)

    Article  Google Scholar 

  27. Li, X., Qian, Z., Lu, S., Wu, J.: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math. Comput. Modell. 58(5–6), 1222–1235 (2013)

    Article  MathSciNet  Google Scholar 

  28. Long, Z., Ji, W.: Power-efficient immune clonal optimization and dynamic load balancing for low energy consumption and high efficiency in green cloud computing. JCM 11(6), 558–563 (2016)

    Google Scholar 

  29. Lu, Y., Sun, N.: An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment. Clust. Comput. 22(1), 513–520 (2019)

    Article  MathSciNet  Google Scholar 

  30. Milan, S.T., Rajabion, L., Darwesh, A., Hosseinzadeh, M., Navimipour, N.J.: Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm. Clust. Comput., pp. 1–9 (2019)

  31. Park, K., Pai, V.S.: Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Oper. Syst. Rev. 40(1), 65–74 (2006)

    Article  Google Scholar 

  32. Quan, D.M., Basmadjian, R., De Meer, H., Lent, R., Mahmoodi, T., Sannelli, D., Mezza, F., Telesca, L., Dupont, C.: Energy efficient resource allocation strategy for cloud data centres. In: Computer and information sciences II, pp. 133–141. Springer (2011)

  33. Ranganathan, P., Leech, P., Irwin, D., Chase, J.: Ensemble-level power management for dense blade servers. ACM SIGARCH Comput. Archit. News 34(2), 66–77 (2006)

    Article  Google Scholar 

  34. Sharifi, M., Salimi, H., Najafzadeh, M.: Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques. J. Supercomput. 61(1), 46–66 (2012)

    Article  Google Scholar 

  35. Sharma, N.K., Guddeti, R.M.R.: Multi-objective resources allocation using improved genetic algorithm at cloud data center. In: Proceedings of the 2016 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), pp. 73–77. IEEE (2016)

  36. Shu, W., Wang, W., Wang, Y.: A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP J. Wirel. Commun. Netw. 2014(1), 1–9 (2014)

    Article  Google Scholar 

  37. Shuja, J., Madani, S.A., Bilal, K., Hayat, K., Khan, S.U., Sarwar, S.: Energy-efficient data centers. Computing 94(12), 973–994 (2012)

    Article  Google Scholar 

  38. Tang, M., Pan, S.: A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process. Lett. 41(2), 211–221 (2015)

    Article  Google Scholar 

  39. Tian, W., He, M., Guo, W., Huang, W., Shi, X., Shang, M., Toosi, A.N., Buyya, R.: On minimizing total energy consumption in the scheduling of virtual machine reservations. J. Netw. Comput. Appl. 113, 64–74 (2018)

    Article  Google Scholar 

  40. Ulker, E.D., Ulker, S.: Comparison study for clonal selection algorithm and genetic algorithm. arXiv:1209.2717 (2012)

  41. Verma, A., Dasgupta, G., Nayak, T.K., De, P., Kothari, R.: Server workload analysis for power minimization using consolidation. In: Proceedings of the 2009 conference on USENIX Annual technical conference, pp. 28–28 (2009)

  42. Wang, J., Huang, C., He, K., Wang, X., Chen, X., Qin, K.: An energy-aware resource allocation heuristics for vm scheduling in cloud. In: Proceedings of the 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp. 587–594. IEEE (2013)

  43. Wang, S., Liu, Z., Zheng, Z., Sun, Q., Yang, F.: Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In: Proceedings of the 2013 International Conference on Parallel and Distributed Systems, pp. 102–109. IEEE (2013)

  44. Xavier, V.A., Annadurai, S.: Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Clust. Comput. 22(1), 287–297 (2019)

    Google Scholar 

  45. Xiao, P., Ni, Z., Liu, D., Hu, Z.: A power and thermal-aware virtual machine management framework based on machine learning. Clust. Comput., pp. 1–18 (2021)

  46. Xiong, A.P., Xu, C.X.: Energy efficient multiresource allocation of virtual machine based on pso in cloud data center. Math. Probl. Eng., 2014 (2014)

  47. Yang, J.H., Sun, L., Lee, H.P., Qian, Y., Liang, Y.C.: Clonal selection based memetic algorithm for job shop scheduling problems. J. Bion. Eng. 5(2), 111–119 (2008)

    Article  Google Scholar 

  48. Yavari, M., Rahbar, A.G., Fathi, M.H.: Temperature and energy-aware consolidation algorithms in cloud computing. J. Cloud Comput. 8(1), 1–16 (2019)

    Article  Google Scholar 

  49. Zareizadeh, Z., Helfroush, M.S., Kazemi, K.: A new multiobjective evolutionary optimization algorithm based on \(\theta\)-multiobjective clonal selection. J. Intell. Fuzzy Syst. 32(3), 1685–1696 (2017)

    Article  Google Scholar 

  50. Zhang, X., Wu, T., Chen, M., Wei, T., Zhou, J., Hu, S., Buyya, R.: Energy-aware virtual machine allocation for cloud with resource reservation. J. Syst. Softw. 147, 147–161 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida-62, India

    Kashav Ajmera & Tribhuwan Kumar Tewari

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  1. Kashav Ajmera
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Correspondence to Kashav Ajmera.

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Ajmera, K., Tewari, T.K. VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm. Cluster Comput 24, 3531–3549 (2021). https://doi.org/10.1007/s10586-021-03320-5

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  • DOI: https://doi.org/10.1007/s10586-021-03320-5

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