Monitoring performance computing environments and autoscaling using AI

Abstract

The fluctuating workloads and erratic demands faced by modern enterprises make efficient use of computer resources necessary. Cloud computing autoscaling provides an answer by allowing apps to independently modify their ability to operate in response to fluctuating demand. In order to overcome the difficulties in monitoring as well as autoscaling, this article investigates the use of AI-powered algorithms, taking into account variables including memory needs, network traffic, CPU use, and custom metrics. AI-driven models provide several benefits, such as better use of resources, scalability, dependability, lower maintenance costs, continuous availability, affordability, and easier computing environment management. On the other hand, there are several significant disadvantages, including setup complexity, possible performance deterioration, uneven performance, security issues, and higher expenses. This study assesses the contribution of AI to the resolution of issues encountered by alternative methodologies by comparing AI-powered techniques with other conventional methods. This paper illustrates the advantages of AI-driven solutions in several areas, including CPU consumption, memory usage, throughput, and reaction time, via experimental assessment and thorough analysis. Additionally, the study points out a number of areas that still need work in order to maximize effectiveness and lower computing expenses.