nach oben

Journal of Network and Systems Management

Erschienen in:

26.05.2020

Modified Dragonfly Algorithm for Optimal Virtual Machine Placement in Cloud Computing

verfasst von: Atul Tripathi, Isha Pathak, Deo Prakash Vidyarthi

Erschienen in: Journal of Network and Systems Management | Ausgabe 4/2020

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

The ease and affordability offered by the cloud computing has attracted large number of customers towards it. Cloud service providers offer its services, to the cloud customers, usually in form of Virtual Machines (VMs). With the growth in the number of customers, cloud data centers encounter overwhelming number of VM requests. These requests need to be mapped on the real cloud hardware and therefore, VM placement has been an important research area in the cloud research community. Virtual machine placement, being an NP hard problem, is modelled as an optimization problem with the objective to optimize resource wastage. Dragonfly Algorithm (DA), a nature inspired technique, originates from static and dynamic swarming behavior of dragonfly and is well suited to solve VM placement problem. Therefore, in the proposed work, a modified dragonfly algorithm is applied for VM placement for better resource utilization at cloud data centers. The performance of the proposed model is analyzed through simulation and comparative study. Observations, obtained from the experiments, exhibit the superiority of the proposed model in solving VM placement problem.

Vorheriger Artikel Distributed Network Coding-Aware Routing Protocol Incorporating Fuzzy-Logic-Based Forwarders in Wireless Ad hoc Networks

Nächster Artikel Detecting and Mitigating DDoS Attack in Named Data Networking

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

Zhang, Q., Cheng, L., Boutaba, R.: Cloud Coimputing: state-of-the-art and research challenges. In: J Internet Serv, pp. 626–631. Springer Verlag, IEEE, (2010)

Rhoton, J.: Cloud computing explained: implementation handbook for enterprises (2009)

Addya, S.K., Turuk, A.K., Sahoo, B., Sarkar, M., Biswash, S.K.: Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers. Eng. Sci. Technol. Int. J. 20, 1249–1259 (2017). https://doi.org/10.1016/j.jestch.2017.09.003CrossRef

Békési, J., Galambos, G., Kellerer, H.: A 5/4 linear time bin packing algorithm. J. Comput. Syst. Sci. 60, 145–160 (2000). https://doi.org/10.1006/jcss.1999.1667MathSciNetCrossRefMATH

Grit, L., Irwin, D., Yumerefendi, A., Chase, J.: Virtual machine hosting for networked clusters: Building the foundations for “autonomic” orchestration. In: VTDC 2006 2nd International Workshop on virtualization technology in distributed computing; held in conjunction with SC06. IEEE Computer Society, pp. 1–7 (2006)

Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans. Serv. Comput. 3, 266–278 (2010). https://doi.org/10.1109/TSC.2010.25CrossRef

Cardosa, M., Korupolu, MR., Singh. A .: Shares and utilities based power consolidation in virtualized server environments. In: 2009 IFIP/IEEE International Symposium on integrated network management, IM 2009, pp. 327–334. IEEE, New York (2009)

Bichler, M., Setzer, T., Speitkamp, B.: Capacity planning for virtualized servers. In: Workshop on information technologies and systems, Milwaukee, Wisconsin. Milwaukee, Wisconsin, USA (2006)

Srikantaiah, S., Kansal, A., Zhao, F,: Energy aware consolidation for cloud computing. In: Proceedings of the 2008 Conference on power aware computing and systems (HotPower) (2008)

10.

Verma, A., Ahuja, P., Neogi, A.: pMapper: Power and migration cost aware application placement in virtualized systems. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp. 243–264. Springer-Verlag, New York Inc (2008)

11.

Li, B., Li, J., Huai, J., Wo, T., Li, Q., Zhong, L.: EnaCloud: an energy-saving application live placement approach for cloud computing environments. In: CLOUD 2009–2009 IEEE International Conference on cloud computing, pp. 2009. IEEE, New York (2009)

12.

Verma, A., Ahuja, P. Neogi, A .: Power-aware dynamic placement of HPC applications. In: Proceedings of the 22nd annual international conference on ACM, pp. 175–184 (2008)

13.

Lin, J.W., Chen, C.H., Lin, C.Y.: Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications. Futur. Gener. Comput. Syst 37, 478–487 (2014). https://doi.org/10.1016/j.future.2013.12.034MathSciNetCrossRef

14.

Liao, X., Jin, H., Liu, H.: Towards a green cluster through dynamic remapping of virtual machines. Futur. Gener. Comput. Syst 28, 469–477 (2012). https://doi.org/10.1016/j.future.2011.04.013CrossRef

15.

Van, H.N., Tran, F.D., Menaud, J.M.: Performance and power management for cloud infrastructures. In: Proceedings 2010 IEEE 3rd International Conference on cloud computing, CLOUD 2010, pp. 329–336. IEEE, New York. (2010)

16.

Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: Proceedings 2011 12th IEEE/ACM International Conference on grid computing, Grid 2011. IEEE Computer Society, pp. 26–33 (2011)

17.

Jeyarani, R., Nagaveni, N., Ram, R.V.: Self adaptive particle swarm optimization for efficient virtual machine provisioning in Cloud. In: International Journal of intelligent information technologies, pp. 88–107. IGI Global, Pennsylvania (2011)

18.

Mi, H., Wang, H., Yin, G., Zhou, Y., Shi, D., Yuan, L.: Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: Proceedings 2010 IEEE 7th International Conference on services computing, SCC 2010, pp. 514–521. IEEE, New York (2010)

19.

Xu, J., Fortes, J.A.B.: Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings 2010 IEEE/ACM International Conference on green computing and communications, GreenCom 2010, 2010 IEEE/ACM International Conference on cyber, physical and social computing, CPSCom 2010, pp. 179–188. IEEE, New York (2010)

20.

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, 1230–1242 (2013). https://doi.org/10.1016/j.jcss.2013.02.004MathSciNetCrossRefMATH

21.

Jeyarani, R., Nagaveni, N., Vasanth Ram, R.: Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence. Futur. Gener. Comput. Syst 28, 811–821 (2012). https://doi.org/10.1016/j.future.2011.06.002CrossRef

22.

Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Black-box and Gray-box strategies for virtual machine migration. In: 4th USENIX Symposium on networked systems design and implementation, pp. 229–242 (2007)

23.

Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Futur. Gener. Comput. Syst 28, 755–768 (2012). https://doi.org/10.1016/j.future.2011.04.017CrossRef

24.

Hermenier, F., Lorca, X., Menaud, J.M., Muller, G., Lawall, J.: Entropy: a Consolidation Manager for Clusters. In: Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on virtual execution environments VEE’09. ACM, pp 41–50 (2009)

25.

Duong-Ba, T.H., Nguyen, T., Bose, B., Tran, T.T.: A dynamic virtual machine placement and migration scheme for data centers. IEEE Trans. Serv, Comput (2018)

26.

Tripathi, A., Pathak, I., Vidyarthi, D.P.: Energy efficient VM placement for effective resource utilization using modified binary PSO. Comput. J. 61, 832–846 (2018). https://doi.org/10.1093/comjnl/bxx096CrossRef

27.

Zheng, Q., Li, R., Li, X., Shah, N., Zhang, J., Tian, F., Chao, K.M., Li, J.: Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Futur. Gener. Comput. Syst. 54, 95–122 (2016). https://doi.org/10.1016/j.future.2015.02.010CrossRef

28.

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. Cluster Comput (2018). https://doi.org/10.1007/s10586-018-1769-zCrossRef

29.

Satpathy, A., Addya, S.K., Turuk, A.K., Majhi, B., Sahoo, G.: Crow search based virtual machine placement strategy in cloud data centers with live migration. Comput. Electr. Eng. 69, 334–350 (2018). https://doi.org/10.1016/j.compeleceng.2017.12.032CrossRef

30.

Singh, A., Korupolu, M., Mohapatra, D.:Server-storage virtualization: Integration and load balancing in data centers. In: 2008 SC International Conference for high performance computing, networking, storage and analysis, SC 2008, pp. 1–12. IEEE, New York (2008)

31.

Ghribi, C., Hadji, M., Zeghlache, D.: Energy efficient VM scheduling for cloud data centers: Exact allocation and migration algorithms. In: Proceedings 13th IEEE/ACM International Symposium on cluster, cloud, and grid computing, CCGrid 2013. pp. 671–678. IEEE, New York (2013)

32.

Wang, M., Meng, X., Zhang, L.: Consolidating virtual machines with dynamic bandwidth demand in data centers. In: Proceedings IEEE INFOCOM, pp. 71–75. IEEE, New York (2011)

33.

Alahmadi, A., Alnowiser, A., Zhu, M.M., Che, D., Ghodous, P.: Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud. In: Proceedings 2014 International Conference on computational science and computational intelligence, CSCI 2014, pp. 69–74 (2014)

34.

Chen, W., Hu, Z.-H., You-Gan, W.: Exact algorithms for energy-efficient virtual machine placement in data centers. Futur. Gener. Comput. Syst 106, 77–91 (2020). https://doi.org/10.1016/j.future.2019.12.043CrossRef

35.

Ponraj, A.: Optimistic virtual machine placement in cloud data centers using queuing approach. Futur. Gener. Comput. Syst. 93, 338–344 (2019). https://doi.org/10.1016/j.future.2018.10.022CrossRef

36.

Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2016). https://doi.org/10.1007/s00521-015-1920-1CrossRef

37.

Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model, in computer graphics. ACM SIGGRAPH Comput. Graph 21, 25–34 (1987)CrossRef

38.

Yang, X.-S.: Nature-inspired metaheuristic algorithms (2010)

39.

Mirjalili, S., Lewis, A.: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization. Swarm. Evol. Comput. 9, 1–14 (2013). https://doi.org/10.1016/j.swevo.2012.09.002CrossRef

40.

Mirjalili, S., Wang, G.G., dos Coelho L, S.: Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput. Appl. 25, 1423–1435 (2014). https://doi.org/10.1007/s00521-014-1629-6CrossRef

41.

Mafarja, M., Aljarah, I., Heidari, A.A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S.: Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge Based Syst 161, 185–204 (2018). https://doi.org/10.1016/j.knosys.2018.08.003CrossRef

42.

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, 23–50 (2011). https://doi.org/10.1002/spe.995CrossRef

43.

Amazon.: EC2 Instance types –Amazon Web Services (AWS). Amazon, Seattle (2019). http://aws.amazon.com/ec2/instance-types

Titel: Modified Dragonfly Algorithm for Optimal Virtual Machine Placement in Cloud Computing
verfasst von: Atul Tripathi
Isha Pathak
Deo Prakash Vidyarthi
Publikationsdatum: 26.05.2020
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 4/2020
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-020-09538-9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2020

A New Multi-Agent Hybrid Marketplace for Cloud Resource Allocation

Monte Carlo Tree Search with Last-Good-Reply Policy for Cognitive Optimization of Cloud-Ready Optical Networks

ORCHESTRA: Supercharging Wireless Backhaul Networks Through Multi-technology Management

Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines

Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

DFIOT: Data Fusion for Internet of Things