Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Advances in Petri nets 1986, part II on Petri nets: applications and relationships to other models of concurrency
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
The implementation of temporal intervals in qualitative simulation graphs
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Workflow performance and scalability analysis using the layered queuing modeling methodology
GROUP '01 Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work
Graph-based modeling: PERT scheduling with resources using qualitative simulation graphs
Proceedings of the 32nd conference on Winter simulation
Architecture of Integrated Information Systems: Foundations of Enterprise Modelling
Architecture of Integrated Information Systems: Foundations of Enterprise Modelling
Aris-Business Process Frameworks
Aris-Business Process Frameworks
Enterprise-Wide Workflow Management
IEEE Concurrency
Performance and Scalability Analysis on Client-Server Workflow Architecture
ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
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In the scenario of multiple homogenous intra enterprise workflow domains, several partial processes cooperate according to certain interoperability models. In this case, workflow simulation requires more flexible and powerful mathematical models. In this paper, BCMP queuing network is imported to work-flow based business process simulation filed for the first time. With its special multi-class and multi-chain characteristics, we can provide a workflow simulation across multiple domains using three basic interoperability models. This simulation approach accords well with the dynamic and randomness of workflow. Thus, we can report some performance indexes and analysis before deploying.