Optimizing AWS Lambda Performance: Unveiling the Relationship between Memory Allocation, CPU Power and Cost Efficiency

Abstract

This technical paper examines, emphasizing AWS Lambda, the crucial relationship between memory use, cost-effectiveness and performance in server less applications. We highlight the significance of memory allocation, look into factors that affect consumption, and offer methods for locating and resolving memory-related problems. An essential part of our research is the relationship between CPU power assignment and Lambda execution time. We illustrate the significant influence of CPU power on execution time and operating expenses using a Lambda function from the real world. We present a sample Lambda function and examine its performance with different memory setups. The AWS Lambda Power Tuning framework results, intended to identify the most economical memory configuration automatically are compared against manual memory determination in the article. Empirical evidence evaluates the framework's success in maximizing performance and cost. The report concludes by synthesizing significant findings and providing practitioners with practical suggestions. This study adds significant knowledge to server less computing by offering real-world examples, empirical support, and contrasts between automated and human memory tuning. By addressing the direct relation of memory allocation to cost-effectiveness and its implications on server less application performance, the work advances our understanding of the complex balance necessary for optimal resource management.