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Nature-Inspired Hybrid Virtual Machine Placement Approach in Cloud

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Advances in Data-Driven Computing and Intelligent Systems (ADCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 698))

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Abstract

Many nature-inspired, swarm intelligent, and hybrid algorithms have been designed to figure out an optimal virtual machine (VM) placement solution. In this paper, a hybridized intelligent water drop cycle algorithm (IWCDA) has been proposed that aims to achieve better resource utilization and minimum energy consumption in cloud datacenters. The algorithm works in two phases. First, intelligent water drop (IWD) is adapted to construct an efficient VM placement solution by using its unique heuristic function. In the second phase, the water cycle algorithm (WCA) is applied to bring out the best solution for the VM placement problem. At the end, the solutions of both phases are compared and the best one is taken as an optimal VM placement solution after undergoing several iterations. Energy and resource aware schemes are involved in selecting suitable physical machines (PM) for respective VM in both phases, respectively. The proposed work has been implemented on MATLAB, and simulation results have shown that IWDCA performed better as compared to IWD and WCA by 4% and 7%, respectively, in terms of energy consumption, number of active servers, and resource utilization rate.

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References

  1. Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problem. Article Soft Comput 1–25

    Google Scholar 

  2. Kumar M (2021) Suman: meta-heuristics techniques in cloud computing: applications and challenges. Indian J Comput Sci Eng (IJCSE) 12:1–10

    Google Scholar 

  3. Masdari M, Gharehpasha S, Ghobaei-Arani M, Ghasemi V (2019) Bio-inspired virtual machine placement schemes in cloud computing environment taxonomy; review, and future research direction. Cluster Comput 1–11

    Google Scholar 

  4. Braiki K, Youssef H (2020) Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. J Supercomput 76:427–454

    Article  Google Scholar 

  5. 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 8:1–27

    Google Scholar 

  6. Liu X-F, Zhan Z-H, Deng JD, Li Y, Gu T, Zhang J (2018) An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans Evol Comput 22(1):113–128

    Google Scholar 

  7. Kumar J, Singh A, Mohan A (2021) Resource-efficient load-balancing framework for cloud data center networks. ETRI J 43:53–63

    Article  Google Scholar 

  8. Barlaskar E, Singh YJ, Isaac B (2016) Energy efficient VM placement using firefly algorithm. Multi Agent Grid Syst-Int J 12:167–198

    Article  Google Scholar 

  9. Soltanshahi M, Asemi R, Shafiei N (2019) Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers. Heliyon 5:1–8

    Article  Google Scholar 

  10. Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2019) An improved Lévy based whale optimization algorithm for bandwidth efficient virtual machine placement in cloud computing environment. Clust Comput 22:8319–8334

    Article  Google Scholar 

  11. Madhumala RB, Tiwari H, Verma CD (2021) Hybrid model for virtual machine optimization in cloud data center. In: 2021 5th International conference on intelligent computing and control systems (ICICCS), pp 137–141

    Google Scholar 

  12. Singhrova A, Anu A (2021) Prioritized GA-PSO algorithm for efficient resource allocation in fog computing. Indian J Comput Sci Eng 11:907–916

    Google Scholar 

  13. Kumar D, Mandal T (2017) Bi-objective virtual machine placement using hybrid of genetic algorithm and particle swarm optimization in cloud data center. Int J Appl Eng Res 12:12044–12051

    Google Scholar 

  14. Alharbe N, Aljohani A, Rakrouki M (2022) A fuzzy grou** genetic algorithm for solving a real-world virtual machine placement problem in a healthcare-cloud. Algorithms 1–17

    Google Scholar 

  15. Alhammadi ASA, Vasanthi V (2021) Multi-objective algorithms for virtual machine selection and placement in cloud data center. In: Proceedings of the 2021 international congress of advanced technology and engineering, ICOTEN, pp 1–7

    Google Scholar 

  16. Alresheedi SS, Lu S, Abd Elaziz M, Ewees AA (2019) Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing. Hum-centric Comput Inf Sci 9:1–24

    Article  Google Scholar 

  17. Sivaraman E, Daniel D, Jayapandian N, Prasad BH (2020) Augmented intelligent water drops optimization model for virtual machine placement in cloud environment. IET Networks 9:215–222

    Article  Google Scholar 

  18. Wei S, Yan W, Shiyong L (2020) Optimal resource allocation scheme for virtual machine placement of deploying enterprise applications into cloud. AIMS Mathematics 5:3966–3989

    Article  MathSciNet  MATH  Google Scholar 

  19. Azizi S, Zandsalimi M, Li D (2020) An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Clust Comput 23:3421–3434

    Article  Google Scholar 

  20. Dubey K, Sharma S (2022) An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework. J King Saud Univ Comput Inf Sci 34:3948–3958

    Google Scholar 

  21. Usman MJ, Ismail AS, Chizari H (2019) Energy-efficient virtual machine allocation technique using flower pollination algorithm in cloud datacenter: a panacea to green computing. J Bionic Eng 16:354–366

    Article  Google Scholar 

  22. Salami HO, Bala A, Sait SM, Ismail I (2021) An energy-efficient cuckoo search algorithm for virtual machine placement in cloud computing data centers. J Supercomput 77:13330–13357

    Article  Google Scholar 

  23. Reddy M, Kongara R (2019) Virtual machine placement using JAYA optimization algorithm. Appl Artif Intell 34:1–16

    Google Scholar 

  24. Fatima A, Javaid N, Anjum Butt A, Sultana T, Hussain W, Bilal M, Hashmi MA, Akbar M, Ilahi M (2019) An enhanced multi-objective gray wolf optimization for virtual machine placement in cloud data centers. Electronics 1–27

    Google Scholar 

  25. Dubey K, Nasr AA, Sharma SC, El-Bahnasawy N, Attiya G, El-Sayed A (2020) Efficient VM placement policy for data centre in cloud environment. Soft Comput Theor Appl Springer 301–309

    Google Scholar 

  26. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algoritha novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166

    Article  Google Scholar 

  27. The Grid Workload Archive. http://gwa.ewi.tudelft.nl/datasets/gwa-t-12bitbrains. Last accessed 07 May 2022

  28. Kesavaraja D, Shenbagavalli A (2018) QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization. J Parallel Distrib Comput 118:267–279

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Chayan Bhatt & Sunita Singhal

Authors
  1. Chayan Bhatt
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  2. Sunita Singhal
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Corresponding author

Correspondence to Chayan Bhatt .

Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

  2. Birla Institute of Technology and Science, Goa, India

    Snehanshu Saha

  3. Department of Computer Science, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City, Mexico

    Carlos A. Coello Coello

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Bhatt, C., Singhal, S. (2023). Nature-Inspired Hybrid Virtual Machine Placement Approach in Cloud. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2022. Lecture Notes in Networks and Systems, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-99-3250-4_21

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  • DOI: https://doi.org/10.1007/978-981-99-3250-4_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3249-8

  • Online ISBN: 978-981-99-3250-4

  • eBook Packages: EngineeringEngineering (R0)

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