Policy-Aware Language Service Composition

  • Trang Mai Xuan
  • Yohei Murakami
  • Toru Ishida
Part of the Cognitive Technologies book series (COGTECH)


Many language resources are being shared as web services to process data on the Internet. As dataset size keeps growing, language services are experiencing more big data problems, such as the storage and processing overheads caused by the huge amounts of multilingual texts. Parallel execution and cloud technologies are the keys to making service invocation practical. In the Service-Oriented Architecture approach, service providers typically employ policies to limit parallel execution of the services based on arbitrary decisions. In order to attain optimal performance, users need to adapt to the services policies. A composite service is a combination of several atomic services provided by various providers. To use parallel execution for greater composite service efficiency, the degree of parallelism (DOP) of the composite services need to be optimized by considering the policies of all atomic services. We propose a model that embeds service policies into formulae and permits composite service performance to be calculated. From the calculation results, we can predict the optimal DOP for the composite service that allows the best performance to be attained. Extensive experiments are conducted on real-world translation services. The analysis results show that our proposed model has good prediction accuracy in identifying optimal DOPs for composite services.


Parallel execution policy Performance prediction Degree of parallelism 



This research was partly supported by a Grant-in-Aid for Scientific Research (S) (24220002, 2012-2016) and a Grant-in-Aid for Young Scientists (A) (17H04706, 2017-2020) from Japan Society for the Promotion of Science (JSPS).


  1. 1.
    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18–20, 1967, Spring Joint Computer Conference, AFIPS ’67 (Spring), pp. 483–485. ACM, New York, NY, USA (1967)Google Scholar
  2. 2.
    Tallent, N.R., Mellor-Crummey, J.M.: Effective performance measurement and analysis of multithreaded applications. SIGPLAN Not. 44(4), 229–240 (2009)CrossRefGoogle Scholar
  3. 3.
    Raicu, I., Foster, I., Zhao, Y., Szalay, A., Little, P., Moretti, C.M., Chaudhary, A., Thain, D.: Towards data intensive many-task computing. In: Data Intensive Distributed Computing: Challenges and Solutions for Largescale Information Management, vol. 13, no. 3, pp. 28–73 (2012)Google Scholar
  4. 4.
    Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M.: Workflows for e-Science: scientific workflows for grids. Springer Publishing Company, Incorporated (2014)Google Scholar
  5. 5.
    Pautasso, C., Alonso, G.: Parallel computing patterns for grid workflows. In: 2006 Workshop on Workflows in Support of Large-Scale Science, pp. 1–10 (2006)Google Scholar
  6. 6.
    de Oliveira, D., Ogasawara, E., Ocaa, K., Baio, F., Mattoso, M.: An adaptive parallel execution strategy for cloud-based scientific workflows. Concurrency Comput. Pract. Experience 24(13), 1531–1550 (2012)CrossRefGoogle Scholar
  7. 7.
    Ishida, T. (ed.): The Language Grid: Service-Oriented Collective Intelligence for Language Resource Interoperability. Springer Science & Business Media (2011)Google Scholar
  8. 8.
    Murakami, Y., Lin, D., Ishida, T.: Service-Oriented Architecture for Interoperability of Multilanguage Services, pp. 313–328. Springer, Berlin (2014)Google Scholar
  9. 9.
    Sun, X.H., Chen, Y.: Reevaluating Amdahl’ s law in the multicore era. J. Parallel Distrib. Comput. 70(2), 183–188 (2010)CrossRefzbMATHGoogle Scholar
  10. 10.
    Trang, M., Murakami, Y., Ishida, T.: Policy-aware optimization of parallel execution of composite services. IEEE Trans. Serv. Comput. PP(99), 109–113 (2017)Google Scholar
  11. 11.
    Cardoso, J., Sheth, A., Miller, J., Arnold, J., Kochut, K.: Quality of service for workflows and web service processes. Web Semant. Sci. Serv. Agents World Wide Web 1(3), 281–308 (2004)CrossRefGoogle Scholar
  12. 12.
    Yu, Q., Bouguettaya, A.: Framework for web service query algebra and optimization. ACM Trans. Web 2(1), 6:1–6:35 (2008)CrossRefGoogle Scholar
  13. 13.
    Xuan, T.M., Murakami, Y., Lin, D., Ishida, T.: Integration of workflow and pipeline for language service composition. In: Chair, N.C.C., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 3829–3836. European Language Resources Association (ELRA), Reykjavik, Iceland (2014)Google Scholar
  14. 14.
    Oinn, T., Addis, M., Ferris, J., Marvin, D., Senger, M., Greenwood, M., Carver, T., Glover, K., Pocock, M.R., Wipat, A., Li, P.: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17), 3045–3054 (2004)CrossRefGoogle Scholar
  15. 15.
    Ludscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., Lee, E.A., Tao, J., Zhao, Y.: Scientific workflow management and the kepler system. Concurrency Computat. Pract. Experience 18(10), 1039–1065 (2006)CrossRefGoogle Scholar
  16. 16.
    Yu, J., Buyya, R., Ramamohanarao, K.: Workflow Scheduling Algorithms for Grid Computing, pp. 173–214. Springer, Berlin (2008)Google Scholar
  17. 17.
    Szabo, C., Sheng, Q.Z., Kroeger, T., Zhang, Y., Yu, J.: Science in the cloud: Allocation and execution of data-intensive scientific workflows. J. Grid Comput. 12(2), 245–264 (2014)CrossRefGoogle Scholar
  18. 18.
    Lin, D., Shi, C., Ishida, T.: Dynamic service selection based on context-aware QoS. In: 2012 IEEE Ninth International Conference on Services Computing, pp. 641–648 (2012)Google Scholar
  19. 19.
    Lin, D., Ishida, T., Murakami, Y., Tanaka, M.: Qos analysis for service composition by human and web services. IEICE Trans. Inf. Syst. 97(4), 762–769 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Social InformaticsKyoto UniversityKyotoJapan
  2. 2.Unit of DesignKyoto UniversityKyotoJapan

Personalised recommendations