Automated Pattern-Based Recommendation for Improving API Operation Performance and Reliability in Cloud-Based Architectures

Abstract

The extensive use of APIs as the entry point to many Cloud-based applications has created challenging problems, especially concerning API quality properties such as performance and reliability. API best practices and patterns, such as bundling requests, rate limiting, or load balancing, have been proposed to solve these challenges. Unfortunately, no study investigating the impact of existing API practices and patterns on such quality properties exists beyond informal recommendations. In this paper, we fill this gap by proposing a pattern-based, automated recommendation approach to improve the performance and reliability of API operations. We provide a benchmark suite based on a realistic open-source microservice application to enable the automatic generation of comprehensive decision tree models. These models are then processed to generate API design recommendation algorithms to improve API operations regarding performance and reliability stored in catalogs for reuse. We validate our algorithms using extensive data sets generated by running the benchmark on a private cloud and AWS. For both environments, based on the decision tree models automatically generated from the measured data, API design recommendation algorithms have been calculated using our approach.