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Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm

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Abstract

Cloud computing is a computing paradigm, where a large pool of systems is connected in private or public networks to provide dynamically scalable infrastructure for application, data, and file storage. With the advent of this technology, the cost of power computation, application hosting, content storage, resource wastage, and delivery is reduced significantly. Cloud computing provides the possibility of merely concentrating on business goals instead of expanding hardware resources for users. Challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines over physical ones in cloud data centers can lead to the management of resources and prevention of the resources waste. Hereby, a new approach is proposed based on the combination of the hybrid discrete multi-object whale optimization algorithm, multi-verse optimizer with chaotic functions for optimal placement in the cloud data center. The first object of the proposed algorithm is to decrease power consumption, which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut the resource wastage and managing resources using the optimal placement of virtual machines over physical machines in cloud data centers. With this method, the increasing rate of virtual migration to physical machines is prevented. Finally, the results obtained from the proposed algorithm were compared to some algorithms such as first fit, VMPACS, MBFD.

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References

  • 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(4):8319–8334

    Google Scholar 

  • Alashaikh AS, Alanazi EA (2019) Incorporating ceteris paribus preferences in multiobjective virtual machine placement. IEEE Access 7:59984–59998

    Google Scholar 

  • Alharbi F, Tian Y-C, Tang M, Zhang W-Z, Peng C, Fei M (2019) An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Syst Appl 120:228–238

    Google Scholar 

  • Al-Moalmi A, Luo J, Salah A, Li K (2019) Optimal virtual machine placement based on grey wolf optimization. Electronics 8(3):283

    Google Scholar 

  • Asemi R, Doostsadigh E, Ahmadi M, Malazi HT (2015) Energy efficiency in virtual machines allocation for cloud data centers using the imperialist competitive algorithm. In: 2015 IEEE 5th international conference on big data and cloud computing. IEEE, New York, pp 62–67

  • Baalamurugan K, Bhanu SV (2018) A multi-objective krill herd algorithm for virtual machine placement in cloud computing. J Supercomput 2018:1–18

    Google Scholar 

  • Bao R (2016) Performance evaluation for traditional virtual machine placement algorithms in the cloud. In: International conference on internet of vehicles. Springer, Berlin, pp 225–231

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

    Google Scholar 

  • Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut Gener Comput Syst 28(5):755–768

    Google Scholar 

  • Biran O, Corradi A, Fanelli M, Foschini L, Nus A, Raz D, Silvera E (2012) A stable network-aware VM placement for cloud systems. In: 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (ccgrid 2012). IEEE, New York, pp 498–506

  • Calcavecchia NM, Biran O, Hadad E, Moatti Y (2012) VM placement strategies for cloud scenarios. In: 2012 IEEE 5th international conference on cloud computing. IEEE, New York, pp 852–859

  • Chen J, Liu W, Song J (2012a) Network performance aware virtual machine migration in data centers. Cloud Comput 2012:65–71

    Google Scholar 

  • Chen W, Qiao X, Wei J, Huang T (2012b) A profit-aware virtual machine deployment optimization framework for cloud platform providers. In: 2012 IEEE 5th international conference on cloud computing. IEEE, New York, pp 17–24

  • Chen K-Y, Xu Y, Xi K, Chao HJ (2013) Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems. In: 2013 IEEE international conference on communications (ICC). IEEE, New York, pp 3498–3503

  • Dai X, Wang JM, Bensaou B (2014) Energy-efficient virtual machine placement in data centers with heterogeneous requirements. In: 2014 IEEE 3rd international conference on cloud networking (CloudNet). IEEE, New York, pp 161–166

  • Dong Y-S, Xu G-C, Fu X-D (2014) A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform. Sci World J 2014:259139

    Google Scholar 

  • Fatima A, Javaid N, Anjum Butt A, Sultana T, Hussain W, Bilal M, Akbar M, Ilahi M (2019) An enhanced multi-objective gray wolf optimization for virtual machine placement in cloud data centers. Electronics 8(2):218

    Google Scholar 

  • Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2015) Network-aware virtual machine placement and migration in cloud data centers. In: Emerging research in cloud distributed computing systems. IGI Global, New York, pp 42–91

  • Gahlawat M, Sharma P (2014) Survey of virtual machine placement in federated clouds. In: 2014 IEEE international advance computing conference (IACC). IEEE, New York, pp 735–738

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

    MathSciNet  MATH  Google Scholar 

  • Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24

    Google Scholar 

  • Gharehchopogh FS, Shayanfar H, Gholizadeh H (2019) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev 2019:1–48

    Google Scholar 

  • Guerrero C, Lera I, Juiz C (2018) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput 16(1):113–135

    Google Scholar 

  • Gupta MK, Amgoth T (2018) Resource-aware virtual machine placement algorithm for IAAS cloud. J Supercomput 74(1):122–140

    Google Scholar 

  • Gupta MK, Jain A, Amgoth T (2018) Power and resource-aware virtual machine placement for IAAS cloud. Sustain Comput Inf Syst 19:52–60

    Google Scholar 

  • Hassen FB, Brahmi Z, Toumi H (2016) VM placement algorithm based on recruitment process within ant colonies. In: 2016 international conference on digital economy (ICDEc). IEEE, New York, pp 1–7

  • Huang W, Li X, Qian Z (2013) An energy efficient virtual machine placement algorithm with balanced resource utilization. In: 2013 7th international conference on innovative mobile and internet services in ubiquitous computing. IEEE, New York, pp 313–319

  • Jeyarani R, Nagaveni N, Ram RV (2012) Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence. Fut Gener Comput Syst 28(5):811–821

    Google Scholar 

  • Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Des Eng 5(3):275–284

    Google Scholar 

  • Kessaci Y, Melab N, Talbi E-G (2013) A Pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment. In: 2013 IEEE congress on evolutionary computation. IEEE, New York, pp 2496–2503

  • Komu M, Sethi M, Mallavarapu R, Oirola H, Khan R, Tarkoma S (2012) Secure networking for virtual machines in the cloud. In: 2012 IEEE international conference on cluster computing workshops. IEEE, New York, pp 88–96

  • Kovács J, Kacsuk P (2018) Occopus: a multi-cloud orchestrator to deploy and manage complex scientific infrastructures. J Grid Comput 16(1):19–37

    Google Scholar 

  • Li X, Qian Z, Chi R, Zhang B, Lu S (2012) Balancing resource utilization for continuous virtual machine requests in clouds. In: 2012 6th international conference on innovative mobile and internet services in ubiquitous computing. IEEE, New York, pp 266–273

  • Li Z, Li Y, Yuan T, Chen S, Jiang S (2019) Chemical reaction optimization for virtual machine placement in cloud computing. Appl Intell 49(1):220–232

    Google Scholar 

  • Liu X-F, Zhan Z-H, Du K-J, Chen W-N (2014) Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation. ACM, Berlin, pp 41–48

  • Liu X-F, Zhan Z-H, Deng JD, Li Y, Gu T, Zhang J (2016) An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans Evol Comput 22:113–128

    Google Scholar 

  • López-Pires F, Barán B, Benítez L, Zalimben S, Amarilla A (2018) Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty. Fut Gener Comput Syst 79:830–848

    Google Scholar 

  • Masdari M, Jalali M (2016) A survey and taxonomy of dos attacks in cloud computing. Secur Commun Netw 9(16):3724–3751

    Google Scholar 

  • Masdari M, Khezri H (2020) Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review. Clust Comput 2020:1–30

    Google Scholar 

  • Masdari M, Khoshnevis A (2019) A survey and classification of the workload forecasting methods in cloud computing. Clust Comput 2019:1–26

    Google Scholar 

  • Masdari M, Zangakani M (2019) Green cloud computing using proactive virtual machine placement: challenges and issues. J Grid Comput 2019:1–33

    Google Scholar 

  • Masdari M, Nabavi SS, Ahmadi V (2016) An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 66:106–127

    Google Scholar 

  • Masdari M, Salehi F, Jalali M, Bidaki M (2017) A survey of pso-based scheduling algorithms in cloud computing. J Netw Syst Manag 25(1):122–158

    Google Scholar 

  • Masdari M, Barshande S, Ozdemir S (2019a) Cdabc: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNS. J Supercomput 75(11):7174–7208

    Google Scholar 

  • Masdari M, Gharehpasha S, Ghobaei-Arani M, Ghasemi V (2019b) Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Clust Comput 2019:1–31

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Google Scholar 

  • Ortigoza J, López-Pires F, Barán B (2016) A taxonomy on dynamic environments for provider-oriented virtual machine placement. In: 2016 IEEE international conference on cloud engineering (IC2E). IEEE, New York, pp 214–215

  • Pires FL, Barán B (2013) Multi-objective virtual machine placement with service level agreement: a memetic algorithm approach. In: Proceedings of the 2013 IEEE/ACM 6th international conference on utility and cloud computing. IEEE Computer Society, New York, pp 203–210

  • Qi H, Shiraz M, Liu J-Y, Gani A, Rahman ZA, Altameem TA (2014) Data center network architecture in cloud computing: review, taxonomy, and open research issues. J Zhejiang Univ Sci C 15(9):776–793

    Google Scholar 

  • Sarker TK, Tang M (2015) A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers. In: International conference on neural information processing. Springer, Berlin, pp 161–169

  • Seddigh M, Taheri H, Sharifian S (2015) Dynamic prediction scheduling for virtual machine placement via ant colony optimization. In: 2015 signal processing and intelligent systems conference (SPIS). IEEE, New York, pp 104–108

  • Shabeera T, Kumar SM, Salam SM, Krishnan KM (2017) Optimizing vm allocation and data placement for data-intensive applications in cloud using aco metaheuristic algorithm. Eng Sci Technol Int J 20(2):616–628

    Google Scholar 

  • Sharma O, Saini H (2019) Energy and SLA efficient virtual machine placement in cloud environment using non-dominated sorting genetic algorithm. Int J Inf Secur Privacy (IJISP) 13(1):1–16

    Google Scholar 

  • Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746

    Google Scholar 

  • Shigeta S, Yamashima H, Doi T, Kawai T, Fukui K (2012) Design and implementation of a multi-objective optimization mechanism for virtual machine placement in cloud computing data center. In: International conference on cloud computing. Springer, Berlin, pp 21–31

  • Singh V, Gupta I, Jana PK (2019) An energy efficient algorithm for workflow scheduling in IAAS cloud. J Grid Comput 2019:1–20

    Google Scholar 

  • Srinivasan A, Quadir MA, Vijayakumar V (2015) Era of cloud computing: a new insight to hybrid cloud. Proc Comput Sci 50:42–51

    Google Scholar 

  • Sun G, Liao D, Anand V, Zhao D, Yu H (2016) A new technique for efficient live migration of multiple virtual machines. Fut Gener Comput Syst 55:74–86

    Google Scholar 

  • Svärd P, Hudzia B, Walsh S, Tordsson J, Elmroth E (2015) Principles and performance characteristics of algorithms for live vm migration. ACM SIGOPS Oper Syst Rev 49(1):142–155

    Google Scholar 

  • Tawfeek MA, El-Sisi AB, Keshk AE, Torkey FA (2014) Virtual machine placement based on ant colony optimization for minimizing resource wastage. In: International conference on advanced machine learning technologies and applications. Springer, Berlin, pp 153–164

  • Vu HT, Hwang S (2014) A traffic and power-aware algorithm for virtual machine placement in cloud data center. Int J Grid Distrib Comput 7(1):350–355

    Google Scholar 

  • Wang B, Qi Z, Ma R, Guan H, Vasilakos AV (2015) A survey on data center networking for cloud computing. Comput Netw 91:528–547

    Google Scholar 

  • Wei W, Gu H, Lu W, Zhou T, Liu X (2019) Energy efficient virtual machine placement with an improved ant colony optimization over data center networks. IEEE Access 7:60617–60625

    Google Scholar 

  • Wu Y, Tang M, Fraser W (2012) A simulated annealing algorithm for energy efficient virtual machine placement. In: 2012 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, Berlin, pp 1245–1250

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Authors and Affiliations

  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Sasan Gharehpasha & Mohammad Masdari

  2. Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran

    Ahmad Jafarian

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  1. Sasan Gharehpasha
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  2. Mohammad Masdari
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  3. Ahmad Jafarian
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Correspondence to Mohammad Masdari.

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Gharehpasha, S., Masdari, M. & Jafarian, A. Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm. Artif Intell Rev 54, 2221–2257 (2021). https://doi.org/10.1007/s10462-020-09903-9

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