Workflows are actively being used in both business and scientific domains to automate processes and facilitate collaboration. A workflow management (or enactment) system (WfMS) defines, creates and manages the execution of workflows on one or more workflow engines, which are able to interpret workflow definitions, allocate resources, interact with workflow participants and, where required, invoke the needed tools (e.g., databases, job schedulers, etc.) and applications. Traditional WfMSs and workflow design processes view the workflow as a one-time interaction with the various data sources, i.e., when a workflow is invoked, its steps are executed once and in-order. The fundamental underlying assumption has been that data sources are passive and all interactions are structured along the request/reply (query) model. Hence, traditional WfMS cannot effectively support business or scientific monitoring applications that require the processing of data streams such as those generated by sensing devices as well as mobile and web applications.ud udIt is the hypothesis of this dissertation that Workflow Management Systems can be extended to support data stream semantics to enable monitoring applications. This includes the ability to apply flexible bounds on unbounded data streams and the ability to facilitate on-the-fly processing of bounded bundles of data (window semantics). To support this hypothesis this dissertation has produced new specifications, a design, an implementation and a thorough evaluation of a novel Continuous Workflows (CWf) model, which is backwards compatible with currently available workflow models. The CWf model was implemented in a CONtinuous workFLow ExeCution Engine, CONFLuEnCE, as an extension of Kepler, which is a popular scientific WfMS. The applicability of the CWf model in both scientific and business applications was demonstrated by utilizing CONFLuEnCE in Astroshelf to support live annotations (i.e., monitoring of astronomical data), and to support supply chain monitoring and management. The implementation of CONFLuEnCE led to the realization that different applications have different performance requirements and hence an integrated workflow scheduling framework is essential. Towards meeting this need, STAFiLOS, a Stream FLOw Scheduling framework for Continuous Workflows, was designed and implemented, within CONFLuEnCE. The performance of STAFiLOS was evaluated using the Linear Road Benchmark for continuous workflows.ud
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机译:工作流正在业务领域和科学领域中积极使用,以实现流程自动化和促进协作。工作流管理(或制定)系统(WfMS)在一个或多个工作流引擎上定义,创建和管理工作流的执行,这些引擎能够解释工作流定义,分配资源,与工作流参与者进行交互,并在需要时调用所需的工具(例如数据库,作业计划程序等)和应用程序。传统的WfMS和工作流设计过程将工作流视为与各种数据源的一次性交互,即,当调用工作流时,其步骤将按顺序执行一次。基本的基本假设是数据源是被动的,并且所有交互都是根据请求/答复(查询)模型构造的。因此,传统的WfMS无法有效地支持需要处理数据流(例如由传感设备以及移动和Web应用程序生成的数据流)的业务或科学监控应用程序。 ud ud这是本文的假设,即工作流管理系统可以扩展以支持数据流语义以启用监视应用程序。这包括在无限制的数据流上应用灵活范围的能力,以及促进对绑定的数据束进行即时处理的能力(窗口语义)。为了支持这一假设,本论文提出了一种新颖的连续工作流(CWf)模型的新规范,设计,实现和全面评估,该模型与当前可用的工作流模型向后兼容。 CWf模型是在连续的工作流执行引擎CONFLuEnCE中实现的,它是开普勒的扩展,开普勒是一种流行的科学WfMS。通过利用Astroshelf中的CONFLuEnCE支持实时注释(即,监视天文数据)以及支持供应链监视和管理,证明了CWf模型在科学和商业应用中的适用性。 CONFLuEnCE的实现导致认识到不同的应用程序具有不同的性能要求,因此集成的工作流调度框架至关重要。为了满足这一需求,在CONFLuEnCE中设计并实现了STAFiLOS,这是一种用于连续工作流的Stream FLOw计划框架。 STAFiLOS的性能是使用线性道路基准评估连续工作流程的。 ud
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