Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Cluster Computing
Erschienen in: Cluster Computing 4/2019

24.01.2018

An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment

verfasst von: Mohamed Abdel-Basset, Laila Abdle-Fatah, Arun Kumar Sangaiah

Erschienen in: Cluster Computing | Sonderheft 4/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The consolidation of virtual machine (VM) is the strategy of efficient and intelligent use of cloud datacenters resources. One of the important subproblems of VM consolidation is VM placement problem. The main objective of VM placement problem is to minimize the number of running physical machines or hosts in cloud datacenters. This paper focuses on solving VM placement problem with respect to the available bandwidth which is formulated as variable sized bin packing problem. Moreover, a new bandwidth allocation policy is developed and hybridized with an improved variant of whale optimization algorithm (WOA) called improved Lévy based whale optimization algorithm. Cloudsim toolkit is used in order to test the validity of the proposed algorithm on 25 different data sets that generated randomly and compared with many optimization algorithms including: WOA, first fit, best fit, particle swarm optimization, genetic algorithm, and intelligent tuned harmony search. The obtained results are analyzed by Friedman test which indicates the prosperity of the proposed algorithm for minimizing the number of running physical machine.
Vorheriger Artikel Anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment
Nächster Artikel Image compression and reconstruction by examplar based inpainting using wavelet transform on textural regions

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Qian, L., Luo, Z., Du, Y., Guo, L.: Cloud computing: an overview. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing, pp. 626–631. Springer, Berlin (2009)
2.
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J Internet Serv. Appl. 1(1), 7–18 (2010)
3.
Rhoton, J.: Cloud computing explained. Recursive Press, London (2013)
4.
Wohl, A.: Software as a Service (SaaS). In: Simon, P. (ed.) The Next Wave of Technologie: Opportunities from Chaos, pp. 97–113. Wiley, Hoboken (2010)
5.
Santana, M.: Infrastructure as a Service (IaaS). In: Vacca, J.R. (ed.) Cloud Computing Security: Foundations and Challenges. CRC Press, Boca Raton (2016)
6.
Beimborn, D., Miletzki, T., Wenzel, S.: Platform as a service (PaaS). Wirtschaftsinformatik 53(5), 371–375 (2011)
7.
Abawajy, J.H.: An efficient adaptive scheduling policy for high-performance computing. Future Gener. Comput. Syst. 25(2), 364–370 (2009)
8.
Borgetto, D., Casanova, H., Da Costa, G., Pierson, J.M.: Energy-aware service allocation. Future Gener. Comput. Syst. 28(4), 769–779 (2012)
9.
Stavrinides, G.L., Karatza, H.D.: Scheduling real-time DAGs in heterogeneous clusters by combining imprecise computations and bin packing techniques for the exploitation of schedule holes. Future Gener. Comput. Syst. 28(6), 977–988 (2012)
10.
Verbelen, T., Stevens, T., De Turck, F., Dhoedt, B.: Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gener. Comput. Syst. 29(1), 451–459 (2013)
11.
Sheikhalishahi, M., Wallace, R.M., Grandinetti, L., Vazquez-Poletti, J.L., Guerriero, F.: A multi-dimensional job scheduling. Future Gener. Comput. Syst. 54, 123–131 (2016)
12.
Bassem, C., Bestavros, A.: Multi-Capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments. Future Gener. Comput. Syst. 72, 129–144 (2017)
13.
Ye, D., Chen, J.: Non-cooperative games on multidimensional resource allocation. Future Gener. Comput. Syst. 29(5), 1345–1352 (2013)
14.
Kessaci, Y., Melab, N., Talbi, E.G.: A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager. Future Gener. Comput. Syst. 36, 237–256 (2014)
15.
Rao, K.S., Thilagam, P.S.: Heuristics based server consolidation with residual resource defragmentation in cloud data centers. Future Gener. Comput. Syst. 50, 87–98 (2015)
16.
Aroca, J.A., Anta, A.F., Mosteiro, M.A., Thraves, C., Wang, L.: Power-efficient assignment of virtual machines to physical machines. Future Gener. Comput. Syst. 54, 82–94 (2016)
17.
Hallawi, H., Mehnen, J., He, H.: Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation. Future Gener. Comput. Syst. 69, 1–10 (2017)
18.
Xing, Y., Zhan, Y.: Virtualization and cloud computing. In: Zhang, Y. (ed.) Future Wireless Networks and Information Systems, pp. 305–312. Springer, Berlin (2012)
19.
Vaezi, M., Zhang, Y.: Virtualization and cloud computing. In: Vaezi, M., Zhang, Y. (eds.) Cloud Mobile Networks. Springer, New York (2017)
20.
21.
Challita, S., Paraiso, F., Merle, P.: Study of virtual machine placement optimization in data centers. In: Proceedings of the 7th International Conference on Cloud Computing and Services Science, CLOSER 2017 (2017)
22.
Chaisiri, S., Lee, B.S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: 2009 IEEE Asia-Pacific Services Computing Conference (APSCC 2009), pp. 103–110. IEEE (2009)
23.
Alicherry, M., Lakshman, T.V.: Optimizing data access latencies in cloud systems by intelligent virtual machine placement. In: 2013 Proceedings IEEE INFOCOM, pp. 647–655. IEEE (2013)
24.
Dang, H.T., Hermenier, F.: Higher SLA satisfaction in datacenters with continuous VM placement constraints. In: Proceedings of the 9th Workshop on Hot Topics in Dependable Systems, p. 1. ACM (2013)
25.
Adamuthe, A.C., Pandharpatte, R.M., Thampi, G.T.: Multiobjective virtual machine placement in cloud environment. In: Proceedings of the 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE), pp. 8–13. IEEE (2013)
26.
Sampson, J.R.: Adaptation in Natural and Artificial Systems (John H. Holland). SIAM Rev. 18(2), 529–530 (1976)
27.
Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(2), 221–248 (1994)
28.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(1), 182–197 (2002)
29.
Xu, J., Fortes, J.A.: Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings of the 2010 IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, pp. 179–188. IEEE Computer Society (2010)
30.
Zadeh, L.A.: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, vol. 6. World Scientific, Singapore (1996)
31.
Perumal, B., Murugaiyan, A.: A firefly colony and its fuzzy approach for server consolidation and virtual machine placement in cloud datacenters. Adv. Fuzzy Syst. 2016, 5 (2016)MathSciNet
32.
Yang, X.S.: Firefly algorithms for multimodal optimization. In: International Symposium on Stochastic Algorithms, pp. 169–178. Springer, Berlin (2009)
33.
Wu, G., Tang, M., Tian, Y.C., Li, W.: Energy-efficient virtual machine placement in data centers by genetic algorithm. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) Neural Information Processing, pp. 315–323. Springer, Berlin (2012)
34.
Wu, Y., Tang, M., Fraser, W.: A simulated annealing algorithm for energy efficient virtual machine placement. In: Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1245–1250. IEEE (2012)
35.
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNet
36.
Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(7), 1230–1242 (2013)MathSciNet
37.
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
38.
Wang, S., Liu, Z., Zheng, Z., Sun, Q., Yang, F.: Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In: Proceedings of the 2013 International Conference on Parallel and Distributed Systems (ICPADS), pp. 102–109. IEEE (2013)
39.
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS’95), pp. 39–43. IEEE (1995)
40.
Joshi, S., Kaur, S.: Cuckoo search approach for virtual machine consolidation in cloud data centre. In: Proceedings of the 2015 International Conference on Computing, Communication & Automation (ICCCA), pp. 683–686. IEEE (2015)
41.
Ali, H.M., Lee, D.C.: A biogeography-based optimization algorithm for energy efficient virtual machine placement. In: Proceedings of the 2014 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–6. IEEE (2014)
42.
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE (2009)
43.
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(5), 702–713 (2008)
44.
Alboaneen, D.A., Tianfield, H., Zhang, Y.: Glowworm swarm optimisation algorithm for virtual machine placement in cloud computing. In: Proceedings of the 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 808–814. IEEE (2016)
45.
Patel, P., Ranabahu, A., Sheth, A.: Service level agreement in cloud computing. In: Cloud Workshops at OOPSLA09, Orlando, FL, 25–29 October (2009)
46.
Krishnanand, K.N., Ghose, D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst. 2(2), 209–222 (2006)
47.
Friesen, D.K., Langston, M.A.: Variable sized bin packing. SIAM J. Comput. 15(1), 222–230 (1986)
48.
Haouari, M., Serairi, M.: Relaxations and exact solution of the variable sized bin packing problem. Comput. Optim. Appl. 48(1), 345–368 (2011)MathSciNet
49.
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
50.
51.
Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E 49(4), 4677 (1994)
52.
Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(8), 1222–1237 (2014)MathSciNet
53.
Devaney, R.: An Introduction to Chaotic Dynamical Systems. Addison-Wesley, Reading (1989). 13046
54.
Qian, B., Wang, L., Rong, H., Wang, W.L., Huang, D.X., Wang, X.: A hybrid differential evolution method for permutation flow-shop scheduling. Int. J. Adv. Manuf. Technol. 38(7–8), 757–777 (2008)
55.
Rhee, W.T., Talagrand, M.: On line bin packing with items of random size. Math. Oper. Res. 18(1), 438–445 (1993)MathSciNet
56.
Banzhaf, W.: The "molecular" traveling salesman. Biol. Cybern. 64(1), 7–14 (1990)
57.
Michalewicz, Z.: Genetic Algorithms \(+\) Data Structures \(=\) Evolution Programs. Springer, New York (1996)
58.
Grefenstette, J., Gopal, R., Rosmaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and their Applications, pp. 160–165 (1985)
59.
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
60.
Calheiros, R.N., Ranjan, R., De Rose, C.A., Buyya, R.: Cloudsim: a novel framework for modeling and simulation of cloud computing infrastructures and services. arXiv preprint, arXiv:​0903.​2525 (2009)
61.
Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: Proceedings of the International Conference on High Performance Computing & Simulation (HPCS’09), pp. 1–11. IEEE (2009)
62.
Johnson, D.S., Demers, A., Ullman, J.D., Garey, M.R., Graham, R.L.: Worst-case performance bounds for simple one-dimensional packing algorithms. SIAM J. Comput. 3(3), 299–325 (1974)MathSciNet
63.
Yadav, P., Kumar, R., Panda, S.K., Chang, C.S.: An intelligent tuned harmony search algorithm for optimisation. Inf. Sci. 196, 47–72 (2012)
64.
Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(1), 495–513 (2016)
65.
Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference, pp. 977–979. Springer, Berlin (2011)
66.
Asogbon, M.G., Samuel, O.W., Omisore, M.O., Awonusi, O.: Enhanced neuro-fuzzy system based on genetic algorithm for medical diagnosis. J. Med. Diagn. Methods 5(205), 2 (2016)
67.
Omisore, M.O., Samuel, O.W., Atajeromavwo, E.J.: A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis. Appl. Comput. Inform. 13(1), 27–37 (2015)
68.
Samuel, O.W., Omisore, M.O., Ojokoh, B.A., Atajeromavwo, E.J.: Enhanced cloud based model for healthcare delivery organizations in developing countries. Int. J. Comput. Appl. 74(1), (2013)
Metadaten
Titel
An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment
verfasst von
Mohamed Abdel-Basset
Laila Abdle-Fatah
Arun Kumar Sangaiah
Publikationsdatum
24.01.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1769-z

Weitere Artikel der Sonderheft 4/2019

Cluster Computing 4/2019 Zur Ausgabe

OriginalPaper

Management of access privileges for dynamic access control

OriginalPaper

A novel energy efficient and scalable hybrid-mac protocol for massive M2M networks

OriginalPaper

Research on the strategic alliance innovation system evolution mechanism: the perspective of knowledge flow

OriginalPaper

Research on hot news discovery model based on user interest and topic discovery

OriginalPaper

Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging

OriginalPaper

Sequential mining of real time moving object by using fast frequence pattern algorithm