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linear programming. However, this process is based on deterministic values, which
insufficiently reflect the uncertainty associated with a QoS attribute in actual execution.
E.g., response times may heavily fluctuate due to network and computational load, thus
leading to QoS violations in the actual execution of a workflow.
As a solution, we have adapted an existing methodology for the simulation of
generalized activity networks to the specific field of workflows in SOA. This simulation
process allows to assess the expected characteristics of a workflow, most importantly
the likelihood that a QoS constraint will be violated, in more detail. Depending on
a requester’s preferences, the outcome of the simulation process can be utilized to
repeatedly conduct the service selection procedure, thus minimizing the probability of
QoS violations more effectively. The practical applicability and benefit of our approach
has been proven using a prototypical implementation of a workflow simulation tool.
In our future work, we aim at combining the currently separated steps of service
selection and workflow simulation into an integrated tool. We will further investigate
the issue of mining probability distributions from historic service execution data as a
prerequisite of more realistic simulation. In this context, QoS attributes besides response
time will also be explicitly addressed.
Acknowledgements
This work has partly been sponsored by the E-Finance Lab e. V., Frankfurt am Main,
Germany (http://www.efinancelab.de).
References
1.
Krafzig, D., Banke, K., Slama, D.: Enterprise SOA: Service-Oriented Architecture Best
Practices. Prentice Hall PTR, Upper Saddle River, NJ, USA (2004)
2.
Papazoglou, M.P.: Web Services: Principles and Technology. Pearsor Education Limited,
Harlow, England (2008)
3.
Anselmi, J., Ardagna, D., Cremonesi, P.: A QoS-based Selection Approach of Autonomic
Grid Services. In: International Conference on Service-oriented Computing. (2007) 1–8
4.
Menasc
´
e, D.A., Casalicchio, E., Dubey, V.: A Heuristic Approach to optimal Service Selection
in Service-oriented Architectures. In: Workshop on Software and Performance. (2008) 13–24
5.
Mabrouk, N.B., Georgantas, N., Issarny, V.: A Semantic end-to-end QoS Model for Dynamic
Service-oriented Environments. In: Proceedings of the 2009 ICSE Workshop on Principles of
Engineering Service-oriented Systems. (2009) 34–41
6.
Huang, A.F.M., Lan, C.W., Yang, S.J.H.: An Optimal QoS-based Web Service Selection
Scheme. Information Sciences 179 (2009) 3309–3322
7.
Domschke, W., Drexl, A.: Einf
¨
uhrung in Operations Research. Springer Verlag, Heidelberg
(2007)
8.
Schuller, D., Eckert, J., Miede, A., Schulte, S., Steinmetz, R.: QoS-Aware Service Composition
for Complex Workflows. In: International Conference on Internet and Web Applications and
Services. (forthcoming 2010)
9.
van der Aalst, W.M., van Hee, K.M.: Workflow Management: Models, Methods, and Systems.
MIT Press (2002)
10.
Heckmann, O.: A System-oriented Approach to Efficiency and Quality of Service for Internet
Service Providers. PhD thesis, TU Darmstadt, Fachbereich Informatik (2004)
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