Service Composition Management Using Risk Analysis and Tracking

  • Shang-Pin Ma
  • Ching-Lung Yeh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)


How to effectively and efficiently monitor, manage, and adapt web services is becoming a significant issue to address. In this paper, we argue that only solving emerging service faults at deployment time or runtime is not enough; on the contrary, we believe that prediction of service faults is equivalently important. We propose a risk-driven service composition management process including four main phases: preparation, planning, monitoring and reaction, and analysis. By applying the proposed approach, risky component services can be removed earlier, and the fault source can be tracked and identified more easily when any failure occurs. We believe the proposed risk-driven approach can effectively and efficiently ensure the robustness of an SOA-based system.


service management risk management service composition 


  1. 1.
    Baresi, L., Guinea, S., Nano, O., Spanoudakis, G.: Comprehensive Monitoring of BPEL Processes. IEEE Internet Computing 14(3), 50–57 (2010)CrossRefGoogle Scholar
  2. 2.
    Calinescu, R., Grunske, L., Kwiatkowska, M., Mirandola, R., Tamburrelli, G.: Dynamic QoS Management and Optimization in Service-Based Systems. IEEE Transactions on Software Engineering 37(3), 387–409 (2011)CrossRefGoogle Scholar
  3. 3.
    El Haddad, J., Manouvrier, M., Ramirez, G., Rukoz, M.: QoS-Driven Selection of Web Services for Transactional Composition. In: 2008 IEEE International Conference on Web Services, ICWS 2008 (2008)Google Scholar
  4. 4.
    Erradi, A., Maheshwari, P., Tosic, V.: WS-Policy based Monitoring of Composite Web Services. In: The Fifth European Conference on Web Services, pp. 99–108. IEEE Computer Society (2007)Google Scholar
  5. 5.
    Friedrich, G., Fugini, M., Mussi, E., Pernici, B., Tagni, G.: Exception Handling for Repair in Service-Based Processes. IEEE Transactions on Software Engineering 36(2), 198–215 (2010)CrossRefGoogle Scholar
  6. 6.
    Hsin-Yi, T., Yu-Lun, H.: An Analytic Hierarchy Process-Based Risk Assessment Method for Wireless Networks. IEEE Transactions on Reliability 60(4), 801–816 (2011)CrossRefGoogle Scholar
  7. 7.
    Kettunen, J., Salo, A., Bunn, D.W.: Optimization of Electricity Retailer’s Contract Portfolio Subject to Risk Preferences. IEEE Transactions on Power Systems 25(1), 117–128 (2010)CrossRefGoogle Scholar
  8. 8.
    Kokash, N.: Risk Management for Service-Oriented Systems. In: Baresi, L., Fraternali, P., Houben, G.-J. (eds.) ICWE 2007. LNCS, vol. 4607, pp. 563–568. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Kwan, T.W., Leung, H.K.N.: A Risk Management Methodology for Project Risk Dependencies. IEEE Transactions on Software Engineering 37(5), 635–648 (2011)CrossRefGoogle Scholar
  10. 10.
    Lee, J., Ma, S.-P., Lee, S.-J., Wu, C.-L., Lee, C.-H.L.: Towards a High-Availability-Driven Service Composition Framework. In: Service Life Cycle Tools and Technologies: Methods, Trends and Advances, pp. 221–243. IGI Global (2012)Google Scholar
  11. 11.
    Ma, S.-P., Kuo, J.-Y., Fanjiang, Y.-Y., Tung, C.-P., Huang, C.-Y.: Optimal service selection for composition based on weighted service flow and Genetic Algorithm. In: 2010 International Conference on Machine Learning and Cybernetics, ICMLC (2012)Google Scholar
  12. 12.
    Moser, O., Rosenberg, F., Dustdar, S.: Non-intrusive monitoring and service adaptation for WS-BPEL. In: The 17th International Conference on World Wide Web, pp. 815–824. ACM, Beijing (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shang-Pin Ma
    • 1
  • Ching-Lung Yeh
    • 1
  1. 1.Department of Computer Science and EngineeringNational Taiwan Ocean UniversityKeelungTaiwan

Personalised recommendations