An Empirical Study on Serverless Workflow Service

TL;DR

This paper systematically analyzes and compares four major serverless workflow services, evaluating their characteristics and performance to guide developers and providers in making informed choices.

Contribution

It provides a comprehensive comparison of serverless workflow services across multiple dimensions and performance metrics, filling a knowledge gap in the field.

Findings

Different services vary significantly in programming models and state management.

Performance is affected by workflow complexity and data flow, influencing execution times.

Service effectiveness is validated with real workload scenarios.

Abstract

Along with the wide-adoption of Serverless Computing, more and more applications are developed and deployed on cloud platforms. Major cloud providers present their serverless workflow services to orchestrate serverless functions, making it possible to perform complex applications effectively. A comprehensive instruction is necessary to help developers understand the pros and cons, and make better choices among these serverless workflow services. However, the characteristics of these serverless workflow services have not been systematically analyzed. To fill the knowledge gap, we survey four mainstream serverless workflow services, investigating their characteristics and performance. Specifically, we review their official documents and compare them in terms of seven dimensions including programming model, state management, etc. Then, we compare the performance (i.e., execution time of functions, execution time of workflows, orchestration overhead of workflows) under various experimental settings considering activity complexity and data-flow complexity of workflows, as well as function complexity of serverless functions. Finally, we discuss and verify the service effectiveness for two actual workloads. Our findings could help application developers and serverless providers to improve the development efficiency and user experience.