Efficient Auto-Scaling for Microservices in Cloud Environments

Panaganti, Ritesh (2025) Efficient Auto-Scaling for Microservices in Cloud Environments. Masters thesis, Dublin, National College of Ireland.

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Abstract

Modern business applications implement microservices architecture as their popular design methodology for developing cloud-ready modular systems. The use of computing resources for maintaining high performance continues to present difficulties in cases where infrastructure capabilities are insufficient. These consist of problems like CPU overload and higher response time at spiky moments of increased traffic. The research focuses on how to apply auto-scaling features, especially horizontal scaling to microservices it deploys via Amazon Elastic Container Service (ECS) with containers launched with the Fargate launch type. The research aims to put into practice horizontal scaling approaches through performance monitoring and system metrics primarily CPU utilization, which depend on Amazon CloudWatch. The research explores resource allocation alteration systems that react to workload with the intention of optimizing cost-performance ratios and avoiding unnecessary resource allocation. In order to simulate dynamic workload and to analyse the autoscaling behavior, Locust performance testing tool was used to create real-time HTTP traffic. The system has implemented two lightweight approaches of load balancing which are Round Robin and Random in order to distribute requests in a balanced manner. Across all the scenarios Round Robin achieved average response time of 221ms compared with Random of 280ms. Under load, CPU briefly peaked 95% to 100% at-scale-out and then stabilized around 45% per service once the additional task was healthy with balanced utilization. The research is aimed at providing practical data as to the implementation of scalable microservice components in environments with resource constraints when using clouds environments.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Cortes Mendoza, Jorge Mario UNSPECIFIED
Uncontrolled Keywords:	Auto-scaling; Microservices; Amazon ECS; Cloud Computing; Containers; Round Robin; Random; Loadbalancing; Locust; Amazon ECR
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing T Technology > T Technology (General) > Information Technology
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	30 Mar 2026 11:18
Last Modified:	30 Mar 2026 11:18
URI:	https://norma.ncirl.ie/id/eprint/9249

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