LabFlow-l: A database benchmark for high-throughput workflow management

Workflow management is a ubiquitous task faced by many organizations, and entails the coordination of various activities. This coordination is increasingly carried out by software systems called workflow management systems (WFMS). An important component of many WFMSs is a DBMS for keeping track of workflow activity. This DBMS maintains an audit trail, or event history, that records the results of each activity. Like other data, the event history can be indexed and queried, and views can be defined on top of it. In addition, a WFMS must accommodate frequent workflow changes, which result from a rapidly evolving business environment. Since the database schema depends on the workflow, the DBMS must also support dynamic schema evolution. These requirements are especially challenging in high-throughput WFMSs-i.e., systems for managing high-volume, mission-critical workflows. Unfortunately, existing database benchmarks do not capture the combination of flexibility and performance required by these systems. To address this issue, we have developed LabFlow-1, the first version of a benchmark that concisely captures the DBMS requirements of high-throughput WFMSs. LabFlow-1 is based on the data and workflow management needs of a large genome-mapping laboratory, and reflects their real-world experience. In addition, we use LabFlow-1 to test the usability and performance of two object storage managers. These tests revealed substantial differences between these two systems, and highlighted the critical importance of being able to control locality of reference to persistent data.

Duke Scholars

Author Steven George Rozen Biostatistics & Bioinformatics, Division of Integrative Geno ...