Elevating Microservice Design: Implementing Best-in-Class Practices for Achieving Scalability, Resilience, Performance Optimization, and Maintainability in Distributed Systems

Authors

  • Diego Vargas  Department of Computer Science, Universidad Autónoma de la Amazonía

Keywords:

Microservice Architecture, Spring Boot, Docker, Kubernetes

Abstract

This paper investigates the enhancement of Microservice Architecture (MSA) by integrating best practices, aiming to provide a comprehensive analysis of MSA's principles, benefits, challenges, and best practices. MSA, characterized by small, independent services communicating via well-defined APIs, offers significant advantages over traditional monolithic architectures, such as improved scalability, maintainability, and agility. The paper traces the evolution of MSA from Service-Oriented Architecture (SOA) and highlights its adoption by industry leaders like Amazon and Netflix. Core principles including decoupling and service independence, and key design patterns such as API Gateway, Circuit Breaker, and Service Discovery, are explored to demonstrate how they contribute to building robust, scalable systems. The discussion extends to critical components, including services, databases, and communication protocols, underscoring their roles in achieving a resilient architecture. Furthermore, the paper addresses best practices in microservice design, emphasizing Domain-Driven Design (DDD), bounded contexts, event sourcing, and Command Query Responsibility Segregation (CQRS) for effective service boundaries and data management. Through real-world case studies and industry insights, the paper illustrates practical applications and impacts, providing valuable knowledge for both practitioners and researchers in the field.

Author Biography

Diego Vargas,  Department of Computer Science, Universidad Autónoma de la Amazonía

 

 

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Published

2022-11-23

How to Cite

Diego Vargas. (2022). Elevating Microservice Design: Implementing Best-in-Class Practices for Achieving Scalability, Resilience, Performance Optimization, and Maintainability in Distributed Systems. Sage Science Review of Applied Machine Learning, 5(2), 81–112. Retrieved from https://journals.sagescience.org/index.php/ssraml/article/view/176