International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Ai-driven Optimization of Cost, Performance, And Resource Allocation in Multi-Tenant Cloud Data Platforms

Author(s) Rambabu Bandam, Senthil Raj Subramaniam
Country United States
Abstract As cloud computing emerges as one of the influential characteristics in the IT structure of the modern world, cost, performance, and resource management in multi-tenant cloud data structures are increasingly vital for both cloud service providers and enterprises. Multi-tenant environments are typically already complicated due to the issues associated with shared resources because the environment must serve numerous clients with diverse needs and requirements efficiently. This paper provides a study of the issues in multi-tenant cloud platforms and presents an optimal AI-based solution to deal with these issues by reducing cost, enhancing performance metric and dynamic resource management.
The presented framework includes state-of-art AI methods and tools such as predictive analytics for the workload prediction, clustering for tenants’ behavior analysis, and reinforcement learning for an agile and real-time resource allocation. These concepts are then used for anticipation of system workloads and the distribution of resources depending on the tenant’s requirements and needs and the reduction of operational costs at the same time without an impact on efficiency. Also, it integrates a multicriteria optimization that has consideration of costs, performance and resources consumption in accordance with standards of specific tenants.
To illustrate the implementation of the proposed AI-driven optimization framework in a general multi-tenant cloud system for different application workloads, an example case is explained as follows. In this case, certain benefits of the proposed method include a notable decrease in the overall operating cost by 22 %, increase in efficiency by 35 %, and improper resource utilization compared to other optimization techniques. These could only confirm the efficiency of the AI strategies to manage cloud resources and further confirm that AI can make a huge difference in future cloud systems management.
Thus, this research also discusses the future of AI in enhancing cloud optimization with the identified areas for future work including federated learning in distributed cloud environment and application of explainable AI in enhancing cloud computing. In conclusion, this study adds to the existing literature concerning the application of AI in cloud computing as well as offering recommendations that CSPs may use to improve their resource management techniques with respect to scalability and flexibility.
Keywords Ai-driven Cloud Optimization, Multi-tenant Cloud Platforms, Resource Allocation in Cloud Computing, Cost-performance Optimization, Reinforcement Learning in Cloud Systems
Field Engineering
Published In Volume 5, Issue 2, March-April 2023
Published On 2023-04-05
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E-ISSN 2582-2160
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