Interoperability Constraints in Service Selection Algorithms

Paweł L. Kaczmarek

2012

Abstract

In Service Oriented Architecture, composite applications are developed by integration of existing, atomic services that may be available in alternative versions realizing the same functionality but having different Quality of Service (QoS) attributes. The development process requires effective service selection algorithms that balance profits and constraints of QoS attributes. Additionally, services operate in a heterogeneous environment, which requires resolution of interoperability issues during integration. In this paper, the author proposes a methodology that introduces interoperability analysis into existing service selection algorithms. Algorithm data structures are extended with additional constraints that represent interoperability for the two considered computational models: the graph-based model and the combinatorial model based on integer linear programming. The extensions enable a straightforward application of a wide range of existing algorithms as the general structure of input data is preserved. As a part of the research, a system that supports development of SOA-based applications was implemented. Chosen service selection algorithms together with appropriate extensions for interoperability analysis were implemented in the system.

References

  1. Alrifai, M., Risse, T., Dolog, P., and Nejdl, W. (2009). Service-oriented computing - icsoc 2008 workshops. chapter A Scalable Approach for QoS-Based Web Service Selection. Springer-Verlag, Berlin, Heidelberg.
  2. Bhuta, J. and Boehm, B. (2007). Attribute-based cots product interoperability assessment. In Sixth International IEEE Conference on Commercial-off-the-Shelf (COTS)-Based Software Systems.
  3. Booth, D., Haas, H., McCabe, F., Newcomer, E., Champion, M., Ferris, C., and Orchard, D. (2004). Web Services Architecture, Working Group Note. W3C.
  4. Bradley, S. P., Hax, A. C., and Magnati, T. L. (1977). Applied Mathematical Programming. Addison-Wesley.
  5. Cao, H., Feng, X., Sun, Y., Zhang, Z., and Wu, Q. (2007a). A service selection model with multiple qos constraints on the mmkp. In IFIP International Conference on Network and Parallel Computing.
  6. Cao, L., Li, M., and Cao, J. (2007b). Using genetic algorithm to implement cost-driven web service selection. Multiagent and Grid Systems - An International Journal, 3.
  7. Chun-hua, H., Xiao-hong, C., and Xi-ming, L. (2009). Dynamic services selection algorithm in web services composition supporting cross-enterprises collaboration. Cent. South Univ. Technol.
  8. Czarnul, P. (2010). Modeling, run-time optimization and execution ofdistributed workflow applications in the jee-based beesycluster environment. The Journal of Supercomputing, pages 1-26.
  9. Egyedi, T. M. (2007). Standard-compliant, but incompatible?! Computer Standards & Interfaces, 29(6):605- 613.
  10. Fang, J., Hu, S., and Han, Y. (2004). A service interoperability assessment model for service composition. IEEE International Conference on Services Computing, 0:153-158.
  11. Fisher, M., Lai, R., Sharma, S., and Moroney, L. (2006). Java EE and .NET Interoperability: Integration Strategies, Patterns, and Best Practices. FT Prentice Hall.
  12. Ford, T., Colombi, J., Graham, S., and Jacques, D. (2007). A survey on interoperability measurement. In 12th International Command and Control Research and Technology Symposium (ICCRTS) Adapting C2 to the 21st Century.
  13. Fu, X., Bultan, T., and Su, J. (2005). Synchronizability of conversations among web services. IEEE Transactions on Software Engineering, 31(12):1042-1055.
  14. Hong, L. and Hu, J. (2009). A multi-dimension qos based local service selection model for service composition. JOURNAL OF NETWORKS, 4(5).
  15. Jeong, B., Cho, H., and Lee, C. (2009). On the functional quality of service (fqos) to discover and compose interoperable web services. Expert Systems with Applications, 36(3).
  16. Kaczmarek, P. L. and Nowakowski, M. (2011). A developer's view of application servers interoperability. 9th Intr. Conf. on Parallel Processing and Applied Mathematics, Part II, LNCS 7204 (in print).
  17. Lampathaki, F., Mouzakitis, S., Gionis, G., Charalabidis, Y., and Askounis, D. (2009). Business to business interoperability: A current review of xml data integration standards. Computer Standards & Interfaces.
  18. Martello, S. and Toth, P. (1987). Algorithms for knapsack problems. Annals of Discrete Mathematics.
  19. Oasis (2007). Web Services Business Process Execution Language Version 2.0.
  20. OMG (2009). Business Process Model and Notation 2.0 Beta 1 Specification. Object Modeling Group, http://www.omg.org/cgi-bin/doc?dtc/09-08-14.
  21. Sakellariou, R., Zhao, H., Tsiakkouri, E., and Dikaiakos, M. D. (2007). Scheduling workflows with budget constraints. Integrated Research in GRID Computing, (CoreGRID Integration Workshop 2005, Selected Papers).
  22. Tan, W., Fan, Y., and Zhou, M. (2009). A petri net-based method for compatibility analysis and composition of web services in business process execution language. IEEE Transactions on Automation Science and Engineering, 6(1).
  23. Tanenbaum, A. S. and van Steen, M. (2002). Distributed Systems Principles and Paradigms. Prentice Hall.
  24. Tsalgatidou, A., Athanasopoulos, G., and Pantazoglou, M. (2008). Interoperability among heterogeneous services: The case of integration of p2p services with web services. Int. J. Web Service Res., 5(4):79-110.
  25. Ullberg, J., Lagerström, R., and Johnson, P. (2008). A framework for service interoperability analysis using enterprise architecture models. In IEEE SCC (2), pages 99-107.
  26. van der Aalst, W., Hofstede, A. H. M. T., and Weske, M. (2003). Business process management: A survey. 1st International Conference on Business Process Management, LNCS, 2678.
  27. Wang, X.-L., Jing, Z., and zhou Yang, H. (2001). Service selection constraint model and optimization algorithm for web service composition. Information Technology Journal.
  28. WS-I (2004). Interoperability: Ensuring the Success of Web Services. Web Services Interoperability Consortium.
  29. Xia, H., Chen, Y., Li, Z., Gao, H., and Chen, Y. (2009). Web service selection algorithm based on particle swarm optimization. In Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.
  30. Yu, J., Buyya, R., and Tham, C.-K. (2006). Cost-based scheduling of workflow applications on utility grids. In IEEE International Conference on e-Science and Grid Computing (e-Science).
  31. Yu, T., Zhang, Y., and Lin, K.-J. (2007). Efficient algorithms for web services selection with end-to-end qos constraints. ACM Transactions on the Web, 1(1).
  32. Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., and Sheng, Q. Z. (2003). Quality driven web services composition. In Proceedings of the 12th international conference on World Wide Web, WWW 7803.
Download


Paper Citation


in Harvard Style

L. Kaczmarek P. (2012). Interoperability Constraints in Service Selection Algorithms . In Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-8565-13-6, pages 23-32. DOI: 10.5220/0003980500230032


in Bibtex Style

@conference{enase12,
author={Paweł L. Kaczmarek},
title={Interoperability Constraints in Service Selection Algorithms},
booktitle={Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2012},
pages={23-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003980500230032},
isbn={978-989-8565-13-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Interoperability Constraints in Service Selection Algorithms
SN - 978-989-8565-13-6
AU - L. Kaczmarek P.
PY - 2012
SP - 23
EP - 32
DO - 10.5220/0003980500230032