Scalability in Microservices: A systematic literature review

The scalability of microservices architectures is crucial for modern software systems, yet it presents significant challenges due to their inherent complexities. This study aims to systematically review existing literature on the scalability of microservices, identifying key strategies, challenges, and emerging trends. We conducted a systematic literature review following the PRISMA guidelines, analyzing 44 scholarly articles that specifically address the scalability of microservices. The review focused on various scaling approaches, metrics, and the effectiveness of autoscaling mechanisms. Our findings reveal a diverse body of literature with a predominant focus on autoscaling strategies, particularly those utilizing machine learning. Key challenges identified include accurate metrics collection, dynamic scaling decision-making, and balancing performance with cost and security. While progress has been made in addressing scalability challenges, significant gaps remain, particularly in standardizing autoscaling metrics. Future research should focus on developing robust, adaptive autoscaling systems that can effectively manage real-world complexities and dynamic workloads, ensuring both performance and cost optimization in microservices architectures.