Software-Defined Workflows for Distributed Interoperable Closed-Loop Neuromodulation Control Systems.

Closed-loop neuromodulation control systems facilitate regulating abnormal physiological processes by recording neurophysiological activities and modifying those activities through feedback loops. Designing such systems requires interoperable service composition, consisting of cycles. Workflow frameworks enable standard modular architectures, offering reproducible automated pipelines. However, those frameworks limit their support to executions represented by directed acyclic graphs (DAGs). DAGs need a pre-defined start and end execution step with no cycles, thus preventing the researchers from using the standard workflow languages as-is for closed-loop workflows and pipelines. In this paper, we present , a workflow orchestration framework for distributed analytics systems. proposes a Software-Defined Workflows approach, inspired by Software-Defined Networking (SDN), which separates the data flows across the service instances from the control flows. enables creating interoperable workflows with closed loops by defining the workflows in a logically centralized approach, from microservices representing each execution step. The centralized orchestrator facilitates dynamically composing and managing scientific workflows from the services and existing workflows, with minimal restrictions. represents complex workflows as directed hypergraphs (DHGs) rather than DAGs. We illustrate a seamless execution of neuromodulation control systems by supporting loops in a workflow as the use case of . Our evaluations highlight the feasibility, flexibility, performance, and scalability of in modeling and executing closed-loop workflows.