Topological Evaluation of Cloud Computing Networks and Real-Time Scenario-Based Effective Usage

Abstract

Cloud technology provides computing services over the internet, enabling entrepreneurs to access tools and services previously only available to large organizations, enhancing efficiency, business scaling, and competitiveness. With a step-by-step practical performance, the study builds real-time clouds using several lab scenarios. The research offers recommendations for cloud computing networks' performance, security, and awareness in this way. The study investigates and improves cloud computing networks in IoT and other network architectures using cheminformatics, a combination of chemistry, computer, and mathematics. It computes topological invariants, such as K-banhatti sombor (KBSO) invariants (KBSO), Dharwad Invariants, K-banhatti Redefined Zagreb (KBRZ), their different forms, and Quadratic-contra harmonic invariants (QCI), to explore and enhance their characteristics like scalability, efficiency, higher throughput, reduced latency, and best-fit topology. The main objective is to develop formulas to check the topology, and performance of certain cloud networks without experiments and produce mathematical modeling results with graphical results. It also gives the optimized ranges of the network with one optimized value. After these evaluations, the network graph also checks for irregularities if exist with the help of the Irregularity Sombor (ISO) index.  The study also produced real-time scenario-based clouds and performance-based use. The results will help researchers construct and improve these networks with different physical characteristics.