Artificial Intelligence-Based Cloud Planning and Migration to Cut the Cost of Cloud Sasibhushan Rao Chanthati
DOI:
https://doi.org/10.54536/ajsts.v3i2.3210Keywords:
Artificial Intelligence, Cloud Planning, Cost of Cloud, Cloud MitigationAbstract
The paper titled “Artificial Intelligence-Based Cloud Planning and Migration to Cut the Cost of Cloud” aims to examine how AI can be implemented to improve cloud planning and migration in a bid to reduce their costs. The proposal is concerned with the utilization of multiple AI techniques, such as machine learning models, natural language processing, and reinforcement learning, to manage the migration process in the cloud. In incorporating AI within the transitions, the paper establishes how organizations improve productivity, stability, and security during Cloud transitions. It provides a detailed pseudocode of the scenario, making the content sufficiently intelligible to the IT professionals who wish to implement these AI algorithms. In this regard, this paper helps to fill the gap that has been demonstrated in the current literature regarding the link between theoretical uses of AI and its application in cloud migration towards enhancing the deployment efficacy and cost-efficiency of cloud services. The article was first completed in 2021 and later I have modified the article with latest updates till date 2024.
Downloads
References
Alhilali, A. H., & Montazerolghaem, A. (2023). Artificial intelligence based load balancing in SDN: A comprehensive survey. Internet of Things. Advance online publication. https://doi.org/10.1016/j.iot.2023.100814
Bermejo, B., & Juiz, C. (2023). Improving cloud/edge sustainability through artificial intelligence: A systematic review. Journal of Parallel and Distributed Computing, 176, 41-54. https://doi.org/10.1016/j.jpdc.2023.02.006
Bian, Y. J., Xie, L., & Li, J. Q. (2022). Research on influencing factors of artificial intelligence multi-cloud scheduling applied talent training based on DEMATEL-TAISM. Journal of Cloud Computing, 11(1), 35. https://doi.org/10.1186/s13677-022-00315-4
Dhaya, R., & Kanthavel, R. (2022). IoE based private multi-data center cloud architecture framework. Computers and Electrical Engineering, 100, 107933.
Elmagzoub, M. A., Syed, D., Shaikh, A., Islam, N., Alghamdi, A., & Rizwan, S. (2021). A survey of swarm intelligence based load balancing techniques in cloud computing environment. Electronics, 10(21), 2718. https://doi.org/10.3390/electronics10212718
Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., ... Pervaiz, H. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118. https://doi.org/10.1016/j.iot.2019.100118
Hassan, M. B., Ahmed, E. S., & Saeed, R. A. (2024). Green machine learning approaches for cloud-based communications. In M. B. Hassan, E. S. Ahmed, & R. A. Saeed (Eds.), Green Machine Learning Protocols for Future Communication Networks 2024 (pp. 129-160). CRC Press. https://doi.org/10.1201/9781003230427-5
Hemmati, A., Raoufi, P., & Rahmani, A. M. (2024). Edge artificial intelligence for big data: A systematic review. Neural Computing and Applications. Advance online publication. https://doi.org/10.1007/s00521-024-09723-w
Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021). Task scheduling in cloud computing based on meta-heuristics: Review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation, 62, 100841.
Janet, A., & Al-Turjman, F. (2023). The impact of cloud computing on the development of artificial intelligence technologies in e-commerce. NEU Journal for Artificial Intelligence and Internet of Things, 2(3).
Joloudari, J. H., Alizadehsani, R., Nodehi, I., Mojrian, S., Fazl, F., Shirkharkolaie, S. K., Kabir, H. D., Tan, R. S., & Acharya, U. R. (2022, March 28). Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review. arXiv preprint arXiv:2203.12315. https://doi.org/10.13140/RG.2.2.32857.39522
Joloudari, J. H., Mojrian, S., Saadatfar, H., Nodehi, I., Fazl, F., Alizadehsani, R., Kabir, H. M., Tan, R. S., & Acharya, U. R. (2022, March 23). The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms. arXiv preprint arXiv:2203.12315. https://doi.org/10.1007/s11042-024-18123-0
Junaid, M., Shaikh, A., Hassan, M. U., Alghamdi, A., Rajab, K., Al Reshan, M. S., & Alkinani, M. (2021). Smart agriculture cloud using AI based techniques. Energies, 14(16), 5129. https://doi.org/10.3390/en14165129
Kanungo, S. (2024). AI-driven resource management strategies for cloud computing systems, services, and applications. World Journal of Advanced Engineering Technology and Sciences, 11(2), 559-566. https://doi.org/10.30574/wjaets.2024.11.2.0137
Kumar, Y., Kaul, S., & Hu, Y. C. (2022, December 1). Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey. Sustainable Computing: Informatics and Systems, 36, 100780. https://doi.org/10.1016/j.suscom.2022.100780
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.52783/tjjpt.v44.i3.2466
Liang, X., Haiping, L., Liu, J., & Lin, L. (2021). Reform of English interactive teaching mode based on cloud computing artificial intelligence – A practice analysis. Journal of Intelligent & Fuzzy Systems, 40(2), 3617-3629. https://doi.org/10.1177/16878132221122770
Matthew, O. O., Olabanji, D. O., & Fitch, T. (2023, April 30). Cloud-native architecture Portability Framework Validation and Implementation using Expert System. International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE), 12(4).
Mohanty, S. N., Potluri, S., Prakash, V. B., Srinath, B., & Manjunath, B. (2021, July 19). Cloud security concepts, threats and solutions: Artificial intelligence based Approach. Cloud Security: Techniques and Applications, 1, 1.
Nagasundaram, S., Bobinath, B. N., Shedthi, A., Rajalakshmi, K., & Humnekar, T. D. (2023). Analysis of the requirement and artificial intelligence-based resource management system in cloud. In 9th International Conference on Advanced Computing and Communication Systems (ICACCS 2023) (Vol. 1, pp. 2516-2525). IEEE. https://doi.org/10.1109/icaccs57279.2023.10112940
Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023, January 28). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13(3), 1681. https://doi.org/10.3390/app13031681
Nayak, A., Patnaik, A., Satpathy, I., & Patnaik, B. C. (2024). Data storage and transmission security in the cloud: The artificial intelligence (AI) edge. In A. Nayak, A. Patnaik, I. Satpathy, & B. C. Patnaik (Eds.), Improving Security, Privacy, and Trust in Cloud Computing 2024 (pp. 194-212). IGI Global. https://doi.org/10.4018/979-8-3693-1431-9.ch009
Olabanji, D., Fitch, T., & Matthew, O. (2023). Cloud-native architecture Portability Framework Validation and Implementation using Expert System. International Journal of Advanced Studies in Computer Science and Engineering, 12(4), 1-4.
Reátegui, J. L., & Herrera, P. C. (2021). Artificial intelligence in the assessment process of MOOCs using a cloud-computing ecosystem. In 2021 IEEE International Conference on Engineering, Technology & Education (TALE) (pp. 487-493). IEEE. https://doi.org/10.1109/TALE51101.2021.9635760
Rommer, D. (2020). Artificial intelligence-based decision-making algorithms, industrial big data, and smart connected sensors in cloud-based cyber-physical manufacturing systems. Economics, Management, and Financial Markets, 15(1), 40-46.
Sharma, A., Verma, A., Malviya, R., & Sekar, M. (2023, March 10). Artificial-Intelligence-Based Cloud Computing Techniques for Patient Data Management. In A. Sharma, A. Verma, R. Malviya, & M. Sekar (Eds.), Artificial Intelligence for Health 4.0: Challenges and Applications (pp. 149-173). River Publishers. https://doi.org/10.1201/9781003373582-6
Soni, D., & Kumar, N. (Eds.). (2023, March 16). Artificial intelligence in cloud computing. In Artificial Intelligence in Cyber-Physical Systems (pp. 51-73). CRC Press.
Thanka, M. R., Uma Maheswari, P., & Edwin, E. B. (2019, September). An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment. Cluster Computing, 22(Suppl 5), 10905-10913.
Tuli, S., Gill, S. S., Xu, M., Garraghan, P., Bahsoon, R., Dustdar, S., Sakellariou, R., Rana, O., Buyya, R., Casale, G., & Jennings, N. R. (2022, February 1). HUNTER: AI based holistic resource management for sustainable cloud computing. Journal of Systems and Software, 184, 111124. https://doi.org/10.1016/j.jss.2021.111124
Vähäkainu, P., Lehto, M., Kariluoto, A., & Ojalainen, A. (2020). Artificial intelligence in protecting smart building’s cloud service infrastructure from cyberattacks. In Cyber Defence in the Age of AI, Smart Societies and Augmented Humanity (pp. 289-315).
Wang, W., Zhang, Y., Liu, Q., Wang, T., & Jia, W. (2024, April 3). Edge-Intelligence-Based Computation Offloading Technology for Distributed Internet of Unmanned Aerial Vehicles. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2024.3383896
Yahia, H. S., Zeebaree, S. R., Sadeeq, M. A., Salim, N. O., Kak, S. F., Adel, A. Z., Salih, A. A., & Hussein, H. A. (2021, May). Comprehensive survey for cloud computing based nature-inspired algorithms optimization scheduling. Asian Journal of Research in Computer Science, 8(2), 1-6.
Zhang, Y., & Yuen, K. V. (2022, September). Review of artificial intelligence-based bridge damage detection. Advances in Mechanical Engineering, 14(9), 16878132221122770. https://doi.org/10.1177/16878132221122770
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Sasibhushan Rao Chanthati
This work is licensed under a Creative Commons Attribution 4.0 International License.
