Comprehensive Review of Performance Optimization Strategies for Serverless Applications on AWS Lambda
Mohamed Lemine El Bechir, Cheikh Sad Bouh, Abobakr Shuwail
TL;DR
This survey addresses the challenge of optimizing performance, cost, and scalability for AWS Lambda-based serverless applications. It synthesizes a broad set of approaches, including execution management with Step Functions, workflow-aware estimation, dynamic resource tuning (StepConf), architectural optimization via AWS SAM and ALB, enhanced observability, and data-processing considerations, enriched by practical case studies and benchmarking insights. The work highlights key strategies, practical guidelines, and notable gaps—such as real-time analytics integration and ML-assisted adaptive optimization—for practitioners aiming to deploy efficient serverless solutions. Overall, the paper offers a comprehensive, technically grounded resource to improve latency, throughput, energy efficiency, and total cost in serverless deployments.
Abstract
This review paper synthesizes the latest research on performance optimization strategies for serverless applications deployed on AWS Lambda. By examining recent studies, we highlight the challenges, solutions, and best practices for enhancing the performance, cost efficiency, and scalability of serverless applications. The review covers a range of optimization techniques including resource management, runtime selection, observability improvements, and workload aware operations.