Software-Defined Workflows for Distributed Interoperable Closed-Loop Neuromodulation Control Systems.
Software-Defined Workflows for Distributed Interoperable Closed-Loop Neuromodulation Control Systems.
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DOI：
10.1109/access.2021.3113892
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Mahmoudi B
中科院分区：
文献类型：
--
作者：
Kathiravelu P;Sarikhani P;Gu P;Mahmoudi B
闭环神经调节控制系统通过记录神经生理活动并通过反馈回路修改这些活动来促进调节异常生理过程。设计这样的系统需要可互操作的服务组合，由循环组成。工作流框架支持标准模块化架构，提供可重复的自动化管道。然而，这些框架限制了它们对由有向非循环图（DAG）表示的执行的支持。DAG需要一个预定义的开始和结束执行步骤，没有循环，从而防止研究人员将标准工作流语言用于闭环工作流和管道。在本文中，我们提出了NEXUS，分布式分析系统的工作流编排框架。NEXUS提出了一种软件定义的工作流方法，其灵感来自软件定义的网络（SDN），该方法将服务实例中的数据流与控制流分离。NEXUS通过以逻辑集中的方法定义工作流，从代表每个执行步骤的微服务中创建具有闭环的可互操作工作流。集中的NEXUS编排器有助于动态地组合和管理来自服务和现有工作流的科学工作流，限制最小。NEXUS将复杂的工作流表示为有向超图（DHG）而不是DAG。我们通过支持工作流中的循环来说明神经调节控制系统的无缝执行，作为NEXUS的用例。我们的评估突出了NEXUS在建模和执行闭环工作流方面的可行性、灵活性、性能和可扩展性。
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 NEXUS, a workflow orchestration framework for distributed analytics systems. NEXUS 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. NEXUS enables creating interoperable workflows with closed loops by defining the workflows in a logically centralized approach, from microservices representing each execution step. The centralized NEXUS orchestrator facilitates dynamically composing and managing scientific workflows from the services and existing workflows, with minimal restrictions. NEXUS 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 NEXUS. Our evaluations highlight the feasibility, flexibility, performance, and scalability of NEXUS in modeling and executing closed-loop workflows.
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DOI：
10.1093/bioinformatics/btaa1033
发表时间：
2021-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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