Auto Scaling Infrastructure with Monitoring Tools using Linux Server on Cloud

Cloud computing is the term that has gained widespread usage over these last few years. Due to the rapid increase in the use of information in the digital age of the 21st century, it is increasingly becoming a more attractive option for individuals and organizations to manage all their essential data, projects, and collaborations, rather than relying solely on in-house computers. The user's requirement for hardware and software is reduced via cloud computing. The interface software of cloud computing systems, typically as simple as a web browser, is the only thing the user must operate, and the Cloud network handles the rest. To decrease operational costs, both business and government organizations are adopting cloud computing, seeking a flexible and adaptable solution for the supply and delivery of their product services. Microservices and decoupled apps are becoming more popular. These container-based architectures make it easier to build sophisticated SaaS apps quickly, but managing and creating microservices can be a daunting task. Managing and creating microservices that involve a wide range of diverse functions, including handling and storing information, and performing predictive and prescriptive analysis, can be a challenging undertaking. Establishing auto scaling infrastructure on doud can be challenging due to several reasons, some of which are: understanding the application architecture, setting up monitoring, scaling policies, cost optimization and implementation complexity. Server farms include the tremendous and heterogeneous virtualized frameworks, which are continually extending and broadening after sometime are the essential starting point for registering specialized organizations. These solutions also need to be integrated into existing systems while adhering to Quality of Service (QoS) requirements. The principal objective of this work is to propose an on-premise design to leverage Kubernetes and Docker containers to improve the quality of service based on resource usage and Service Level Objectives (SLOs). The Prometheus Administrator set up is used to perform namespace checking. Normally, doud providers enable their own monitoring tools (like CloudWatch) for monitoring CPU, storage and network usage, service component, however these tools cannot monitor the service component. Additionally, the advancements have restricted the capacity to follow QoS highlights at the application level (like security and execution) since the main focus will be dedicated towards the equipment assets. These types of node-level monitoring make it difficult to scale requests and deploy pods to match the demand. Infrastructure monitoring should enable runtime changes to monitor the requirements or metric operationalization should be done on those criteria without modifying the underlying infrastructure.