Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency
Keywords:
Cloud transformation, artificial intelligence, infrastructure managementAbstract
In the rapidly evolving landscape of mobile product development, cloud transformation has emerged as a pivotal strategy for enhancing operational efficiencies, optimizing resource utilization, and ensuring scalability. The integration of artificial intelligence (AI) into cloud infrastructure management presents a transformative opportunity to automate complex processes that are traditionally labor-intensive and prone to human error. This research paper explores the multifaceted role of AI in automating infrastructure management, scalability, and cost efficiency within the context of cloud transformation for mobile products.
The study begins by delineating the foundational concepts of cloud computing and mobile product architecture, emphasizing the significance of a robust cloud infrastructure in supporting mobile applications' performance and reliability. With the increasing demand for mobile applications to scale seamlessly, organizations face the challenge of maintaining performance standards while managing operational costs. Herein lies the relevance of AI, which offers advanced methodologies to analyze, predict, and adapt infrastructure requirements dynamically.
AI techniques such as machine learning (ML) and natural language processing (NLP) are employed to enhance predictive analytics capabilities, enabling organizations to forecast infrastructure demands based on usage patterns and trends. This predictive approach not only facilitates proactive resource allocation but also minimizes downtime, thereby improving user experience. Furthermore, the implementation of AI-driven automation in cloud management significantly reduces the manual overhead associated with routine tasks such as provisioning, monitoring, and scaling resources.
The paper also investigates various AI algorithms that contribute to cost efficiency through optimized resource management. By leveraging AI-driven insights, organizations can identify underutilized resources and reallocate them effectively, ensuring that cloud expenditures are aligned with actual needs. This leads to a more sustainable operational model where resources are utilized more efficiently, reducing waste and lowering costs.
Moreover, this research highlights case studies showcasing successful AI implementations in cloud transformation for mobile products. These case studies demonstrate how AI has been instrumental in enhancing operational agility, accelerating time-to-market for mobile applications, and fostering innovation. For instance, the use of AI in workload management has resulted in significant performance enhancements and resource savings for leading tech companies.
In addition to the benefits, the study addresses the challenges associated with integrating AI into existing cloud infrastructures. Potential barriers such as data privacy concerns, the need for skilled personnel, and the complexities of algorithmic transparency are critically analyzed. Recommendations for overcoming these challenges are provided, emphasizing the importance of establishing a robust governance framework and investing in AI literacy among staff.
Finally, the paper posits future directions for research and practice in the intersection of AI and cloud transformation. As mobile products continue to proliferate, the demand for intelligent cloud solutions will only intensify. The research advocates for ongoing exploration of advanced AI techniques, including deep learning and reinforcement learning, to further enhance infrastructure automation and scalability.
References
Thuraka, Bharadwaj, et al. "Leveraging artificial intelligence and strategic management for success in inter/national projects in US and beyond." Journal of Engineering Research and Reports 26.8 (2024): 49-59.
Pal, Dheeraj Kumar Dukhiram, et al. "AIOps: Integrating AI and Machine Learning into IT Operations." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 288-311.
El-Hassan, Amina. "Transparency in Medicare Broker Commissions: Implications for Consumer Costs and Enrollment Decisions." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 219-237.
Kumar, Charan, and Eduardo Vargas. "Medicare Broker Commissions and Their Effect on Enrollment Stability: A Study on Churn Rates and Consumer Retention." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 198-218.
Siddiqui, Ayesha, and Laila Boukhalfa. "Streamlining Healthcare Claims Processing Through Automation: Reducing Costs and Improving Administrative Workflows." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 602-624.
Thota, Deepak, and Nina Popescu. "The Economic Ripple Effect of AI-Powered Claims Processing in Healthcare: Transforming Costs and Productivity." Australian Journal of Machine Learning Research & Applications 3.2 (2023): 516-536.
J. Singh, “Combining Machine Learning and RAG Models for Enhanced Data Retrieval: Applications in Search Engines, Enterprise Data Systems, and Recommendations ”, J. Computational Intel. & Robotics, vol. 3, no. 1, pp. 163–204, Mar. 2023
Tamanampudi, Venkata Mohit. "Deep Learning Models for Continuous Feedback Loops in DevOps: Enhancing Release Cycles with AI-Powered Insights and Analytics." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 425-463.
Ahmad, Tanzeem, et al. "Explainable AI: Interpreting Deep Learning Models for Decision Support." Advances in Deep Learning Techniques 4.1 (2024): 80-108.
Kodete, Chandra Shikhi, et al. "Determining the efficacy of machine learning strategies in quelling cyber security threats: Evidence from selected literatures." Asian Journal of Research in Computer Science 17.8 (2024): 24-33.
Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.
A. Younge, G. Von Laszewski, L. Wang, S. Lopez-Alarcon, and W. Carithers, "Efficient resource management for cloud computing environments," in Proceedings of the 2010 International Conference on Green Computing, 2010, pp. 357-364, doi: 10.1109/GREENCOMP.2010.5598310.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
