Implementation of Auto Scaling Mechanism for Serverless Computing in Cloud Environment

Abstract

In order to effectively manage varying workloads, serverless computing in cloud settings requires dynamic resource allocation, which is addressed by the deployment of an auto-scaling system. Serverless computing is an affordable option for cloud-based apps since users are only charged for the resources that are really used. However, complex techniques are needed to dynamically scale resources up or down in response to real-time traffic patterns in order to manage the scalability of serverless functions, particularly when demand is variable. In order to optimize resource allocation in serverless systems, this study suggests an adaptive auto-scaling method that makes use of real-time monitoring and predictive analytics. In order to minimize idle resources and guarantee high availability, the suggested system dynamically modifies the number of function instances. Workload monitoring, performance metrics analysis, and the use of machine learning models to forecast future demand and optimize scaling choices are important elements of the mechanism. The system is made to manage different computing job levels while striking a balance between cost effectiveness and responsiveness. When compared to static scaling models, the outcomes of putting this auto-scaling mechanism into practice show better responsiveness, cost savings, and resource usage. Furthermore, the suggested method easily adjusts to the evolving needs of the application, which makes it appropriate for a variety of cloud-based applications, ranging from tiny services to major business solutions. This study demonstrates how intelligent scaling can improve serverless computing's adaptability and effectiveness in cloud settings.