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
The metaheuristics algorithm is increasingly important in solving many kinds of real-life optimization problems but the implementation involves programming difficulties. As a result, many researchers have relied on so...The metaheuristics algorithm is increasingly important in solving many kinds of real-life optimization problems but the implementation involves programming difficulties. As a result, many researchers have relied on software framework to accelerate the development life cycle. However, the available software frameworks were mostly designed for rapid development rather than flexible programming. Therefore, in order to extend software functions, this approach involves modifying software libraries which requires the programmers to have in-depth understanding about the internal working structure of software and the programming language. Besides, it has restricted programmers for implementing flexible user-defined low-level hybridization. This paper presents the concepts and formal definition of metaheuristics and its low-level hybridization. In addition, the weaknesses of current programming approaches supported by available software frameworks for metaheuristics are discussed. Responding to the deficiencies, this paper introduces a rapid and flexible software framework with scripting language environment. This approach is more flexible for programmers to create a variety of user-defined low-level hybridization rather than bounded with built-in metaheuristics strategy in software libraries.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
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The metaheuristics algorithm is increasingly important in solving many kinds of real-life optimization problems but the implementation involves programming difficulties. As a result, many researchers have relied on software framework to accelerate the development life cycle. However, the available software frameworks were mostly designed for rapid development rather than flexible programming. Therefore, in order to extend software functions, this approach involves modifying software libraries which requires the programmers to have in-depth understanding about the internal working structure of software and the programming language. Besides, it has restricted programmers for implementing flexible user-defined low-level hybridization. This paper presents the concepts and formal definition of metaheuristics and its low-level hybridization. In addition, the weaknesses of current programming approaches supported by available software frameworks for metaheuristics are discussed. Responding to the deficiencies, this paper introduces a rapid and flexible software framework with scripting language environment. This approach is more flexible for programmers to create a variety of user-defined low-level hybridization rather than bounded with built-in metaheuristics strategy in software libraries.
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.
