Volume- 12
Issue- 5
Year- 2024
DOI: 10.55524/ijircst.2024.12.5.11 | DOI URL: https://doi.org/10.55524/ijircst.2024.12.5.11 Crossref
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Mrinal Kumar , Yuvaraj Madheswaran
This research paper aims at analysing the application of artificial intelligence and deep learning techniques in the cloud computing paradigm especially in virtualization and containerization. Since cloud computing has been rapidly integrated into organizations, it is necessary to address the problem of resource management. This study evaluates four deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders—across key performance metrics: which are accuracy, precision, recall, and F1 score, for example. The findings suggest that the CNN had the best accuracy of 92% for identifying performance bottlenecks, while the LSTM had the second-best accuracy of 91% for forecasting. The RNN and Autoencoder also had a good performance in terms of predicting resource utilization and detecting abnormal behavior respectively. Consequently, it is suggested that these algorithms can significantly enhance the operational, security, and resource management efficiency of cloud computing and these may be valuable for further research and practical applications.
[1] H. Rehan, "Revolutionizing America's Cloud Computing: The Pivotal Role of AI in Driving Innovation and Security," J. Artif. Intell. Gen. Sci. (JAIGS), vol. 2, no. 1, pp. 239-240, 2024. Available from: https://doi.org/10.60087/jaigs.v2i1.110
[2] S. Mangalampalli, P. K. Sree, S. K. Swain, and G. R. Karri, Cloud Computing and Virtualization, Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation, pp. 13-40, 2023. Available from: https://doi.org/10.1002/9781119905233.ch2
[3] N. Zhou, H. Zhou, and D. Hoppe, "Containerization for high performance computing systems: Survey and prospects," IEEE Trans. Softw. Eng., vol. 49, no. 4, pp. 2722-2740, 2022. Available from: https://doi.org/10.1109/TSE.2022.3229221
[4] O. Bentaleb, A. S. Belloum, A. Sebaa, and A. El-Maouhab, "Containerization technologies: Taxonomies, applications and challenges," J. Supercomput., vol. 78, no. 1, pp. 1144-1181, 2022. Available from: https://doi.org/10.1007/s11227-021-03914-1
[5] M. Abouelyazid and C. Xiang, "Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management," Int. J. Inf. Cybersecurity, vol. 3, no. 1, pp. 1-19, 2019. Available from: https://publications.dlpress.org/index.php/ijic/article/view/92
[6] Y. Liu, D. Lan, Z. Pang, M. Karlsson, and S. Gong, "Performance evaluation of containerization in edge-cloud computing stacks for industrial applications: A client perspective," IEEE Open J. Ind. Electron. Soc., vol. 2, pp. 153-168, 2021. Available from: https://doi.org/10.1109/OJIES.2021.3055901
[7] Y. Liu, D. Lan, Z. Pang, M. Karlsson, and S. Gong, "Performance evaluation of containerization in edge-cloud computing stacks for industrial applications: A client perspective," IEEE Open J. Ind. Electron. Soc., vol. 2, pp. 153-168, 2021. Available from: https://doi.org/10.1109/OJIES.2021.3055901
[8] A. Celesti, D. Mulfari, A. Galletta, M. Fazio, L. Carnevale, and M. Villari, "A study on container virtualization for guarantee quality of service in cloud-of-things," Future Gener. Comput. Syst., vol. 99, pp. 356-364, 2019. Available from: https://doi.org/10.1016/j.future.2019.03.055
[9] S. S. Gill et al., "Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges," Internet of Things, vol. 8, Art. no. 100118, 2019. Available from: https://doi.org/10.1016/j.iot.2019.100118
[10] D. Zhao, M. Mohamed, and H. Ludwig, "Locality-aware scheduling for containers in cloud computing," IEEE Trans. Cloud Comput., vol. 8, no. 2, pp. 635-646, 2018. Available From: https://doi.org/10.1109/TCC.2018.2794344
[11] A. Karteris, E. Tsigkanos, M. Bernou, A. Chatzistylianos, and G. Lentaris, "Towards AI Onboard EO Satellites: Assessment of Virtualization Techniques for Extreme Edge Computing," in Proc. IGARSS 2024 IEEE Int. Geosci. Remote Sens. Symp., 2024, pp. 1727-1732. Available from: https://doi.org/10.1109/IGARSS53475.2024.10642768
[12] F. Al-Doghman et al., "AI-enabled secure microservices in edge computing: Opportunities and challenges," IEEE Trans. Serv. Comput., vol. 16, no. 2, pp. 1485-1504, 2022. Available from: https://doi.org/10.1109/TSC.2022.3155447
[13] B. Thurgood and R. G. Lennon, "Cloud computing with kubernetes cluster elastic scaling," in Proc. 3rd Int. Conf. Future Netw. Distrib. Syst., 2019, pp. 1-7. Available from: https://doi.org/10.1145/3341325.3341995
[14] A. Sapkal and S. S. Kusi, "Evolution of Cloud Computing: Milestones, Innovations, and Adoption Trends," 2024. Available from: https://www.researchgate.net/profile/Leoson-Heisnam-2/publication/379052734_Evolution_of_Cloud_Computing_Milestones_Innovations_and_Adoption_Trends/links/6674e532d21e220d89c509cf/Evolution-of-Cloud-Computing-Milestones-Innovations-and-Adoption-Trends.pdf
[15] Y. Zhang, J. Wang, and T. Li, "Machine learning-based resource management in multi-cloud environments," Journal of Cloud Computing, vol. 10, no. 2, pp. 45-59, 2022. Available from https://doi.org/10.1007/s13203-022-01234
[16] H. Chen and X. Liu, "AI-driven container orchestration systems: Scheduling mechanisms for adaptable workloads," International Journal of Cloud Applications, vol. 15, no. 3, pp. 98-112, 2023. Available from: https://doi.org/10.1109/IJCA.2023.672834.
[17] A. Patel, S. Singh, and P. Gupta, "Anomaly detection techniques in virtualized environments using AI," IEEE Transactions on Network Security, vol. 12, no. 4, pp. 234-250, 2023. Available from: https://doi.org/10.1109/TNS.2023.8742389.
[18] R. Kumar and M. Sharma, "Using AI to monitor container environments: Developing KPIs for real-time performance tracking," Proceedings of the 2024 IEEE Cloud Conference, 2024, pp. 89-97. Available from: https://doi.org/10.1109/CLOUD.2024.981238.
[19] Y. Lin, H. Zhao, and P. Wang, "Enhancing microservices architecture with AI-based analytics for real-time scaling," IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 123-136, 2022. Available from: https://doi.org/ 10.1109/TCC.2022.871245.
[20] J. Smith, L. Roberts, and F. Cheng, "Edge computing and AI integration: Enhancing real-time applications in cloud environments," IEEE Transactions on Edge Computing, vol. 9, no. 3, pp. 201-210, 2023. Available from https://doi.org/10.1109/TEC.2023.891238
School of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, India
No. of Downloads: 9 | No. of Views: 487
Mohankumar T P, D. Ramesh.
November 2024 - Vol 12, Issue 6
Anugrah Shailay, Swati Jadon, Ankush Sharma.
November 2024 - Vol 12, Issue 6
Mrinal Kumar, Mayur Prakashrao Gore.
November 2024 - Vol 12, Issue 6