Queuing Theory in Cloud Computing: Analyzing M/M/1 and M/M/c/N Models with AWS

Abstract

This paper explores the application of queuing theory in cloud computing, emphasizing its potential to optimize service delivery, resource utilization, and cost efficiency. Various queuing models, such as M/M/1, and  M/M/c/N, are analyzed in the context of cloud services to address dynamic workloads and user demands. Implementation steps and code examples for each model are provided, highlighting key metrics like queue length, waiting time, server utilization, and blocking probability. A comparative analysis of these models illustrates their suitability for different scenarios, from single-server setups to complex systems with variable service times. The findings underline the importance of selecting appropriate queuing models to meet system-specific requirements and propose future enhancements to tackle challenges like impatient user behavior and resource constraints.