AI-Powered Multi-Cloud Strategies: Balancing Load and Optimizing Costs Through Intelligent Systems

• Author(s): Jeyasri Sekar
• Paper ID: 1704939
• Page: 675-682
• Published Date: 25-07-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 2 August-2023

Abstract

The purpose of this research is to explore and develop AI-powered strategies for managing multi-cloud environments, focusing on optimizing costs and balancing computational loads. The study aims to address the growing complexity of cloud resource management by leveraging intelligent systems capable of making real-time decisions to improve efficiency and reduce operational expenses. Specifically, it seeks to develop and validate models that can dynamically allocate resources across multiple cloud providers, ensuring optimal performance and cost-effectiveness. The research employs a combination of quantitative and qualitative methodologies to achieve its objectives. The primary approach involves the development of machine learning algorithms designed for load balancing and cost optimization in multi-cloud environments. These algorithms are trained using a large dataset obtained from various cloud service providers, which includes metrics on resource utilization, costs, and performance. The study also incorporates a simulation-based approach to test and validate the performance of the proposed strategies under different scenarios. Key techniques include machine learning algorithms, simulation modeling to emulate multi-cloud environments, comparative analysis with traditional methods, and evaluation metrics such as response time, cost savings, resource utilization, and system throughput. The findings of the research demonstrate significant improvements in both load balancing and cost optimization through the use of AI-powered strategies. The AI algorithms achieved a 30% improvement in load distribution across cloud resources compared to traditional methods and a 25% reduction in overall cloud service costs by optimizing resource allocation and minimizing waste. Enhanced system performance was observed with reduced response times and higher resource utilization rates. Additionally, the AI-powered strategies proved effective in scaling resources dynamically in response to varying workloads, ensuring consistent performance. The research concludes that AI-powered strategies significantly enhance the management of multi-cloud environments by balancing loads and optimizing costs. The intelligent systems developed provide a robust solution to the complexities of modern cloud resource management, offering both economic and performance benefits. The study's findings underscore the potential of AI in transforming cloud computing, paving the way for more efficient, cost-effective, and scalable multi-cloud strategies. Future research should focus on further refining these algorithms and exploring their application in diverse cloud environments to fully realize their potential.

Keywords

AI, Multi-Cloud Strategies, Load Balancing, Cost Optimization, Intelligent Systems

Citations

IRE Journals:
Jeyasri Sekar "AI-Powered Multi-Cloud Strategies: Balancing Load and Optimizing Costs Through Intelligent Systems" Iconic Research And Engineering Journals Volume 7 Issue 2 2023 Page 675-682

IEEE:
Jeyasri Sekar "AI-Powered Multi-Cloud Strategies: Balancing Load and Optimizing Costs Through Intelligent Systems" Iconic Research And Engineering Journals, 7(2)