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MRA-VC: multiple resources aware virtual machine consolidation using particle swarm optimization

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Abstract

The consolidation of virtual machines (VMs) in cloud data centres is an intriguing research subject. The fundamental issue with VM consolidation solutions is the trade-off between energy efficiency, Quality of Service (QoS) efficiency, and optimum Service Level Agreement Violations (SLAV). This study proposes Multiple Resources Aware VM Consolidation (MRA-VC), a unique approach for the dynamic VM consolidation framework in the cloud data centre. The MRA-VC is made up of two contributions: multi-resource-based overload and underload judgment algorithms and a VM placement technique based on Particle Swarm Optimization (PSO). For accurate decision-making on the overloaded and underloaded hosts, multi-resource overload detection algorithms (MR-ODA) and MR-based Underloaded Detection Algorithm (MR-UDA) are designed. The parameters such as CPU utilization, RAM, bandwidth, network traffic, and storage capabilities are computed for MR-ODA and MR-UDA. In MR-UDA, we categorize each underloaded host into either of three states: severe load (SL), moderate load (ML), or low load (LL) using the integrated resource utilization score. This process easily facilitates the subsequent processes of VM migration, VM selection, and placement. The PSO is used to modify the VM selection and placement process in the MRA-VC architecture. PSO-based VM selection and placement methods expand the MR-ODA and MR-UDA algorithms. Optimized VM selection and placement have a direct impact on QoS and energy consumption performance. As a result, after deciding when to migrate, we used the PSO-based optimum VM selection and placement technique. The MRA-VC framework was developed and assessed using the CloudSim tool in terms of energy efficiency and other QoS criteria in comparison to contemporary state-of-the-art approaches.

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Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Mahajan HB, Badarla A (2018) Application of internet of things for smart precision farming: solutions and challenges. Int J Adv Sci Technol 2018:37–45

    Google Scholar 

  2. Alhayani B, Kwekha-Rashid AS, Mahajan HB et al (2022) 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system. Appl Nanosci. https://doi.org/10.1007/s13204-021-02152-4

    Article  Google Scholar 

  3. Mahajan HB, Rashid AS, Junnarkar AA et al (2022) Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems. Appl Nanosci. https://doi.org/10.1007/s13204-021-02164-0

    Article  Google Scholar 

  4. Mahajan HB, Badarla A, Junnarkar AA (2021) CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming. J Ambient Intell Human Comput 12:7777–7791. https://doi.org/10.1007/s12652-020-02502-0

    Article  Google Scholar 

  5. Alhayani B, Abbas ST, Mohammed HJ, Mahajan HB (2021) Intelligent secured two-way image transmission using corvus corone module over WSN. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08484-2

    Article  Google Scholar 

  6. Amazon EC2. [Online]. Available: aws.amazon.com/ec2/

  7. Microsoft Windows Azure. [Online]. Available: https://azure.microsoft.com/

  8. Google Cloud. [Online]. Available: https://cloud.google.com/

  9. IBM Cloud Service. [Online]. Available: www-935.ibm.com/services/us/en/it-services/cloud-services/

  10. Gao Y, Guan H, Qi Z, Song T, Huan F, Liu L (2014) Service level agreement based energy-efficient resource management in cloud data centers. Comput Electr Eng 40(5):1621–1633. https://doi.org/10.1016/j.compeleceng.2013.11.001

    Article  Google Scholar 

  11. Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. PDPTA

  12. World Energy Outlook 2013 Fact Sheet. [Online]. Available: http://www.iea.org/media/files/WEO2013_factsheets.pdf

  13. Wang W, Chen H, Chen X (2012) An availability-aware virtual machine placement approach for dynamic scaling of cloud applications. 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing. https://doi.org/10.1109/uic-atc.2012.3

  14. Bermejo B, Juiz C, Guerrero C (2019) Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J Supercomput 75:808–836. https://doi.org/10.1007/s11227-018-2613-1

    Article  Google Scholar 

  15. Prabha B, Ramesh K, Renjith PN (2021) A review on dynamic virtual machine consolidation approaches for energy-efficient cloud data centers. In: Jeena JI, Kolandapalayam SS, Piramuthu S, Falkowski-Gilski P (eds) Data intelligence and cognitive informatics. Algorithms for intelligent systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-8530-2_60

    Chapter  Google Scholar 

  16. Zolfaghari R, Rahmani AM (2020) Virtual machine consolidation in cloud computing systems: challenges and future trends. Wireless Pers Commun 115:2289–2326. https://doi.org/10.1007/s11277-020-07682-8

    Article  Google Scholar 

  17. Liu Y, Sun X, Wei W, Jing W (2018) Enhancing energy-efficient and QoS dynamic virtual machine consolidation method in cloud environment. IEEE Access 6:31224–31235. https://doi.org/10.1109/access.2018.2835670

    Article  Google Scholar 

  18. Li L, Dong J, Zuo D, Wu J (2019) SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. IEEE Access. https://doi.org/10.1109/access.2019.2891567

    Article  Google Scholar 

  19. Rasouli N, Razavi R, Faragardi HR (2020) EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers. Clust Comput 23(4):3013–3027. https://doi.org/10.1007/s10586-020-03066-6

    Article  Google Scholar 

  20. Saadi Y, El Kafhali S (2020) Energy-efficient strategy for virtual machine consolidation in cloud environment. Soft Comput 24:14845–14859. https://doi.org/10.1007/s00500-020-04839-2

    Article  Google Scholar 

  21. Thiam C, Thiam F (2019) Energy efficient cloud data center using dynamic virtual machine consolidation algorithm. In: Abramowicz W, Corchuelo R (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_40

    Chapter  Google Scholar 

  22. Zhou Q, Xu M, Singh Gill S, Gao C, Tian W, Xu C, Buyya R (2020) Energy efficient algorithms based on VM consolidation for cloud computing: comparisons and evaluations. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). https://doi.org/10.1109/ccgrid49817.2020.00-44

  23. Ibrahim M, Imran M, Jamil F, Lee Y-J, Kim D-H (2021) EAMA: efficient adaptive migration algorithm for cloud data centers (CDCs). Symmetry 13(4):690. https://doi.org/10.3390/sym13040690

    Article  Google Scholar 

  24. Xiao H, Hu Z, Li K (2019) Multi-objective VM consolidation based on thresholds and ant colony system in cloud computing. IEEE Access. https://doi.org/10.1109/access.2019.2912722

    Article  Google Scholar 

  25. Li Z, Yu X, Zhao L (2019) A strategy game system for QoS-efficient dynamic virtual machine consolidation in data centers. IEEE Access 7:104315–104329. https://doi.org/10.1109/access.2019.2931617

    Article  Google Scholar 

  26. Song T, Wang Y, Li G, Pang S (2019) Server consolidation energy-saving algorithm based on resource reservation and resource allocation strategy. IEEE Access 7:171452–171460. https://doi.org/10.1109/access.2019.2954903

    Article  Google Scholar 

  27. Alqudah MA, Ahmed I, Ahmad F, Naseem S, Nisar KS (2021) Energy reduction through memory aware real-time scheduling on virtual machine in multi-cores server. IEEE Access 9:55436–55447. https://doi.org/10.1109/access.2021.3070868

    Article  Google Scholar 

  28. Liu Y, Zhao Y, Dong J, Li L, Wang C, Zuo D (2022) I-Neat: an intelligent framework for adaptive virtual machine consolidation. Tsinghua Sci Technol. 27:13–26. https://doi.org/10.26599/TST.2020.9010033

    Article  Google Scholar 

  29. He K, Li Z, Deng D, Chen Y (2017) Energy-efficient framework for virtual machine consolidation in cloud data centers. China Commun 14(10):192–201. https://doi.org/10.1109/cc.2017.8107643

    Article  Google Scholar 

  30. Sayadnavard MH, Toroghi Haghighat A, Rahmani AM (2021) A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers. Eng Sci Technol Int J. https://doi.org/10.1016/j.jestch.2021.04.014

    Article  Google Scholar 

  31. Elsedimy E, Algarni F (2021) Toward enhancing the energy efficiency and minimizing the SLA violations in cloud data centers. Appl Computat Intel Soft Comput 2021:1–14. https://doi.org/10.1155/2021/8892734

    Article  Google Scholar 

  32. Ibrahim A, Noshy M, Ali HA, Badawy M (2020) PAPSO: a power-aware VM placement technique based on particle swarm optimization. IEEE Access 8:81747–81764

    Article  Google Scholar 

  33. Hariharan B, Siva R, Kaliraj S, Prakash PNS (2021) ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03429-w

    Article  Google Scholar 

  34. Mejahed S, Elshrkawey M (2022) A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization. PeerJ Comput Sci 8:e834. https://doi.org/10.7717/peerj-cs.834

    Article  Google Scholar 

  35. Malik N, Sardaraz M, Tahir M, Shah B, Ali G, Moreira F (2021) Energy-efficient load balancing algorithm for workflow scheduling in cloud data centers using queuing and thresholds. Appl Sci 11(13):5849. https://doi.org/10.3390/app11135849

    Article  Google Scholar 

  36. PlantLab (2019) [Online]. Available: https://www.planet-lab.org/. [Accessed: 26-May-2019]

  37. Chowdhury MR, Mahmud MR, Rahman RM (2015) Implementation and performance analysis of various VM placement strategies in CloudSim. J Cloud Comput. https://doi.org/10.1186/s13677-015-0045-5

    Article  Google Scholar 

  38. Beloglazov A, Buyya R (2011) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurrency Computat Pract Exp 24(13):1397–1420. https://doi.org/10.1002/cpe.1867

    Article  Google Scholar 

  39. Songara N, Jain MK (2020) Design of QoS and energy efficient VM consolidation framework for cloud data centers. In: Gunjan V, Senatore S, Kumar A, Gao XZ, Merugu S (eds) Advances in cybernetics, cognition, and machine learning for communication technologies. Lecture notes in electrical engineering, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-15-3125-5_19

    Chapter  Google Scholar 

  40. Usha Kirana SP, D’Mello DA (2021) Energy-efficient enhanced Particle Swarm Optimization for virtual machine consolidation in cloud environment. Int J Inf Tecnol 13:2153–2161. https://doi.org/10.1007/s41870-021-00745-4

    Article  Google Scholar 

  41. Kumar M, Yadav AK, Khatri P et al (2018) Global host allocation policy for virtual machine in cloud computing. Int J Inf Tecnol 10:279–287. https://doi.org/10.1007/s41870-018-0093-4

    Article  Google Scholar 

  42. Ramegowda A, Agarkhed J, Patil SR (2020) Adaptive task scheduling method in multi-tenant cloud computing. Int J Inf Tecnol 12:1093–1102. https://doi.org/10.1007/s41870-019-00389-5

    Article  Google Scholar 

  43. Shokoohsaljooghi A, Mirvaziri H (2020) Performance improvement of intrusion detection system using neural networks and particle swarm optimization algorithms. Int J Inf Tecnol 12:849–860. https://doi.org/10.1007/s41870-019-00315-9

    Article  Google Scholar 

  44. Patil S, Anandhi RJ (2020) Diversity based self-adaptive clusters using PSO clustering for crime data. Int J Inf Tecnol 12:319–327. https://doi.org/10.1007/s41870-019-00311-z

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Mohanlal Sukhadia University, Udaipur, Rajasthan, 313001, India

    Neha Songara & Manoj Kumar Jain

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  1. Neha Songara
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Correspondence to Neha Songara.

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Songara, N., Jain, M.K. MRA-VC: multiple resources aware virtual machine consolidation using particle swarm optimization. Int. j. inf. tecnol. 15, 697–710 (2023). https://doi.org/10.1007/s41870-022-01102-9

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