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A high-efficiency learning model for virtual machine placement in mobile edge computing

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Abstract

Mobile edge computing requires more and more high-performance servers, resulting in increasing energy consumption. As a well-established way to reduce energy consumption, virtual machine placement can be utilized to optimize the cost of wide-deployment of servers. However, traditional strategies tend to focus on single indicators, there are few existing research taking time delay limitation into account while solving energy consumption problems. In this paper, we propose a brand new method to settle the problems listed above, which is able to reduce the placement time of virtual machine and energy consumption. First, considering the excellent performance of bat swarm algorithm in NP-hard problem, we introduced the second order oscillation factor to avoid premature convergence, and combined the order exchange and migration local search technology. we proposed the OEMBA algorithm, which integrates underutilized servers to save energy. Subsequently, an improved Long Short-Term Memory model is utilized to fasten the placement of virtual machines and reduce latency based on historical data. Our results indicate that the improved learning model can save energy consumption and reduce placement latency.

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References

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

    Article  Google Scholar 

  2. Kotas, C., Naughton, T., Imam, N.: A comparison of Amazon Web Services and Microsoft Azure cloud platforms for high performance computing. In: Proceedings of the 2018 IEEE International Conference on Consumer Elec-tronics (ICCE), pp. 1–4. Las Vegas, NV (2018)

  3. Verma, A., Malla, D., Choudhary, A.K., Arora, V.: A detailed study of azure platform & its cognitive services. In: Proceedings of the International Conference on machine learning, big data, cloud and parallel computing (COMITCon). vol. 2019, pp. 129–134. Faridabad, India (2019)

  4. Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., Flinck, H.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017)

    Article  Google Scholar 

  5. Dayarathna, M., Wen, Y.G., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)

    Article  Google Scholar 

  6. Ahmed, A., Ahmed, E.: A survey on mobile edge computing. In: Proceedings of the 10th International Conference on Intelligent System Control, pp. 1–8 (2016)

  7. Sait, S.M., Shahid, K.S.: Engineering simulated evolution for virtual machine assignment problem. Appl. Intell. 43(2), 296–307 (2015)

    Article  Google Scholar 

  8. Li, Z., Li, Y., Yuan, T., et al.: Chemical reaction optimization for virtual machine placement in cloud computing. Appl. Intell. 49, 220 (2019)

    Article  Google Scholar 

  9. Sait, S.M., Bala, A., El-Maleh, A.H.: Cuckoo search based resource optimization of datacenters. Appl. Intell. 44(3), 489–506 (2016)

    Article  Google Scholar 

  10. Qin, Y., Wang, H., Yi, S., et al.: Virtual machine placement based on multi-objective reinforcement learning. Appl. Intell. 50(8), 2370 (2020)

    Article  Google Scholar 

  11. Tziritas, N., et al.: Data replication and virtual machine migrations to mitigate network overhead in edge computing systems. IEEE Trans. Sustain. Comput. 2(4), 320–332 (2017). https://doi.org/10.1109/TSUSC.2017.2715662

    Article  Google Scholar 

  12. Yi, C., Cai, J., Su, Z.: A multi-user mobile computation offloading and transmission scheduling mechanism for delay-sensitive applications. IEEE Trans. Mob. Comput. 19(1), 29–43 (2020). https://doi.org/10.1109/TMC.2019.2891736

    Article  Google Scholar 

  13. Xu, Z., Liang, W., Xu, W., Jia, M., Guo, S.: Efficient algorithms for capacitated cloudlet placements. IEEE Trans. Parallel Distrib. Syst. 27(10), 2866–2880 (2016)

    Article  Google Scholar 

  14. Kiani, A., Ansari, N.: Toward hierarchical mobile edge computing: an auction-based profit maximization approach. IEEE Internet Things J. 4(6), 2082–2091 (2017)

    Article  Google Scholar 

  15. Rodrigues, T.G., Suto, K., Nishiyama, H., Kato, N., Temma, K.: Cloud-lets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Trans. Comput. 67(9), 1287–1300 (2018). https://doi.org/10.1109/TC.2018.2818144

    Article  MathSciNet  Google Scholar 

  16. Sun, X., Ansari, N.: Green cloudlet network: a sustainable platform for mobile cloud computing. IEEE Trans. Cloud Comput. 8(1), 180–192 (2020). https://doi.org/10.1109/TCC.2017.2764463

    Article  Google Scholar 

  17. Mondal, S., Das, G., Wong, E.: CCOMPASSION: A hybrid cloudlet placement framework over passive optical access networks. In: Proceedings of the IEEE INFOCOM IEEE Conference on Computing Communication, pp. 216–224 (Apr. 2018)

  18. Sun, G., Liao, D., Zhao, D., Xu, Z., Yu, H.: Live migration for multiple correlated virtual machines in cloud-based data centers. IEEE Trans. Services Comput. 11(2), 279–291 (2018). https://doi.org/10.1109/TSC.2015.2477825

    Article  Google Scholar 

  19. Jia, M., Cao, J., Liang, W.: Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans. Cloud Comput. 5(4), O725-737ct (2017)

    Article  Google Scholar 

  20. Hieu, N.T., Francesco, M.D., Ylä-Jääski, A.: Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Services Comput. 13(1), 186–199 (2020). https://doi.org/10.1109/TSC.2017.2648791

    Article  Google Scholar 

  21. Liu, X., Zhan, Z., Deng, J.D., Li, Y., Gu, T., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22(1), 113–128 (2018). https://doi.org/10.1109/TEVC.2016.2623803

    Article  Google Scholar 

  22. Li, Y., Wang, S.: An energy-aware edge server placement algorithm in mobile edge computing, In: Proceedings of the IEEE International Conference on Edge Computing (EDGE), pp. 66–73 (Jul. 2018)

  23. Zhao, L., Lu, L., Jin, Z., Yu, C.: Online virtual machine placement for increasing cloud provider’s revenue. IEEE Trans. Services Comput. 10, 273–2851 (2017). https://doi.org/10.1109/TSC.2015.2447550

    Article  Google Scholar 

  24. Hoang, D.T., Niyato, D., Wang, P.: Optimal admission control policy for mobile cloud computing hotspot with cloudlet. In: Proceedings of the IEEE WCNC, pp. 3145–3149 (Apr. 2012)

  25. Zhan, Z.-H., et al.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47(4), 1–33 (2015)

    Article  Google Scholar 

  26. Mi, H., et al.: Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers, In: Proceedings of the IEEE International Conference on Services Computing, pp. 514–521 (2010)

  27. Abualigah, L., Yousri, D., Elaziz, M.A., et al.: Matlab code of aquila optimizer: a novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157, 107250 (2021)

    Article  Google Scholar 

  28. Meng, X.: Feature selection and enhanced krill herd algorithm for text document clustering. Comput. Rev. 60(8), 318–318 (2019)

    Google Scholar 

  29. Abualigah, L., Diabat, A., Mirjalili, S., et al.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)

    Article  MathSciNet  Google Scholar 

  30. Abualigah, L., Diabat, A.: Advances in sine cosine algorithm: a comprehensive survey. Artif. Intell. Rev. 3, 1–42 (2021)

    Google Scholar 

  31. Degang, X.U., Ping, Z.H.A.O.: Literature survey on research and application of bat algorithm. CEA 55(15), 1–12 (2019)

    Google Scholar 

  32. Kongkaew, W.: Bat algorithm in discrete optimization: a review of recent applications. Songklanakarin J. Sci. Technol. (SJST) 39(5), 641–650 (2017)

    Google Scholar 

  33. Sonmez, C., Ozgovde, A., Ersoy, C.: EdgeCloudSim: an environment for performance evaluation of Edge Computing systems. In: Proceedings of the 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pp. 39–44. Valencia (2017). https://doi.org/10.1109/FMEC.2017.7946405

  34. Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2009)

    Article  Google Scholar 

  35. Fan, X.B., 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 

  36. Kaur, S., Bawa, S.: A review on energy aware VM placement and consolidation techniques. In: Proceedings of the International Conference on Inventive Computation Technologies. IEEE (2017)

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61672461 and 61672463.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Chengfeng Jian, Lukun Bao & Meiyu Zhang

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  1. Chengfeng Jian
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Correspondence to Chengfeng Jian.

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Jian, C., Bao, L. & Zhang, M. A high-efficiency learning model for virtual machine placement in mobile edge computing. Cluster Comput 25, 3051–3066 (2022). https://doi.org/10.1007/s10586-022-03550-1

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