Information Systems and e-Business Management

, Volume 12, Issue 3, pp 307–335 | Cite as

Optimizing customized services: efficient computation in large Service Value Networks

  • Steffen Haak
  • Christof Weinhardt
Original Article


Many times, service innovation occurs when service consumers pose innovative requirements on service providers. The trend towards standardization, simplification and modularization in the service sector has fostered the raise of Service Value Networks where providers and consumers jointly co-create value in an innovative manner. With many different competing services available, the user experience, which is captured by the non-functional Quality-of-Service (QoS) attributes, is an important competitive factor. QoS computation for complex Web services, i.e. the aggregation of QoS factors from atomic services, is essential for an automated an optimized service selection process. However, the computational complexity has often been disregarded in the respective field of research, whereas computational efficiency is inevitable for the application in online scenarios. The threefold contribution of this paper consists of a model for describing the optimization process in Service Value Networks, an extensive elaboration on different optimization techniques that allow for a computational efficient service selection and a broad analytical and simulation-based evaluation of these techniques.


Customized service Optimization Algorithm Heuristic QoS Simulation study 


  1. Asker J, Cantillon E (2008) Properties of scoring auctions. RAND J Econ 39(1):69–85CrossRefGoogle Scholar
  2. Basole RC, Rouse WB (2008) Complexity of service value networks: conceptualization and empirical investigation. IBM Syst J 47:53–70Google Scholar
  3. Bellman RE (1957) Dynamic programming. Princeton University Press, PrincetonGoogle Scholar
  4. Berardi D, Calvanese D, De Giacomo G, Lenzerini M, Mecella M (2005) Automatic service composition based on behavioral descriptions. Int J Coop Inf Syst 14(4):333–376CrossRefGoogle Scholar
  5. Blake M, Cummings D (2007) Workflow composition of service level agreements. In: Services computing, 2007. SCC 2007. IEEE international conference on. IEEE, pp 138–145Google Scholar
  6. Blau B (2009) Coordination in service value networks. PhD dissertation, Universitaet Karlsruhe (TH), Fakultaet fuer WirtschaftswissenschaftenGoogle Scholar
  7. Blau B, Conte T, Weinhardt C (2010) Incentives in service value networks—on truthfulness, sustainability, and interoperability. In: ICIS 2010 proceedings. Saint Louis, Missouri, USA. Paper 8Google Scholar
  8. Blau B, Kramer J, Conte T, Van Dinther C (2009) Service value networks. In: Commerce and enterprise computing, 2009. CEC’09. IEEE conference on. IEEE, pp 194–201Google Scholar
  9. Crockford D (2006) Json: the fat-free alternative to xml. In: Proceedings of XML, vol 2006Google Scholar
  10. Dijkstra E (1959) A note on two problems in connexion with graphs. Numerische mathematik 1(1):269–271CrossRefGoogle Scholar
  11. Fredman ML, Tarjan RE (1987) Fibonacci heaps and their uses in improved network optimization algorithms. J ACM 34(3):596–615CrossRefGoogle Scholar
  12. Gurobi Optimization: Gurobi Optimizer. (2013)
  13. Haak S, Blau B (2012) Efficient QoS aggregation in service value networks. In: Proceedings of the forty-fifth annual Hawaii international conferenceon system sciences. Grand Wailea, MauiGoogle Scholar
  14. Haak S, Grimm S (2011) Towards custom cloud services—using semantic technology to optimize resource configuration. In: Proceedings of the 8th extended semantic web conference, ESWC 2011, Heraklion, Crete, Greece, 29 May–2 June, 2011. Springer, Heraklion, Crete, GreeceGoogle Scholar
  15. Jaeger M, Rojec-Goldmann G, Muhl G (2004) Qos aggregation for web service composition using workflow patterns. In: Enterprise distributed object computing conference, 2004. EDOC 2004. Proceedings. Eighth IEEE international. IEEE, pp 149–159Google Scholar
  16. Knapper R, Blau B, Speiser S, Conte T, Weinhardt C (2010) Service contract automation. AMCIS 2010 proceedingsGoogle Scholar
  17. Lécué F, Léger A (2006) A formal model for web service composition. In: Proceeding of the 2006 conference on leading the Web in concurrent engineering. IOS Press, Amsterdam, The Netherlands, pp 37–46Google Scholar
  18. Ludwig A, Franczyk B (2008) Cosma—an approach for managing slas in composite services. Service-oriented computing–ICSOC 2008, pp 626–632Google Scholar
  19. MacKenzie C et al (2006) Reference model for service oriented architecture. Public Rev Draft. Accessed 2 Aug 2006
  20. Muthusamy V, Jacobsen H, Chau T, Chan A, Coulthard P (2009) Sla-driven business process management in soa. In: Proceedings of the 2009 conference of the Center for Advanced Studies on Collaborative Research. ACM, pp 86–100Google Scholar
  21. Pautasso C, Zimmermann O, Leymann F (2008) Restful web services vs. big’web services: making the right architectural decision. In: Proceeding of the 17th international conference on World Wide Web. ACM, pp 805–814Google Scholar
  22. Richardson L, Ruby S (2007) RESTful web services. O’Reilly Media, SebastopolGoogle Scholar
  23. Rushton A, Carson D (1985) The marketing of services: managing the intangibles. Eur J Mark 19(3):19–40CrossRefGoogle Scholar
  24. Sirin E, Parsia B, Wu D, Hendler J, Nau D (2004) Htn planning for web service composition using shop2. Web Semantics Sci Serv Agents World Wide Web 1(4):377–396CrossRefGoogle Scholar
  25. Unger T, Leymann F, Mauchart S, Scheibler T (2008) Aggregation of service level agreements in the context of business processes. In: Enterprise distributed object computing conference, 2008. EDOC’08. 12th International IEEE. IEEE, pp 43–52Google Scholar
  26. Zeng L, Benatallah B, Ngu A, Dumas M, Kalagnanam J, Chang H (2004) Qos-aware middleware for web services composition. Softw Eng IEEE Trans 30(5):311–327CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.IPEResearch Center for Information Technology (FZI)KarlsruheGermany
  2. 2.IISMKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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