Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Cluster Computing
Published in: Cluster Computing 3/2021

04-05-2021

The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Authors: Behrouz Pourghebleh, Amir Aghaei Anvigh, Amir Reza Ramtin, Behnaz Mohammadi

Published in: Cluster Computing | Issue 3/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.
previous article Cloud-based intrusion detection using kernel fuzzy clustering and optimal type-2 fuzzy neural network
next article Dynamic provisioning with structure inspired selection and limitation of VMs based cost-time efficient workflow scheduling in the cloud

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
1.
Seyfollahi, A., Ghaffari, A.: A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks. Comput. Netw. 179, 107368 (2020)
2.
Meng, Q., Zhang, J.: Optimization and application of artificial intelligence routing algorithm. Clust. Comput. 22(4), 8747–8755 (2019)
3.
Hayyolalam, V., Kazem, A.A.P.: A systematic literature review on QoS-aware service composition and selection in cloud environment. J. Netw. Comput. Appl. 110, 52–74 (2018)
4.
Li, S., Da Xu, L., Zhao, S.: 5G Internet of Things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)
5.
Moniruzzaman, M., et al.: Blockchain for smart homes: review of current trends and research challenges. Comput. Electr. Eng. 83, 106585 (2020)
6.
Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)
7.
Ghobaei-Arani, M., Khorsand, R., Ramezanpour, M.: An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. J. Netw. Comput. Appl. 142, 76–97 (2019)
8.
Sehgal, N.K., Bhatt, P.C.: Cloud Computing. Springer, Cham (2018)
9.
Kumar, E.S., Vengatesan, K.: Trust based resource selection with optimization technique. Clust. Comput. 22(1), 207–213 (2019)
10.
Magid, S.A., Petrini, F., Dezfouli, B.: Image classification on IoT edge devices: profiling and modeling. Clust. Comput. 23(1), 1–19 (2019)
11.
Nikoui, T.S., Rahmani, A.M., Tabarsaied, H.: Data management in fog computing. In: Fog and Edge Computing: Principles and Paradigms, pp. 171–190. Wiley, Hoboken (2019)
12.
Kunwar, V., et al.: Load balancing in cloud—a systematic review. In: Big Data Analytics, pp. 583–593. Springer, Singapore (2018)
13.
Mahapatra, P.K., et al.: Security model for preserving privacy of image in cloud. In: Advances in Data Science and Management, pp. 247–256. Springer, Singapore (2020)
14.
Sundararaj, V.: Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel. Pers. Commun. 104(1), 173–197 (2019)
15.
Darzanos, G., Koutsopoulos, I., Stamoulis, G.D.: Cloud federations: economics, games and benefits. IEEE/ACM Trans. Netw. 27(5), 2111–2124 (2019)
16.
Sohaib, O., et al.: Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method. Comput. Ind. Eng. 132, 47–58 (2019)
17.
Nzanywayingoma, F., Yang, Y.: Efficient resource management techniques in cloud computing environment: a review and discussion. Int. J. Comput. Appl. 41(3), 165–182 (2019)
18.
Oliveira, T., et al.: Understanding SaaS adoption: the moderating impact of the environment context. Int. J. Inf. Manag. 49, 1–12 (2019)
19.
Costache, S., et al.: Resource management in cloud platform as a service systems: analysis and opportunities. J. Syst. Softw. 132, 98–118 (2017)
20.
Haghighi, M.A., Maeen, M., Haghparast, M.: An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Wirel. Pers. Commun. 104(4), 1367–1391 (2019)
21.
Chen, T., et al.: Improving resource utilization via virtual machine placement in data center networks. Mob. Netw. Appl. 23(2), 227–238 (2018)
22.
23.
Piraghaj, S.F., et al.: Virtual machine customization and task mapping architecture for efficient allocation of cloud data center resources. Comput. J. 59(2), 208–224 (2016)
24.
Bermejo, B., Juiz, C.: Virtual machine consolidation: a systematic review of its overhead influencing factors. J. Supercomput. 76(1), 324–361 (2020)
25.
Li, Z., et al.: Energy-efficient and quality-aware VM consolidation method. Future Gener. Comput. Syst. 102, 789–809 (2020)
26.
27.
Malekloo, M.-H., Kara, N., El Barachi, M.: An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain. Comput. Inform. Syst. 17, 9–24 (2018)
28.
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)
29.
Abdelsamea, A., et al.: Virtual machine consolidation challenges: a review. Int. J. Innov. Appl. Stud. 8(4), 1504 (2014)
30.
Abadi, R.M.B., Rahmani, A.M., Alizadeh, S.H.: Challenges of server consolidation in virtualized data centers and open research issues: a systematic literature review. J. Supercomput. 76, 1–52 (2019)
31.
Ahmad, R.W., et al.: A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J. Netw. Comput. Appl. 52, 11–25 (2015)
32.
Bermejo, B., Juiz, C., Guerrero, C.: Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J. Supercomput. 75(2), 808–836 (2019)
33.
Shirvani, M.H., Rahmani, A.M., Sahafi, A.: A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challenges. J. King Saud Univ. Comput. Inf. Sci. 32(3), 267–286 (2020)
34.
Abadi, R.M.B., Rahmani, A.M., Alizadeh, S.H.: Server consolidation techniques in virtualized data centers of cloud environments: a systematic literature review. Softw. Pract. Exp. 48(9), 1688–1726 (2018)
35.
Masdari, M., et al.: Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Clust. Comput. 23, 1–31 (2019)
36.
Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Internet of Things applications: a systematic review. Comput. Netw. 148, 241–261 (2019)
37.
Pourghebleh, B., Hayyolalam, V., Anvigh, A.A.: Service discovery in the Internet of Things: review of current trends and research challenges. Wirel. Netw. 26(7), 5371–5391 (2020)
38.
Pourghebleh, B., Hayyolalam, V.: A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things. Clust. Comput. 23, 1–21 (2019)
39.
Pourghebleh, B., Wakil, K., Navimipour, N.J.: A comprehensive study on the trust management techniques in the Internet of Things. IEEE Internet Things J. 6(6), 9326–9337 (2019)
40.
Pourghebleh, B., Navimipour, N.J.: Data aggregation mechanisms in the Internet of Things: a systematic review of the literature and recommendations for future research. J. Netw. Comput. Appl. 97, 23–34 (2017)
41.
Hayyolalam, V., et al.: Exploring the state-of-the-art service composition approaches in cloud manufacturing systems to enhance upcoming techniques. Int. J. Adv. Manuf. Technol. 105(1–4), 471–498 (2019)
42.
Pourghebleh, B., Jafari Navimipour, N.: Towards efficient data collection mechanisms in the vehicular ad hoc networks. Int. J. Commun. Syst. 32(5), e3893 (2019)
43.
Hayyolalam, V., Pourghebleh, B., Pourhaji Kazem, A.A.: Trust management of services (TMoS): investigating the current mechanisms. Trans. Emerg. Telecommun. Technol. 31(10), e4063 (2020)
44.
Feller, E., Morin, C., Esnault, A.: A case for fully decentralized dynamic VM consolidation in clouds. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings. 2012. IEEE (2012)
45.
Farahnakian, F., et al.: Energy-aware dynamic VM consolidation in cloud data centers using ant colony system. In: 2014 IEEE 7th International Conference on Cloud Computing. 2014. IEEE (2014).
46.
Matre, P., Silakari, S., Chourasia, U.: Ant colony optimization (ACO) based dynamic VM consolidation for energy efficient cloud computing. Int. J. Comput. Sci. Inf. Secur. 14(8), 345 (2016)
47.
Ferdaus, M.H., et al.: Multi-objective, decentralized dynamic virtual machine consolidation using ACO metaheuristic in computing clouds (2017). arXiv preprint arXiv:1706.06646
48.
Zhang, H., et al.: Workload-aware VM consolidation in cloud based on max–min ant system. In: International Conference on Cloud Computing and Security. 2017. Springer (2017)
49.
Ashraf, A., Porres, I.: Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int. J. Parallel Emerg. Distrib. Syst. 33(1), 103–120 (2018)
50.
Aryania, A., Aghdasi, H.S., Khanli, L.M.: Energy-aware virtual machine consolidation algorithm based on ant colony system. J. Grid Comput. 16(3), 477–491 (2018)
51.
Liu, F., et al.: A virtual machine consolidation algorithm based on ant colony system and extreme learning machine for cloud data center. IEEE Access 8, 53–67 (2019)
52.
Zheng, Q., et al.: Multi-objective optimization algorithm based on BBO for virtual machine consolidation problem. In: 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS). 2015. IEEE (2015)
53.
Shi, K., et al.: Multi-objective biogeography-based method to optimize virtual machine consolidation. In: Proceedings of the International Conference on Software Engineering and Knowledge Engineering. 2016
54.
Zheng, Q., et al.: Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gener. Comput. Syst. 54, 95–122 (2016)
55.
Jiang, J., et al.: DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model. Future Gener. Comput. Syst. 74, 132–141 (2017)
56.
Li, Z., et al.: Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener. Comput. Syst. 80, 139–156 (2018)
57.
Joshi, S., Kaur, S.: Cuckoo search approach for virtual machine consolidation in cloud data centre. In: International Conference on Computing, Communication and Automation. 2015. IEEE (2015)
58.
Naik, B.B., et al.: Developing a cloud computing data center virtual machine consolidation based on multi-objective hybrid fruit-fly cuckoo search algorithm. In: 2018 IEEE 5G World Forum (5GWF). 2018. IEEE (2018)
59.
60.
Yavari, M., Rahbar, A.G., Fathi, M.H.: Temperature and energy-aware consolidation algorithms in cloud computing. J. Cloud Comput. 8(1), 1–16 (2019)
61.
Wu, Q., Ishikawa, F.: Heterogeneous virtual machine consolidation using an improved grouping genetic algorithm. In: IEEE 17th International Conference on High Performance Computing and Communications. 2015. IEEE (2015)
62.
Wu, Q., et al.: Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans. Serv. Comput. 12(4), 550–563 (2016)
63.
Theja, P.R., Babu, S.K.: Evolutionary computing based on QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers. Cybern. Inf. Technol. 16(2), 97–112 (2016)MathSciNet
64.
Arianyan, E., Taheri, H., Sharifian, S.: Multi target dynamic VM consolidation in cloud data centers using genetic algorithm. J. Inf. Sci. Eng. 32(6), 1575–1593 (2016)MathSciNet
65.
Riahi, M., Krichen, S.: A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study. J. Supercomput. 74(7), 2984–3015 (2018)
66.
Yousefipour, A., Rahmani, A.M., Jahanshahi, M.: Energy and cost-aware virtual machine consolidation in cloud computing. Softw. Pract. Exp. 48(10), 1758–1774 (2018)
67.
Fathi, M.H., Khanli, L.M.: Consolidating VMs in green cloud computing using harmony search algorithm. In: Proceedings of the 2018 International Conference on Internet and e-Business (2018)
68.
Kim, M., Hong, J., Kim, W.: An efficient representation using harmony search for solving the virtual machine consolidation. Sustainability 11(21), 6030 (2019)
69.
Dashti, S.E., Rahmani, A.M.: Dynamic VMs placement for energy efficiency by PSO in cloud computing. J. Exp. Theor. Artif. Intell. 28(1–2), 97–112 (2016)
70.
Li, H., et al.: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3), 303–317 (2016)MathSciNetMATH
71.
Marotta, A., Avallone, S.: A simulated annealing based approach for power efficient virtual machines consolidation. In: 2015 IEEE 8th International Conference on Cloud Computing. 2015. IEEE (2015)
72.
Rajabzadeh, M., Haghighat, A.T.: Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers. J. Supercomput. 73(5), 2001–2017 (2017)
73.
Nasim, R., Kassler, A.J.: A robust Tabu Search heuristic for VM consolidation under demand uncertainty in virtualized datacenters. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 2017. IEEE (2017)
74.
Abdel-Basset, M., Abdle-Fatah, L., Sangaiah, A.K.: An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust. Comput. 22(4), 8319–8334 (2019)
75.
Al-Moalmi, A., et al.: A whale optimization system for energy-efficient container placement in data centers. Expert Syst. Appl. 164, 113719 (2021)
76.
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
77.
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)MathSciNetMATH
78.
Mareli, M., Twala, B.: An adaptive Cuckoo search algorithm for optimisation. Appl. Comput. Inform. 14(2), 107–115 (2018)
79.
Zhang, T., Geem, Z.W.: Review of harmony search with respect to algorithm structure. Swarm Evol. Comput. 48, 31–43 (2019)
80.
Zhang, W., et al.: Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy 163, 191–207 (2018)
81.
Xue, X., Chen, J.: Using Compact Evolutionary Tabu Search algorithm for matching sensor ontologies. Swarm Evol. Comput. 48, 25–30 (2019)
82.
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
83.
Hayyolalam, V., Kazem, A.A.P.: Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020)
84.
Seyfollahi, A., Ghaffari, A.: Reliable data dissemination for the Internet of Things using Harris Hawks optimization. Peer-to-Peer Netw. Appl. 13(6), 1886–1902 (2020)
85.
Abualigah, L., et al.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)MathSciNetMATH
86.
Kaveh, A., Zaerreza, A.: Shuffled Shepherd optimization method: a new meta-heuristic algorithm. Eng. Comput. 37(7), 2357–2389 (2020)
87.
Yapici, H., Cetinkaya, N.: A new meta-heuristic optimizer: pathfinder algorithm. Appl. Soft Comput. 78, 545–568 (2019)
88.
Abualigah, L., Diabat, A.: A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust. Comput. 24, 1–19 (2020)
89.
Hayyolalam, V., Kazem, A.A.P.: QoS-aware optimization of cloud service composition using symbiotic organisms search algorithm. J. Intell. Proced. Electr. Technol. 8(32), 29–38 (2017)
Metadata
Title
The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments
Authors
Behrouz Pourghebleh
Amir Aghaei Anvigh
Amir Reza Ramtin
Behnaz Mohammadi
Publication date
04-05-2021
Publisher
Springer US
Published in
Cluster Computing / Issue 3/2021
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-021-03294-4

Other articles of this Issue 3/2021

Cluster Computing 3/2021 Go to the issue

OriginalPaper

TC3PoP: a time-cost compromised workflow scheduling heuristic customized for cloud environments

OriginalPaper

Early DGA-based botnet identification: pushing detection to the edges

OriginalPaper

A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm

OriginalPaper

Resource allocation mechanisms for maximizing provider’s revenue in infrastructure as a service (IaaS) cloud

OriginalPaper

CUDAQuat: new parallel framework for fast computation of quaternion moments for color images applications

OriginalPaper

A robust blind medical image watermarking approach for telemedicine applications

Premium Partner

    Image Credits