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An energy-efficient black widow-based adaptive VM placement approach for cloud computing

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Abstract

The demand for cloud-based computation is increasing exponentially in the age of information technology virtualization, which is increasing data centre energy consumption and service level agreement (SLA) violations. This contributes to global warming and excessive load on existing infrastructure. As a result, it is critical to improve the resource utilisation of cloud data centres (CDCs). Virtual machine (VM) consolidation is the most effective method for optimising resource utilisation in CDCs. In this context, this research proposes the global optimal search-based meta-heuristic algorithm black widow adaptive VM placement (BWAVP) approach-based VM consolidation that combines energy conservation, resource utilisation and required quality of services with accurate VM-PM mapping. The investigation of the BWAVP approach shows that it reduces energy consumption by 18% on average when compared to other VM placement approaches, and reduces SLA violations, and VM migrations by more than 80%. Further, The research proposed a localized adaptive over and underutilisation host detection technique that further reduces energy consumption by 10% while maintaining the quality of services (QoSs) at the same level.

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References

  1. Liu, H., Aljbri, A., Song, J., Jiang, J., Hua, C.: Research advances on ai-powered thermal management for data centers. Tsinghua Sci. Technol. 27(2), 303–314 (2021). https://doi.org/10.26599/TST.2021.9010019

  2. Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012). https://doi.org/10.1016/j.future.2011.04.017

    Article  Google Scholar 

  3. Verma, A., Ahuja, P., Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pp. 243–264. Springer, New York (2008). https://doi.org/10.1007/978-3-540-89856-6_13

  4. Shirvani, M.H., Babaeikiadehi, S.: A hybrid meta-heuristic-based linear regression algorithm for live virtual machine migration in cloud datacenters. In: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–5 (2022). https://doi.org/10.1109/ICECET55527.2022.9872935 . IEEE

  5. 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. Model. 58(5–6), 1222–1235 (2013). https://doi.org/10.1016/j.mcm.2013.02.003

    Article  MathSciNet  Google Scholar 

  6. Gupta, M.K., Amgoth, T.: Resource-aware virtual machine placement algorithm for iaas cloud. J. Supercomput. 74(1), 122–140 (2018). https://doi.org/10.1007/s11227-017-2112-9

    Article  Google Scholar 

  7. Li, Z., Guo, S., Yu, L., Chang, V.: Evidence-efficient affinity propagation scheme for virtual machine placement in data center. IEEE Access 8, 158356–158368 (2020). https://doi.org/10.1109/ACCESS.2020.3020043

    Article  Google Scholar 

  8. Mapetu, J.P.B., Kong, L., Chen, Z.: A dynamic vm consolidation approach based on load balancing using Pearson correlation in cloud computing. J. Supercomput. (2020). https://doi.org/10.1007/s11227-020-03494-6

    Article  Google Scholar 

  9. Paulraj, G.J.L., Francis, S.A.J., Peter, J.D., Jebadurai, I.J.: A combined forecast-based virtual machine migration in cloud data centers. Comput. Electr. Eng. 69, 287–300 (2018). https://doi.org/10.1016/j.compeleceng.2018.01.012

    Article  Google Scholar 

  10. Wei, C., Hu, Z.H., Wang, Y.G.: Exact algorithms for energy-efficient virtual machine placement in data centers. Futur. Gener. Comput. Syst. 106, 77–91 (2020). https://doi.org/10.1016/j.future.2019.12.043

    Article  Google Scholar 

  11. Zhou, Z., Hu, Z., Li, K.: Virtual machine placement algorithm for both energy-awareness and sla violation reduction in cloud data centers. Sci. Program. 2016, 5612039 (2016). https://doi.org/10.1155/2016/5612039

    Article  Google Scholar 

  12. Arianyan, E., Taheri, H., Sharifian, S.: Novel energy and sla efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Comput. Electr. Eng. 47, 222–240 (2015). https://doi.org/10.1016/j.compeleceng.2015.05.006

    Article  Google Scholar 

  13. Shaw, S.B., Singh, A.K.: Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center. Comput. Electr. Eng. 47, 241–254 (2015). https://doi.org/10.1016/j.compeleceng.2015.07.020

    Article  Google Scholar 

  14. Yadav, R., Zhang, W., Li, K., Liu, C., Laghari, A.A.: Managing overloaded hosts for energy-efficiency in cloud data centers. Clust. Comput. (2021). https://doi.org/10.1007/s10586-020-03182-3

    Article  Google Scholar 

  15. Moghaddam, M.J.: Minimizing virtual machine migration probability in cloud computing environments. Clust. Comput. (2020). https://doi.org/10.1007/s10586-020-03067-5

    Article  Google Scholar 

  16. Ibrahim, M., Nabi, S., Baz, A., Naveed, N., Alhakami, H.: Towards a task and resource aware task scheduling in cloud computing: an experimental comparative evaluation. Int. J. Netw. Distrib. Comput. 8(3), 131–138 (2020). https://doi.org/10.2991/ijndc.k.200515.003

    Article  Google Scholar 

  17. Gholipour, N., Arianyan, E., Buyya, R.: A novel energy-aware resource management technique using joint vm and container consolidation approach for green computing in cloud data centers. Simul. Model. Pract. Theory 104, 102127 (2020). https://doi.org/10.1016/j.simpat.2020.102127

    Article  Google Scholar 

  18. Garg, V., Jindal, B.: Resource optimization using predictive virtual machine consolidation approach in cloud environment. Intell. Decis. Technol. (2023). https://doi.org/10.3233/IDT-220222

    Article  Google Scholar 

  19. Hsieh, S.Y., Liu, C.S., Buyya, R., Zomaya, A.Y.: Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers. J. Parallel Distrib. Comput. 139, 99–109 (2020). https://doi.org/10.1016/j.jpdc.2019.12.014

    Article  Google Scholar 

  20. Wang, J.V., Ganganath, N., Cheng, C.T., Chi, K.T.: Bio-inspired heuristics for vm consolidation in cloud data centers. IEEE Syst. J. 14(1), 152–163 (2019). https://doi.org/10.1109/JSYST.2019.2900671

    Article  Google Scholar 

  21. 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). https://doi.org/10.1109/ACCESS.2020.2990828

    Article  Google Scholar 

  22. Farzai, S., Shirvani, M.H., Rabbani, M.: Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters. Sustain. Comput. 28, 100374 (2020). https://doi.org/10.1016/j.suscom.2020.100374

    Article  Google Scholar 

  23. Junaid, M., Sohail, A., Ahmed, A., Baz, A., Khan, I.A., Alhakami, H.: A hybrid model for load balancing in cloud using file type formatting. IEEE Access 8, 118135–118155 (2020). https://doi.org/10.1109/ACCESS.2020.3003825

    Article  Google Scholar 

  24. Shirvani, M.H.: An energy-efficient topology-aware virtual machine placement in cloud datacenters: a multi-objective discrete jaya optimization. Sustain. Comput. 38, 100856 (2023). https://doi.org/10.1016/j.suscom.2023.100856

    Article  Google Scholar 

  25. Caviglione, L., Gaggero, M., Paolucci, M., Ronco, R.: Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters. Soft Comput. 25(19), 12569–12588 (2021). https://doi.org/10.1007/s00500-020-05462-x

    Article  Google Scholar 

  26. Alboaneen, D., Tianfield, H., Zhang, Y., Pranggono, B.: A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers. Future Gener. Comput. Syst. 115, 201–212 (2021). https://doi.org/10.1016/j.future.2020.08.036

    Article  Google Scholar 

  27. Saeedi, P., Hosseini Shirvani, M.: An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters. Soft Comput. 25, 5233–5260 (2021). https://doi.org/10.1007/s00500-020-05523-1

    Article  Google Scholar 

  28. Byatarayanapura, S., Indrajit, V., Seetharam, M.: Chicwhale optimization algorithm for the vm migration in cloud computing platform. Evol. Intell. (2020). https://doi.org/10.1007/s12065-020-00386-9

    Article  Google Scholar 

  29. Naik, B.B., Singh, D., Samaddar, A.B.: Fhcs: hybridised optimisation for virtual machine migration and task scheduling in cloud data center. IET Commun. 14(12), 1942–1948 (2020). https://doi.org/10.1049/iet-com.2019.1149

    Article  Google Scholar 

  30. Zolfaghari, R., Sahafi, A., Rahmani, A.M., Rezaei, R.: An energy-aware virtual machines consolidation method for cloud computing: simulation and verification. Softw. Pract. Exp. 52(1), 194–235 (2022)

    Article  Google Scholar 

  31. Abdullah, M., Lu, K., Wieder, P., Yahyapour, R.: A heuristic-based approach for dynamic vms consolidation in cloud data centers. Arab. J. Sci. Eng. 42, 3535–3549 (2017). https://doi.org/10.1007/s13369-017-2580-5

    Article  Google Scholar 

  32. Khan, M.S.A., Santhosh, R.: Hybrid optimization algorithm for vm migration in cloud computing. Comput. Electr. Eng. 102, 108152 (2022). https://doi.org/10.1016/j.compeleceng.2022.108152

    Article  Google Scholar 

  33. Xiao, H., Hu, Z., Li, K.: Multi-objective vm consolidation based on thresholds and ant colony system in cloud computing. IEEE Access 7, 53441–53453 (2019). https://doi.org/10.1109/ACCESS.2019.2912722

    Article  Google Scholar 

  34. Khan, A.A., Zakarya, M., Buyya, R., Khan, R., Khan, M., Rana, O.: An energy and performance aware consolidation technique for containerized datacenters. IEEE Trans. Cloud Comput. 9(4), 1305–1322 (2019). https://doi.org/10.1109/TCC.2019.2920914

    Article  Google Scholar 

  35. Abuhamdah, A.: An adaptive black widow optimisation algorithm for data clustering. Int. J. Math. Oper. Res. 20(2), 239–263 (2021). https://doi.org/10.1504/IJMOR.2021.118740

    Article  MathSciNet  MATH  Google Scholar 

  36. Nachmani, O.: Consumption, utilization and elasticity: Cloud basics-iod-the content engineers. IOD-The Content Engineers (2012)

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  1. Sahul Goyal and Lalit Kumar Awasthi have contributed equally to this work.

Authors and Affiliations

  1. Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Sahul Goyal

  2. National Institute of Technology, Pauri, Uttarakhand, India

    Lalit Kumar Awasthi

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  1. Sahul Goyal
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Goyal, S., Awasthi, L.K. An energy-efficient black widow-based adaptive VM placement approach for cloud computing. Cluster Comput (2023). https://doi.org/10.1007/s10586-023-04204-6

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