International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Allocation of Cloud Computing in Machine Learning

Sunita K. TotadeDepartment of MCA, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, IndiaDhiraj BhuyarMCA-II, Department of MCA, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, IndiaShraddha A. MathaneMCA-II, Department of MCA, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, IndiaRutuja PandaoMCA-II, Department of MCA, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, India

Vol 8 No 10 (2024): Volume 8, Issue 10, October 2024 | Pages: 263-265

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 30-10-2024

doi Logo doi.org/10.47001/IRJIET/2024.810036

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Abstract

Asset allotment in barters could be a challenging issue for cloud computing. In any case, the asset allotment issue is NP-hard and cannot be fathomed in polynomial time. The existing ponders basically utilize surmised calculations such as PTAS or heuristic calculations to decide a doable arrangement; in any case, these calculations have the impediments of moo computational proficiency or moo designate exactness. In this paper, we utilize the classification of machine learning to demonstrate and analyze the multi-dimensional cloud asset allotment issue and propose two asset allotment expectation calculations based on straight and calculated relapses. By learning a small-scale preparing set, the expectation show can ensure that the social welfare, assignment precision, and asset utilization within the doable arrangement are exceptionally near to those of the ideal assignment arrangement. The exploratory comes about appear that the proposed plot has great impact on asset assignment in cloud computing.

Keywords

Cloud computing, Resource Allocation, Machine learning, Linear regression, Logistic regression, Decision Tree, Cloud Efficiency, Resource Allocation, Load Balancing, Traffic Load, Cost of Service, CoS, Long-Short Term Memory, LSTM, Cloud Data Centre, CDC


Citation of this Article

Sunita K. Totade, Dhiraj Bhuyar, Shraddha A. Mathane, & Rutuja Pandao. (2024). Allocation of Cloud Computing in Machine Learning. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(10), 263-265. Article DOI https://doi.org/10.47001/IRJIET/2024.810036

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References
  1. https://www.google.com/search?q=machine+learning&sca_esv=b3ab8c9452317ae8&rlz=1C1RLNS_enIN1077IN1079&udm=2&biw=1517&bih=712&sxsrf=ADLYWII6txywI0Vogl8wtO8AGnpXuOLMJw%3A1729564590103&ei=rg8XZ7T5Bfif4-EPhPGQgQ8&oq=machine&gs_
  2. Hasan, Md., E, B., Almamun, Md. and K, S. (2018). An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment.EAI Endorsed Transactions on Cloud Systems, 0(0), p.165501. doi:https://doi.org/10.4108/eai.13-7-2018.165501.
  3. Khan, T., Tian, W. and Buyya, R. (2021). Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions Resource Provisioning VM consolidation Scheduling Train Models ML models Data Collection Validate Models Deploy Models Resource Management System IaaS PaaS Web Applications Scientific Applications Enterprise Applications SaaS Users. [online] Available at: https://arxiv.org/pdf/2105.05079.pdf.
  4. Malik, S., Tahir, M., Sardaraz, M. and Alourani, A. (2022). A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques. Applied Sciences, 12(4), p.2160. doi:https://doi.org/10.3390/app12042160.
  5. Manimegalai, R. and Durai, U. (2021). Optimizing Resource Allocation In Cloud Computing. https://www.webology.org/data-cms/articles/20220927064958pmwebology%2018%20(3)%20-%20132.pdf, [online] 18(3), p.1776. Available at: https://www.webology.org/data-cms/articles/20220927064958pmwebology%2018%20(3)%20-%20132.pdf [Accessed 11 Mar. 2024].
  6. Oshawa, M., Douglas, O., Osamor, J., and Jackie, R. (2022). Improving cloud efficiency through optimized resource allocation technique for load balancing using LSTM machine learning algorithm. Journal of Cloud Computing, 11(1). https://doi.org/10.1186/s13677-022-00362-x.
  7. Powell, M. (2023). Optimizing Resource Allocation in Cloud Computing Environments Using Machine Learning. Journal of Computer Science & Systems Biology, [online] 16(2). doi: https://doi.org/10.37421/0974-7230.2023.16.457.
  8. Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J. and Wan, W. (2022). Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management. [online] arXiv.org. doi: https://doi.org/10.48550/arXiv.
  9. Dang, L.M.; Piran, M.; Han, D.; Min, K.; Moon, H. A Survey on Internet of Things and Cloud Computing for Healthcare. Electronics 2019, 8, 768.
  10. Srinivasamurthy, S.; Liu, D. Survey on Cloud Computing Security. 2020. Available online: https://www.semanticscholar.org/ (accessed on 19 July 2020).
  11. Stefan, H.; Liakat, M. Cloud Computing Security Threats And Solutions. J. Cloud Comput. 2015, 4, 1.
  12. Fauzi, C.; Azila, A.; Noraziah, A.; Tutut, H.; Noriyani, Z. On Cloud Computing Security Issues. Intell. Inf. Database Syst. Lect. Notes Comput. Sci. 2012, 7197, 560–569.
  13. Palumbo, F.; Aceto, G.; Botta, A.; Ciuonzo, D.; Persico, V.; Pescapé, A. Characterizing Cloud-to-user Latency as perceived by AWS and Azure Users spread over the Globe. In Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, 7–11 December 2019; pp. 1–6
  14. Li, K.; Gibson, C.; Ho, D.; Zhou, Q.; Kim, J.; Buhisi, O.; Gerber, M., Assessment of machine learning algorithms in cloud computing frameworks. In Proceedings of the IEEE Systems and Information Engineering Design Symposium, Charlottesville, VA, USA, 26 April 2013; pp. 98–103.
  15. Callara, M.; Wira, P., User Behavior Analysis with Machine Learning Techniques in Cloud Computing Architectures. In Proceedings of the 2018 International Conference on Applied Smart Systems, Médéa, Algeria, 24–25 November 2018; pp. 1–6.
  16. Singh, S.; Jeong, Y.-S.; Park, J. A Survey on Cloud Computing Security: Issues, Threats, and Solutions. J. Netw. Comput. Appl. 2016, 75, 200–222.
  17. Kyriakos N. Agavanakis, George. E. Karpetas, Michael Taylor, Evangelia Pappa, Christos M. Michail, John Filos, Varvara Trachana, Lamprini Kontopoulou; Practical machine learning based on cloud computing resources. AIP Conference Proceedings 17 July 2019.

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