AI-Driven Predictive Scaling for Multi-Cloud Resource Management: Using Adaptive Forecasting, Cost-Optimization, and Auto-Tuning Algorithms

Charan Shankar Kummarapurugu

Abstract: Serverless computing has revolutionized cloud infrastructure by enabling application development without managing underlying servers. However, integrating serverless with multi-cloud environments introduces unique security and scaling challenges. This paper presents an AI-driven predictive scaling approach using three key algorithms: Adaptive Forecasting Algorithm (AFA), Cost-Optimized Resource Allocation Algorithm (CORA), and AI-Based Auto-Tuning Algorithm (AITA). These algorithms address the challenges of workload prediction, resource optimization, and performance tuning. Experimental results demonstrate significant cost reductions and performance improvements compared to conventional methods.

Keywords: Serverless computing, multi-cloud, predictive scaling, AI algorithms, cost optimization, performance tuning

How to Cite?: Charan Shankar Kummarapurugu, "AI-Driven Predictive Scaling for Multi-Cloud Resource Management: Using Adaptive Forecasting, Cost-Optimization, and Auto-Tuning Algorithms", Volume 13 Issue 10, October 2024, International Journal of Science and Research (IJSR), Pages: 1164-1167, https://www.ijsr.net/getabstract.php?paperid=SR241015062841, DOI: https://dx.doi.org/10.21275/SR241015062841