Refining Distributed System Efficiency with Microservices: Advanced Strategies for Enhancing Performance, Scalability, and Resilience in Complex Architectural Environments

Abstract

This paper explores the optimization of distributed systems through the adoption of microservices architecture. Distributed systems, which leverage multiple networked nodes to perform tasks more efficiently and reliably, have evolved significantly from centralized mainframes to client-server models and, more recently, to cloud computing and microservices. Microservices architecture decomposes applications into small, independently deployable services, enhancing scalability, flexibility, and resilience compared to traditional monolithic architectures. Key optimization techniques discussed include load balancing, data partitioning, caching, and elastic scaling to improve performance and scalability. The paper addresses critical research questions about effective optimization techniques, scalability maintenance, the role of microservices, and associated challenges. Through a comprehensive literature review, detailed case studies, and analysis of findings, the paper concludes that microservices offer substantial benefits in optimizing distributed systems, particularly in terms of independent deployment and technological heterogeneity, thereby providing robust solutions for modern computing demands.

Keywords: Microservices, Docker, Kubernetes, Spring Boot, Apache Kafka, RESTful APIs, gRPC, Consul, Istio, Prometheus, Grafana, Jenkins, Ansible, Terraform, AWS Lambda, Node.js, Redis