Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations
Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations
Performance Equivalent Analysis of Workflow Systems Based on Stochastic Petri Net Models
EDCIS '02 Proceedings of the First International Conference on Engineering and Deployment of Cooperative Information Systems
On the integration of delay and throughput measures in distributed processing models
On the integration of delay and throughput measures in distributed processing models
Current Solutions for Web Service Composition
IEEE Internet Computing
Measurement-based Performance Analysis of E-commerce Applications with Web Services Components
ICEBE '05 Proceedings of the IEEE International Conference on e-Business Engineering
Performance Analysis Using Stochastic Petri Nets
IEEE Transactions on Computers
Performance Evaluation of Workflows Using Continuous Petri Nets with Interval Firing Speeds
PETRI NETS '08 Proceedings of the 29th international conference on Applications and Theory of Petri Nets
A Stochastic Approach to Predict Performance of Web Service Composition
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
Static Analysis of Concurrent Programs Using Ordinary Differential Equations
ICTAC '09 Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing
Transforming BPEL to petri nets
BPM'05 Proceedings of the 3rd international conference on Business Process Management
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Web services technology has yet to address questions such as how can I know that the Web service will meet my performance requirements such as response time? In this paper, a new method is proposed to measure the performance of service composition. Service composition described with BPEL is modeled by a family of ordinary differential equations, where each equation describes the state change of the service composition. Each service state is measured by a time-dependent function that indicates the extent to which the state can be reached in execution. This measure information can help us to conduct performance analysis such as estimating response time, throughput and efficiency. This method has the following advantages: 1) it treats the system as a 'white' box and displays a global picture of execution state to the users, thus users know exactly where to improve the performance; 2) it can entirely avoid state explosion problem; 3) it is faster than SPN based performance analysis methods.