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Autoscaling Enabled Intelligent Load Balancing In Cloud Computing

Year: 2025

Type: article

Abstract: Cloud computing environments demand robust and intelligent task management to ensure scalability, efficiency, and cost-effectiveness. This paper introduces an Autoscaling Enabled Intelligent Load Balancing System designed for dynamic cloud environments. The system is divided into four key modules: task classification using XGBoost, resource prediction via XGBoost Regression, optimal load balancing using Particle Swarm Optimization (PSO), and adaptive autoscaling with fuzzy logic. Evaluation of each module demonstrated significant improvements in task distribution accuracy, resource utilization, and system stability. The integration of machine learning and heuristic algorithms ensures a dynamic, responsive, and energy-efficient cloud environment.

Source: International Journal for Research in Applied Science and Engineering Technology

DOI: 10.22214/ijraset.2025.69216

Author: Shivam Kumar

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4409625904

FWCI:

Cites: 1

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Related to: 10