PAPER ID:IJIM/V. 10 (1)/50-53 /7

AUTHOR: Pushpendra Yadav

TITLE : LOAD BALANCING AND AUTO-SCALING IN CLOUD COMPUTING

ABSTRACT: Cloud computing has revolutionized the deployment and scalability of applications by offering flexible, on-demand access to computing resources. Two critical components that enable high availability and cost-efficient operations in cloud environments are load balancing and auto-scaling. This paper explores how modern DevOps practices—including continuous integration/deployment (CI/CD), infrastructure as code (IaC), and monitoring—enhance the implementation of these mechanisms. We analyze and compare load balancing and auto-scaling solutions across major cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), while also addressing their integration with container orchestration platforms like Kubernetes. Furthermore, we highlight how tools like Terraform, Prometheus, and Kubernetes Horizontal Pod Autoscaler (HPA) are utilized to dynamically adjust resources based on real-time metrics. The study emphasizes the role of DevOps in automating scalability and improving application resilience, and concludes with recommendations for future research in intelligent scaling strategies

Keywords : Cloud computing, Auto scaling, Load balancing.

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