Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification

Guoxin Su () and Li Liu
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Guoxin Su: School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
Li Liu: School of Big Data & Software Engineering, Chongqing University, Chongqing 400044, China

Future Internet, 2024, vol. 16, issue 9, 1-19

Abstract: Workflows orchestrate a collection of computing tasks to form a complex workflow logic. Different from the traditional monolithic workflow management systems, modern workflow systems often manifest high throughput, concurrency and scalability. As service-based systems, execution time monitoring is an important part of maintaining the performance for those systems. We developed a trace profiling approach that leverages quantitative verification (also known as probabilistic model checking ) to analyse complex time metrics for workflow traces. The strength of probabilistic model checking lies in the ability of expressing various temporal properties for a stochastic system model and performing automated quantitative verification. We employ semi-Makrov chains (SMCs) as the formal model and consider the first passage times (FPT) measures in the SMCs. Our approach maintains simple mergeable data summaries of the workflow executions and computes the moment parameters for FPT efficiently. We describe an application of our approach to AWS Step Functions, a notable workflow web service. An empirical evaluation shows that our approach is efficient for computer high-order FPT moments for sizeable workflows in practice. It can compute up to the fourth moment for a large workflow model with 10,000 states within 70 s.

Keywords: AWS Step Functions; execution time; probabilistic model checking; profiling; semi-Markov chain; workflow (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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