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
In this paper, we propose astochastic Petri net model P-timed Workflow (WPTSPN) to specify, verify, and analyze a business process (BP) of a Flexible Manufacturing System (FMS). After formalizing the semantics of our ...In this paper, we propose astochastic Petri net model P-timed Workflow (WPTSPN) to specify, verify, and analyze a business process (BP) of a Flexible Manufacturing System (FMS). After formalizing the semantics of our model, we illustrate how to verifysome of its properties (reachability, safety, boundedness, liveness, correctness, alive tokens, and security) in the P-Timed context. Next, we validate the relevance of the proposed model with MATLAB simulation through a specific FMS case study. Finally, we use a generalized truncated density function to predict the duration of a token’s sojourn (residence) in a timed place with respect to the sequence states of the global FMS workflow.展开更多
Workflow management systems are being introduced in many organizations to automate the business process. The initial emphasis of introducing a workflow management system is on its application to the workflow in a give...Workflow management systems are being introduced in many organizations to automate the business process. The initial emphasis of introducing a workflow management system is on its application to the workflow in a given organization. The next step is to interconnect the workflow across organizations. We call it interworkflow, and the total support technologies, which are necessary for its realization, interworkflow management mechanism. Interworkflow is being expected as a supporting mechanism for Business to Business Electronic Commerce. We had proposed this management mechanism and confirmed its realization with the prototype. At the same time, the interface and the protocol for interconnecting heterogeneous workflow management systems has been standardized by the WfMC. So, we advance the project of the implementation of interworkflow management system for the practical use and its experimental proof.展开更多
In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The mi...In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The middleware,which offers services in such environments,should support the automation services suited for the user using various types of situational information around the user.In this paper,based on context-aware workflow language(CAWL),we propose a CAWL based composite workflow handler for supporting composite workflow services,which can integrate more than two service flows and handle them.The test results shows that the proposed CAWL handler can provide the user with the composite workflow services to cope with various demands on a basis of a scenario document founded on CAWL.展开更多
Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriat...Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.展开更多
In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired ...In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.展开更多
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.
文摘In this paper, we propose astochastic Petri net model P-timed Workflow (WPTSPN) to specify, verify, and analyze a business process (BP) of a Flexible Manufacturing System (FMS). After formalizing the semantics of our model, we illustrate how to verifysome of its properties (reachability, safety, boundedness, liveness, correctness, alive tokens, and security) in the P-Timed context. Next, we validate the relevance of the proposed model with MATLAB simulation through a specific FMS case study. Finally, we use a generalized truncated density function to predict the duration of a token’s sojourn (residence) in a timed place with respect to the sequence states of the global FMS workflow.
文摘Workflow management systems are being introduced in many organizations to automate the business process. The initial emphasis of introducing a workflow management system is on its application to the workflow in a given organization. The next step is to interconnect the workflow across organizations. We call it interworkflow, and the total support technologies, which are necessary for its realization, interworkflow management mechanism. Interworkflow is being expected as a supporting mechanism for Business to Business Electronic Commerce. We had proposed this management mechanism and confirmed its realization with the prototype. At the same time, the interface and the protocol for interconnecting heterogeneous workflow management systems has been standardized by the WfMC. So, we advance the project of the implementation of interworkflow management system for the practical use and its experimental proof.
基金The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center)support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The middleware,which offers services in such environments,should support the automation services suited for the user using various types of situational information around the user.In this paper,based on context-aware workflow language(CAWL),we propose a CAWL based composite workflow handler for supporting composite workflow services,which can integrate more than two service flows and handle them.The test results shows that the proposed CAWL handler can provide the user with the composite workflow services to cope with various demands on a basis of a scenario document founded on CAWL.
基金is with the School of Computing Science,Beijing University of Posts and Telecommunications,Beijing 100876,and also with the Key Laboratory of Trustworthy Distributed Computing and Service(BUPT),Ministry of Education,Beijing 100876,China(e-mail:zuoxq@bupt.edu.cn).supported in part by the National Natural Science Foundation of China(61874204,61663028,61703199)the Science and Technology Plan Project of Jiangxi Provincial Education Department(GJJ190959)。
文摘Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.
基金Project(61473078)supported by the National Natural Science Foundation of ChinaProject(2015-2019)supported by the Program for Changjiang Scholars from the Ministry of Education,China+1 种基金Project(16510711100)supported by International Collaborative Project of the Shanghai Committee of Science and Technology,ChinaProject(KJ2017A418)supported by Anhui University Science Research,China
文摘In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.
