Skip to main content
Springer Nature Link
Log in

Probabilistic Optimized Kernel Naive Bayesian Cloud Resource Allocation System

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Cloud service providers offer diverse utilities to the consumer on their heterogenous requirements which may fit on different levels of performance measurements such as costs, quality of service, and accuracy, etc. Cloud is enriched with unlimited resources along with many advanced features such as scalability, robustness which increased the cloud demand in recent years. The selection of appropriate resources is a challenging task that can minimize the cost, and maximize the resource utilization and consumer experience. We have proposed the optimized kernel naive bayes cloud service selection model (OKNB) which works on the concept of maximum probability. The cloud resource bundle with maximum probability is chosen as a predicted cloud resource. Our proposed model achieves 88.76%, 88.45%, and 93.65% accuracy on response time, CPU utilization, and memory utilization models which is 3.15 and 8.04% higher than the state of the art models on response time and memory utilization models respectively. The proposed model yields 16.83 and 29.175 s lower waiting time in standard deviation and mean value compare to the GMP-SVM model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of data and materials

Available on [24].

References

  1. Garg, S. K., Versteeg, S., & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems, 29(4), 1012–23.

    Article  Google Scholar 

  2. Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents. Computer Networks, 179, 107340.

    Article  Google Scholar 

  3. Makhlouf, R. (2020). Cloudy transaction costs: A dive into cloud computing economics. Journal of Cloud Computing, 9(1), 1.

    Article  MathSciNet  Google Scholar 

  4. Kumar, J., Singh, A. K., & Buyya, R. (2020). Ensemble learning based predictive framework for virtual machine resource request prediction. Neurocomputing, 397, 20–30.

    Article  Google Scholar 

  5. Chauhan, N., Agarwal, R., Garg, K., & Choudhury, T. (2020). Redundant Iaas cloud selection with consideration of multi criteria decision analysis. In Procedia Computer Science, 167, 1325–1333.

  6. Djiroun, R., Guessoum, M. A., Boukhalfa, K., & Benkhelifa, E. (2017). A novel cloud services recommendation system based on automatic learning techniques. In 2017 International conference on new trends in computing sciences (ICTCS) (pp. 42–49). IEEE.

  7. Wen, Z., Shi, J., He, B., Chen, J., & Chen, Y. (2018). Efficient multi-class probabilistic SVMs on GPUs. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1693–706.

    Article  Google Scholar 

  8. Zain, T., Aslam, M., Imran, M. R., & Martinez-Enriquez, A. M. (2014). Cloud service recommender system using clustering. In 2014 11th international conference on electrical engineering, computing science and automatic control (CCE) (pp. 1–6). IEEE.

  9. Md, A. Q., Varadarajan, V., & Mandal, K. (2019). Efficient algorithm for identification and cache based discovery of cloud services. Mobile Networks and Applications, 24(4), 1181–97.

    Article  Google Scholar 

  10. Idrissi, A., & Abourezq, M. (2014). Skyline in cloud computing. Journal of Theoretical and Applied Information Technology, 60(3), 637–648.

    Google Scholar 

  11. Jules, O., Hafid, A., & Serhani, M. A. (2014). Bayesian network, and probabilistic ontology driven trust model for sla management of cloud services. In 2014 IEEE 3rd international conference on cloud networking (CloudNet) (pp. 77–83). IEEE.

  12. Mohamed, A., Mai, M., & Victor, C. (2018). NMCDA: A framework for evaluating cloud computing services. Future Generation Computer Systems, 86, 12–29.

    Article  Google Scholar 

  13. Paunović, M., Ralević, N. M., Gajović, V., Mladenović Vojinović, B., & Milutinović, O. (2018). Two-stage fuzzy logic model for cloud service supplier selection and evaluation. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/7283127

  14. Aveek, B., & Sanchitha, G. (2018). Implementing fuzzy TOPSIS in cloud type and service provider selection. Advances in Fuzzy Systems, 2018, 1–12.

    Google Scholar 

  15. Sun, L., Ma, J., Zhang, Y., Dong, H., & Hussain, F. K. (2016). Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection. Future Generation Computer Systems., 57, 42–55.

    Article  Google Scholar 

  16. Kontarinis, A., Kantere, V., & Koziris, N. (2016). Cloud resource allocation from the user perspective: A bare-bones reinforcement learning approach. In International conference on web information systems engineering (pp. 457–469). Springer, Cham.

  17. Cheng, M., Li, J., & Nazarian, S. (2018). DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In 2018 23rd Asia and South pacific design automation conference (ASP-DAC) (pp. 129–134). IEEE.

  18. Ye, D., Zhang, M., & Yang, Y. A. (2015). Multi-agent framework for packet routing in wireless sensor networks. Sensors, 15(5), 10026–10047.

    Article  Google Scholar 

  19. Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V., Samdanis, K., & Costa-Perez, X. (2017). Optimising 5G infrastructure markets: The business of network slicing. In IEEE INFOCOM 2017-IEEE conference on computer communications (pp. 1–9). IEEE.

  20. Al-faifi, A. M., Song, B., Hassan, M. M., Alamri, A., & Gumaei, A. (2018). Performance prediction model for cloud service selection from smart data. Future Generation Computer Systems, 85, 97–106.

    Article  Google Scholar 

  21. Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.

    Article  Google Scholar 

  22. Shaikh, T. A., & Ali, R. (2020). An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm. Informatics in Medicine Unlocked, 21, 100408.

    Article  Google Scholar 

  23. Rohmer, J., & Gehl, P. (2020). Sensitivity analysis of Bayesian networks to parameters of the conditional probability model using a Beta regression approach. Expert Systems with Applications, 145, 113130.

    Article  Google Scholar 

  24. Al-Faifi, A. M., Song, B., Hassan, M. M., Alamri, A., & Gumaei, A. (2018). Data on performance prediction for cloud service selection. Data in Brief, 20, 1039–1043.

    Article  Google Scholar 

  25. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., & Buyya, R. (2011). CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software Practice and Experience, 41(1), 23–50.

    Article  Google Scholar 

Download references

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. KIET Group of Institutions, Delhi-NCR, Ghaziabad, India

    Naveen Chauhan

  2. G. L. Bajaj Institute of Technology and Management, Greater Noida, India

    Rajeev Agrawal

Authors
  1. Naveen Chauhan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rajeev Agrawal
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Naveen Chauhan: Investigation, Proposed Framework designing, Conceptualization, Writing-original draft, Result simulation, Result compilation, Validation. Rajeev Agrawal: Supervision, Conceptualization, Result compilation, Quality check.

Corresponding author

Correspondence to Naveen Chauhan.

Ethics declarations

Conflict of interest

The authors declare that they have no confict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chauhan, N., Agrawal, R. Probabilistic Optimized Kernel Naive Bayesian Cloud Resource Allocation System. Wireless Pers Commun 124, 2853–2872 (2022). https://doi.org/10.1007/s11277-022-09493-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09493-5

Keywords