Published October 31, 2021 | Version v1
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Resolving Scalability and Performance Bottlenecks in Aws-Based Microservices Architectures

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The increasing adoption of microservices architectures within AWS environments has brought to the forefront the critical challenges of scalability and performance bottlenecks. This review paper systematically examines these challenges, presenting a comprehensive analysis of the inherent limitations and inefficiencies that impede optimal performance. The study identifies key bottlenecks such as latency, resource contention, inefficient load balancing, and suboptimal service orchestration. By leveraging a thorough literature review, the paper explores state-of-the-art strategies and best practices aimed at mitigating these issues. The research highlights the effectiveness of employing containerization technologies, such as Docker and Kubernetes, to enhance resource utilization and scalability. Furthermore, it delves into advanced load balancing techniques, including the use of service meshes and dynamic scaling policies, to improve traffic management and system resilience. The paper also evaluates the role of serverless computing in addressing performance constraints by providing on-demand resource allocation and reducing overhead. Additionally, it discusses monitoring and observability tools essential for identifying and resolving performance bottlenecks in real-time. Through comparative analyses, the review underscores the importance of adopting a holistic approach that integrates multiple strategies tailored to specific application needs. The findings suggest that a combination of container orchestration, serverless architectures, and robust monitoring frameworks can significantly enhance the scalability and performance of AWS-based microservices. The paper concludes with recommendations for future research directions, emphasizing the need for continuous innovation in adaptive load balancing and automated orchestration to cope with evolving demands and technological advancements. This paper serves as a valuable resource for practitioners and researchers seeking to optimize microservices performance within cloud environments.

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