Developers still need design workflow system according to users＇ specific needs, though workflow management coalition standardized the five kinds of abstract interfaces in workflow reference model. Specific business ...Developers still need design workflow system according to users＇ specific needs, though workflow management coalition standardized the five kinds of abstract interfaces in workflow reference model. Specific business process characteristics are still supported by specific workflow system. A set of common functionalities of workflow engine are abstracted from business component, so the reusability of business component is extended into workflow engine and composition method is proposed. Needs of different business requirements and characteristics are met by reusing the workflow engine.展开更多
At present, there is no formalized description of the executing procedure of workflow models. The procedure of workflow models executing in workflow engine is described using operational semantic. The formalized descr...At present, there is no formalized description of the executing procedure of workflow models. The procedure of workflow models executing in workflow engine is described using operational semantic. The formalized description of process instances and activity instances leads to very clear structure of the workflow engine, has easy cooperation of the heterogeneous workflow engines and guides the realization of the workflow engine function. Meanwhile, the software of work flow engine has been completed by means of the formalized description.展开更多
In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected re...In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected resource request spikes,the engine is limited to the current workflow load information for resource allocation,which lacks the agility and predictability of resource allocation,resulting in over and underprovisioning resources.This mechanism seriously hinders workflow execution efficiency and leads to high resource waste.To overcome these drawbacks,we propose an adaptive resource allocation scheme named adaptive resource allocation scheme(ARAS)for the Kubernetes-based workflow engines.Considering potential future workflow task requests within the current task pod’s lifecycle,the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios.The ARAS offers resource discovery,resource evaluation,and allocation functionalities and serves as a key component for our tailored workflow engine(KubeAdaptor).By integrating the ARAS into KubeAdaptor for workflow containerized execution,we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS.Compared with the baseline algorithm,experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows,time-saving of 26.4% to 79.86% in the average duration of individual workflow,and an increase of 1% to 16% in centrol processing unit(CPU)and memory resource usage rate.展开更多
文摘Developers still need design workflow system according to users＇ specific needs, though workflow management coalition standardized the five kinds of abstract interfaces in workflow reference model. Specific business process characteristics are still supported by specific workflow system. A set of common functionalities of workflow engine are abstracted from business component, so the reusability of business component is extended into workflow engine and composition method is proposed. Needs of different business requirements and characteristics are met by reusing the workflow engine.
基金This workis supported by the Jilin Province Science and Technology Development Plan Project (20050527) .
文摘At present, there is no formalized description of the executing procedure of workflow models. The procedure of workflow models executing in workflow engine is described using operational semantic. The formalized description of process instances and activity instances leads to very clear structure of the workflow engine, has easy cooperation of the heterogeneous workflow engines and guides the realization of the workflow engine function. Meanwhile, the software of work flow engine has been completed by means of the formalized description.
基金supported by the National Natural Science Foundation of China(61873030,62002019).
文摘In a cloud-native era,the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes.However,when encountering continuous workflow requests and unexpected resource request spikes,the engine is limited to the current workflow load information for resource allocation,which lacks the agility and predictability of resource allocation,resulting in over and underprovisioning resources.This mechanism seriously hinders workflow execution efficiency and leads to high resource waste.To overcome these drawbacks,we propose an adaptive resource allocation scheme named adaptive resource allocation scheme(ARAS)for the Kubernetes-based workflow engines.Considering potential future workflow task requests within the current task pod’s lifecycle,the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios.The ARAS offers resource discovery,resource evaluation,and allocation functionalities and serves as a key component for our tailored workflow engine(KubeAdaptor).By integrating the ARAS into KubeAdaptor for workflow containerized execution,we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS.Compared with the baseline algorithm,experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows,time-saving of 26.4% to 79.86% in the average duration of individual workflow,and an increase of 1% to 16% in centrol processing unit(CPU)and memory resource usage rate.
