Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Automating process discovery through event-data analysis
Proceedings of the 17th international conference on Software engineering
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
An Overview of Standards and Related Technology in Web Services
Distributed and Parallel Databases
Distributed and Parallel Databases
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
E-services: a look behind the curtain
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Deriving Service Models in Cross-Organizational Workflows
RIDE '99 Proceedings of the Ninth International Workshop on Research Issues on Data Engineering: Information Technology for Virtual Enterprises
Response-Time Analysis of Composite Web Services
IEEE Internet Computing
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Bottlenecks Identification in Multiclass Queueing Networks Using Convex Polytopes
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
| Hi-index | 0.00 |
Web service composition provides a way to build value-added services and web applications by integrating and composing existing web services. In this paper, a composite web service is modeled using queueing network for the purpose of performance analysis. Each component web service participating the composite web service corresponds to one service center. The control flow between component web services is represented by the Markov chain that describes the transition of customers between service centers. To perform performance analysis, the Markov chain should be known first. However, a web service is usually a black box and only its interfaces can be seen externally, so the internal control flow can be only estimated from history execution logs. This paper gives a method that mines the Markov chain of a composite web service from its execution logs. Then, bottlenecks identification and performance analysis are conducted for the queueing network model. Experimental results show that this model mining method is effective and efficient.