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
Kongresse
Themenseiten
Künstliche Intelligenz Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
Zukunftswerkstatt Sales Excellence 2025 | 08. Mai 2025

KI als Booster im Vertrieb

Lernen Sie von Digital-Experten, wie der gezielte Einsatz von KI-Unterstützung Ihrem Unternehmen in vielen Bereichen hilft, im Vertrieb einen Schritt voraus zu sein. Jetzt wieder live vor Ort teilnehmen in Frankfurt am Main!
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in:

29.05.2024

Design and Development of Pragmatic Load Balancing Algorithm for Cloud Environment

verfasst von: Tejinder Sharma, R. P. S Bedi

Erschienen in: Wireless Personal Communications | Ausgabe 1/2024

Einloggen

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

search-config
loading …

Abstract

With due span of time, Cloud computing has grown phenomenally as it is a best approach for using the scalable shared pool of resources at economical cost. Though it seems easy but distribution of the jobs assigned to various Virtual Machines (VMs) is a cumbersome task for the Data Centre (DC) Controller. Various techniques have been proposed from time-to-time for balancing the load on distinct VMs available in the DCs. The primarily used load balancing techniques are Round Robin, Equal Spread Current Execution and Throttled. The major thrust for using the aforementioned techniques is to improve the performance parameters such as Overall Response Time (ORT), Data Center Processing Time (DCPT) and Cost. In this manuscript, a new algorithm named Pragmatic Load Balancing (PLB) has been designed and developed for optimization of afore-said parameters and various simulation scenarios have also been created for the evaluation of these parameters. In the given Scenarios created by using Cloud Analyst Simulator, different number of VMs like 20, 40, 60, 80 and 100 have been allotted with distinct number of DCs from 1 to 5 to the application at each Cloud Configuration and the divulged that the value of the performance parameters have been improved by using the PLB Algorithm in comparison to other available Load Balancing Algorithms.
Vorheriger Artikel A Novel Approach Based on Fuzzy Rule and LSOWL–CNN Forecasting Students with Dropout Prediction and Recommendation Model
Nächster Artikel Iterative Time-Varying Channel Prediction Based on the Vector Prony Method

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

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+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 "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
Literatur
1.
Vashish, K., & Shukla, S. K. (2010). A Survey on Grid Computing Approach. International Journal of Computer Science and Technology, 1(2), 192–195.
2.
Ghomi, E. J., Rahmani, A. M., & Qader, N. N. (2017). Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications, 88, 50–71.CrossRef
3.
4.
Devi, D. C., & Uthariaraj, V. R. (2016). Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. The scientific world journal, 2016.
5.
Mondal, B., Dasgupta, K., & Dutta, P. (2012). Load balancing in cloud computing using stochastic hill climbing-a soft computing approach. Procedia Technology, 4, 783–789.CrossRef
6.
Mathur, S., Larji, A. A., & Goyal, A. (2017). Static load balancing using ASA max-min algorithm. Int J Res Appl Sci Eng Technol.
7.
Chen, H., Wang, F., Helian, N., & Akanmu, G. (2013, February). User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. In 2013 national conference on parallel computing technologies (PARCOMPTECH) (pp. 1–8). IEEE.
8.
James, J., & Verma, B. (2012). Efficient VM load balancing algorithm for a cloud computing environment. International Journal on Computer Science and Engineering, 4(9), 1658.
9.
Patel, G., Mehta, R., & Bhoi, U. (2015). Enhanced load balanced min-min algorithm for static meta task scheduling in cloud computing. Procedia Computer Science, 57, 545–553.CrossRef
10.
Singh, A. N., & Prakash, S. (2018). WAMLB: weighted active monitoring load balancing in cloud computing. In Big Data Analytics: Proceedings of CSI 2015 (pp. 677–685). Springer Singapore.
11.
Tripathi, A. M., & Singh, S. (2018). PMAMA: Priority-based modified active monitoring load balancing algorithm in cloud computing. J Adv Res Dynam Cont Syst, 809–823.
12.
13.
Galloway, J. M., Smith, K. L., & Vrbsky, S. S. (2011, October). Power aware load balancing for cloud computing. In proceedings of the world congress on engineering and computer science (Vol. 1, pp. 19–21).
14.
Haryani, N., & Jagli, D. (2014). Dynamic method for load balancing in cloud computing. IOSR Journal of Computer Engineering (IOSR-JCE), 16(4), 23–28.CrossRef
15.
Kumar, M., & Sharma, S. C. (2017). Dynamic load balancing algorithm for balancing the workload among virtual machine in cloud computing. Procedia Computer Science, 115, 322–329.CrossRef
16.
Adhikari, M., & Amgoth, T. (2018). Heuristic-based load-balancing algorithm for IaaS cloud. Future Generation Computer Systems, 81, 156–165.CrossRef
17.
Rajput, S. S., & Kushwah, V. S. (2016, December). A genetic based improved load balanced min-min task scheduling algorithm for load balancing in cloud computing. In 2016 8th international conference on Computational Intelligence and Communication Networks (CICN) (pp. 677–681). IEEE.
18.
Naha, R. K., & Othman, M. (2016). Cost-aware service brokering and performance sentient load balancing algorithms in the cloud. Journal of Network and Computer Applications, 75, 47–57.CrossRef
19.
Kaur, J., et al. (2017). Various load balancing algorithms for Cloud Computing. World Wide Journal of Multidisciplinary Research and Development, 3(5), 60–63.
20.
Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. International Journal of Parallel Programming, 42, 739–754.CrossRef
21.
Vanitha, M., & Marikkannu, P. (2017). Effective resource utilization in cloud environment through a dynamic well-organized load balancing algorithm for virtual machines. Computers & Electrical Engineering, 57, 199–208.CrossRef
22.
Cho, K. M., Tsai, P. W., Tsai, C. W., & Yang, C. S. (2015). A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Computing and Applications, 26, 1297–1309.CrossRef
23.
Vasudevan, S. K., Anandaram, S., Menon, A. J., & Aravinth, A. (2016). A novel improved honey bee based load balancing technique in cloud computing environment. Asian Journal of Information Technology, 15(9), 1425–1430.
24.
Sharma, S., Luhach, A. K., & Abdhullah, S. S. (2016). An optimal load balancing technique for cloud computing environment using bat algorithm. Indian journal of science and technology, 9(28).
25.
Chaczko, Z., Mahadevan, V., Aslanzadeh, S., & Mcdermid, C. (2011, September). Availability and load balancing in cloud computing. In International conference on computer and software modeling, singapore (Vol. 14, pp. 134–140). IACSIT Press.
26.
Abdulhussein, A., Joshi, A. N. A., Twinamatsiko, J. H., Lashkari, A. M., A. H., & Sadeghi, M. (2012). An Efficient Load Balancing Algorithm for virtualized Cloud Data Centers. Recent Advances in Electrical and Computer Engineering, 2(7), 65–71, ISBN, 978-1.
27.
Mondal, B., & Choudhury, A. (2015). Simulated annealing (SA) based load balancing strategy for cloud computing. International Journal of Computer Science and Information Technologies, 6(4), 3307–3312.
28.
Pasha, N., Agarwal, A., & Rastogi, R. (2014). Round robin approach for VM load balancing algorithm in cloud computing environment. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 34–39.
29.
Borovskiy, V., Wust, J., Schwarz, C., Koch, W., & Zeier, A. (2011). A linear programming approach for optimizing workload distribution in a cloud. Cloud Computing, 127–132.
30.
Polepally, V., & Shahu Chatrapati, K. (2019). Dragonfly optimization and constraint measure-based load balancing in cloud computing. Cluster Computing, 22(Suppl 1), 1099–1111.CrossRef
31.
Dasgupta, K., Mandal, B., Dutta, P., Mandal, J. K., & Dam, S. (2013). A genetic algorithm (Ga) based load balancing strategy for cloud computing. Procedia Technology, 10, 340–347.CrossRef
32.
Haidri, R. A., Katti, C. P., & Saxena, P. C. (2014, July). A load balancing strategy for Cloud Computing environment. In 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014) (pp. 636–641). IEEE.
33.
34.
35.
36.
37.
38.
Choi, G. W., Jo, H. G., Park, H. S., & Jang, D. W. (2020). Application of decision making model for leakage reduction to economic project in water distribution systems. Journal of Ambient Intelligence and Humanized Computing, 1–10. https://​doi.​org/​10.​1007/​s12652-019-01634-2.
39.
40.
41.
42.
43.
44.
45.
Wickremasinghe, B. (2010). Cloud analyst: A cloud-sim-based Tool for modeling and analysis of large Scale Cloud Computing environments. MEDC Project.
46.
Wickremasinghe, B., Calheiros, R. N., & Buyya, R. (2010, April). Cloudanalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 446–452) (Australia). IEEE.
47.
Jain, A., & Kumar, R. (2017). Critical analysis of load balancing strategies for cloud environment. International Journal of Communication Networks and Distributed Systems, 18(3–4), 213–234.CrossRef
48.
49.
Razaq, A., Tianfield, H., Barrie, P., & Yue, H. (2016, July). Service broker based on cloud service description language. In 2016 15th International Symposium on Parallel and Distributed Computing (ISPDC) (pp. 196–201). IEEE.
50.
Gupta, A. (2017). Load balancing in cloud computing. International Journal of Recent Trends in Engineering and Research, 3(3), 260–267.CrossRef
51.
52.
Mulat, W. W., Mohapatra, S. K., Sathpathy, R., & Dhal, S. K. (2022, May). Improving Throttled Load Balancing Algorithm in Cloud Computing. In Proceedings of International Joint Conference on Advances in Computational Intelligence: IJCACI 2021, (pp. 369–377). Singapore: Springer Nature Singapore. https://​doi.​org/​10.​1007/​978-981-19-0332-8_​27.
Metadaten
Titel
Design and Development of Pragmatic Load Balancing Algorithm for Cloud Environment
verfasst von
Tejinder Sharma
R. P. S Bedi
Publikationsdatum
29.05.2024
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2024
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-024-11117-z

Weitere Artikel der Ausgabe 1/2024

Secure Load Balancing in Fog Computing Using improved Tasmanian Devil Optimization Algorithm with Blockchain

SPIS: Signal Processing for Integrated Sensing Technologies Using 6G Networks with Machine Learning Algorithms

  • Open Access
  • Research

A New Secure Data Communication Method Using Wavelet Transform

Trade-Off in Energy Efficiency and Response Performance in Green CRNs: Perspective of Two Sleep Timers and Bursty Traffic

  • Research

Power Allocation with Meta-heuristic Algorithms for Indoor MIMO-NOMA Based VLC Systems

Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks

  • Research