Introducing test data-set for the QoS-aware web-services discovery and composition

Document Type : Research Paper


Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran


The QoS-aware web services discovery and composition are considered as two important, open and hot issues in Service-Oriented Architecture (SOA). By definition, web-service discovery is about how to select the best web-service for a role in a workflow among available web-services whereas web-services composition works on developing merely optimum coordination among a number of available web-services to provide a new composed web-service intended to satisfy some users requirements for which a single web service is not (good) enough. The criteria, upon which the web service selection, position, and composition processes are based, may or may not cover the Quality of services (QoS) parameters. The latter case would turn the name of the job into QoS-aware web services discovery and composition. In this article, the QoS-WSC test data-set is introduced for the QoS-aware web-services discovery and composition with regard to specific potentials and needs of the web-services world. In this respect, at first, an interface has been designed to define QoS for the roles attended in a service-oriented architecture. Then a solution, which allows comparison among web-services through the calculation of similarity of the request to amounts of the QoS parameters of available web services, is proposed. The similarity is obtained using the internal multiplication of two vectors of weighted numerical parameters representing request parameters and QoS parameters of available web services. The weights are technically significant coefficients, which can be obtained from the QoS-WSC data-set, which is assembled out of a rule-based integration of two well-known prior data sets in the field of web services.


[1] E. Al-Masri and Q. H. Mahmoud, Investigating web services on the world wide web, Proc.e 17th Int. Conf. World
Wide Web 2008, WWW’08, 2008, pp. 795–804.
[2] S. Araban and L. Sterling, Quality of service for web services, WSEAS Trans. Comput. 3 (2004).
[3] P. Bocciarelli and A. D’Ambrogio, A model-driven method for describing and predicting the reliability of composite
services, Softw. Syst. Model. 10(2) (2011) 265—280.
[4] S. Chattopadhyay, A. Banerjee and N. Banerjee, A fast and scalable mechanism for web service composition,
ACM Trans. Web, 11(4) (2017) 1—36.
[5] F. Chen, S. Yuan and B. Mu, User-QoS-based web service clustering for QoS prediction, Proc. 2015 IEEE Int.
Conf. Web Services, ICWS 2015, 2015, pp. 583–590.
[6] A. D’Ambrogio and Andrea, A Model-driven WSDL Extension for Describing the QoS ofWeb Services, 2006
IEEE Int. Conf. Web Services (ICWS’06), 2006, pp. 789–796.
[7] A. D’Ambrogio, “Model-Driven Quality Engineering of Service-Based Systems,” Springer, Berlin, Heidelberg,
2010, pp. 81–103.
[8] A. D’Ambrogio, A WSDL extension for performance-enabled description of web services, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
2005, vol. 3733 LNCS, pp. 371–381.
[9] A. D’Ambrogio, A model-driven WSDL extension for describing the QoS of web services, Proc-ICWS 2006: 2006
IEEE Int. Conf. Web Services, 2006, pp. 789–796.
[10] M. K. Dehnoi and S. Araban, “Automatic qos-aware web services composition based on set-cover problem,” Int.
J. Nonlinear Anal. Appl., vol. 12, no. 1, pp. 87–109, Feb. 2020.
[11] D. Z. G. Garcia and M. B. F. De Toledo, Semantics-enriched QoS policies for web service interactions, ACM
International Conference Proceeding Series, 2006, vol. 192, pp. 35–44.
[12] ICEBE 2020.” [Online]. Available: [Accessed: 16-Feb2020].
[13] ISO/IEC 13236:1998 Information technology - Quality of service: Framework. 1998.
[14] M. Khani and S. Araban, QoS-WSC, Mendeley Data, 2020. [Online]. Available:
[15] M. K. Dehnoi and M. K. Dehnoi, Fast fault localization in optical WDM networks, 2nd International Congress
on Technology, Communication and Knowledge, ICTCK 2015, 2016, pp. 332–336.
[16] R. Mohana and D. Dahiya, Optimized service discovery using QoS based ranking: A fuzzy clustering and particle
swarm optimization approach, Proc. Int. Computer Software and Applications Conference, 2011, pp. 452–457.
[17] M. A. Moulavi, B. Bahmani, M. Sadeghizadeh, J. A. Nasiri, H. Parvar and M. Naghibzadeh, DHA-KD: Dynamic
hierarchical agent based key distribution in group communication, Proc. 9th ACIS Int. Conf. Software Engineering,
Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 and 2nd Int. Workshop on
Advanced Internet Technology and Applications, 2008, pp. 301–306.
[18] QWS dataset (the qality of service for web services dataset), [Online]. Available:
[Accessed: 16-Feb-2020].
[19] T. Rajendran, P. Balasubramanie and R. Cherian, An efficient WS-QoS broker based architecture for web services
selection, Int. J. Comput. Appl. 1(9) (2010) 79—84.
[20] M. Rathore, M. Rathore and U. Suman, A quality of service broker based process model for dynamic web service
composition, Proc. 3RD Int. Work. Model. Enterp. Inf. Syst. 7 (2011) 1267–1274.[21] M. Sadeghizadeh and O. R. Marouzi, Securing cluster-heads in wireless sensor networks by a hybrid intrusion
detection system based on data mining, J. Commun. Eng. 8(1) (2019) 1–19.
[22] M. Sadeghizadeh and O. R. Marouzi, A lightweight intrusion detection system based on specifications to improve
security in wireless sensor networks, J. Commun. Eng. 7(2) (2018) 29-–60.
[23] V. Tewari, U. Thakar and N. Dagdee, Classifying Web Services based on QoS Parameters using Extended Dataset,
Int. J. Comput. Appl. 74(8) (2013) 33-–36.
[24] A. K. Tripathy, M. R. Patra, M. A. Khan, H. Fatima and P. Swain, Dynamic web service composition with QoS
clustering, Proc. IEEE Int. Conf. Web Serv. ICWS 2014, (2014) 678—679.
[25] H. Wang, B. Zou, G. Guo, D. Yang and J. Zhang, Integrating trust with user preference for effective web service
composition, IEEE Trans. Serv. Comput. 10(4) (2017) 574-–588.
[26] J. Wu, L. Chen, Z. Zheng, M. R. Lyu and Z. Wu, Clustering Web services to facilitate service discovery, Knowl.
Inf. Syst. 38(1) (2014) 207—229.
[27] B. Wu, C. H. Chi and S. Xu, Service selection model based on QoS reference vector, Proc IEEE Congress on
Services, SERVICES 2007, (2007) 270-–277.
[28] Y. Xia, P. Chen, L. Bao, M. Wang and J. Yang, A QoS-aware Web service selection algorithm based on clustering,
Proc. IEEE 9th Int. Conf. Web Serv. ICWS 2011, (2011) 428—435. 
Volume 12, Special Issue
December 2021
Pages 255-263
  • Receive Date: 15 June 2020
  • Revise Date: 07 January 2021
  • Accept Date: 23 January 2021