Advertisement

SEFAP: an efficient approach for ranking skyline web services

  • Abdelaziz OuadahEmail author
  • Allel Hadjali
  • Fahima Nader
  • Karim Benouaret
Original Research

Abstract

With the increasing number of Web services published on the Web, many of services provide the same functionality with different quality of service. Ranking similar web services based on QoS is then an important issue. This paper proposes a hybrid approach to rank-order Skyline Web services, which mixes several methods borrowed from Multi-Criteria Decision Making field. The Skyline method is used to reduce the decision space and focusing only on interesting Web services that are not dominated by any other service. For weighting QoS criteria, we aggregate objective and subjective weights. The objective Entropy weights are extracted directly from invocation history data, however, the subjective weights are calculated using Fuzzy AHP from user opinions. Promethee method is leveraged to rank Skyline Web services, by taking advantage of the outranking relationships between Skyline Web services and generating positive, negative and Net flows. An efficient algorithm to rank-order Skyline Web services on the basis of Net flow is developed. A case study is presented to illustrate the different steps of our approach. The experimental evaluation conducted on real-world datasets demonstrates that our approach can better capture the user preferences and retrieve the best ranked Skyline Web services.

Keywords

Skyline web services Multi-criteria decision making Entropy Fuzzy AHP Promethee User preferences 

References

  1. Alam KA, Ahmad R (2016). A hybrid fuzzy multi-criteria decision model for cloud service selection and importance degree of component services in service compositions. In: Uncertainty modelling in knowledge engineering and decision making. Proceedings of the 12th international FLINS conference (FLINS 2016), vol 10, p 334. World ScientificGoogle Scholar
  2. Albadvi A, Chaharsooghi SK, Esfahanipour A (2007) Decision making in stock trading: an application of PROMETHEE. Eur J Oper Res 177(2):673–683.  https://doi.org/10.1016/J.EJOR.2005.11.022 CrossRefzbMATHGoogle Scholar
  3. Al-Masri E, Mahmoud QH (2007a) Qos-based discovery and ranking of web services. Comput Commun Netw. In: ICCCN 2007. Proceedings of 16th international conference, IEEE, pp 529–534.  https://doi.org/10.1109/ICCCN.2007.4317873
  4. Al-Masri E, Mahmoud QH (2007b). Crawling multiple UDDI business registries. In Proceedings of the 16th international conference on world wide web, ACM, pp 1255–1256.  https://doi.org/10.1145/1242572.1242794
  5. Almulla M, Almatori K, Yahyaoui H (2011) A QoS-based fuzzy model for ranking real world web services. In: Web services (ICWS), 2011 IEEE international conference, IEEE, pp 203–210.  https://doi.org/10.1109/ICWS.2011.43
  6. Almulla M, Yahyaoui H, Al-Matori K (2015) A new fuzzy hybrid technique for ranking real world web services. Knowl Based Syst 77:1–15.  https://doi.org/10.1016/j.knosys.2014.12.021 CrossRefGoogle Scholar
  7. Alrifai M, Skoutas D, Risse T (2010) Selecting skyline services for QoS-based web service composition. In: Proceedings of the 19th international conference on world wide web, ACM, pp 11–20.  https://doi.org/10.1145/1772690.1772693
  8. Balali V, Zahraie B, Roozbahani A (2014) A comparison of AHP and PROMETHEE family decision making methods for selection of building structural system. Am J Civil Eng Archit 2(5):149–159CrossRefGoogle Scholar
  9. Behzadian M, Kazemzadeh RB, Albadvi A, Aghdasi M (2010) PROMETHEE: a comprehensive literature review on methodologies and applications. Eur J Oper Res 200(1):198–215.  https://doi.org/10.1016/j.ejor.2009.01.021 CrossRefzbMATHGoogle Scholar
  10. Benouaret K (2012). Advanced techniques for web service query optimization. Doctoral dissertation, Université Claude Bernard-Lyon IGoogle Scholar
  11. Benouaret K, Benslimane D, Hadjali A (2012) Selecting skyline web services for multiple users preferences. In: Web services (ICWS), 2012 IEEE 19th international conference, IEEE, pp 635–636.  https://doi.org/10.1109/ICWS.2012.108
  12. Borzsony S, Kossmann D, Stocker K (2001) The skyline operator. Data Eng. In: Proceedings 17th international conference, IEEE, pp 421–430.  https://doi.org/10.1109/ICDE.2001.914855 Google Scholar
  13. Brans JP, Mareschal B (1994) The PROMCALC & GAIA decision support system for multicriteria decision aid. Decis Supp Syst 12(4–5):297–310.  https://doi.org/10.1016/0167-9236(94)90048-5 CrossRefGoogle Scholar
  14. Brans JP, Mareschal B (2005) PROMETHEE methods. In: Multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 163–186Google Scholar
  15. Brans JP, Vincke P, Mareschal B (1986) How to select and how to rank projects: the PROMETHEE method. Eur J Oper Res 24(2):228–238.  https://doi.org/10.1016/0377-2217(86)90044-5 MathSciNetCrossRefzbMATHGoogle Scholar
  16. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247.  https://doi.org/10.1016/0165-0114(85)90090-9 MathSciNetCrossRefzbMATHGoogle Scholar
  17. Chakhar S, Youcef S, Mousseau V, Mokdad L, Haddad S (2011) Multicriteria evaluation-based conceptual framework for composite web service selection. Research report. Lamsade, University Paris Dauphine, FranceGoogle Scholar
  18. Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95:649–655.  https://doi.org/10.1016/0377-2217(95)00300-2 CrossRefzbMATHGoogle Scholar
  19. Chouiref Z, Belkhir A, Benouaret K, Hadjali A (2016) A fuzzy framework for efficient user-centric web service selection. Appl Soft Comput 41:51–65.  https://doi.org/10.1016/j.asoc.2015.12.011 CrossRefGoogle Scholar
  20. Dai G, Qingsheng Z (2013) Using skyline and dominance relationship for web ranking services. Newsp Comput Inf Syst 9(10):3977–3984Google Scholar
  21. Du Y, Hu H, Song W, Ding J, Lü J (2015) Efficient computing composite service skyline with QoS correlations. In: Services computing (SCC), 2015 IEEE international conference, IEEE, pp 41–48.  https://doi.org/10.1109/SCC.2015.16
  22. Fletcher KK, Liu XF, Tang M (2015) Elastic personalized nonfunctional attribute preference and trade-off based service selection. ACM Trans Web TWEB 9(1):1.  https://doi.org/10.1145/2697389 CrossRefGoogle Scholar
  23. Godse M, Sonar R, Mulik S (2008) The analytical hierarchy process approach for prioritizing features in the selection of web service. In: Web services. ECOWS’08. IEEE sixth European conference, IEEE, pp 41–50.  https://doi.org/10.1109/ECOWS.2008.21
  24. Hao F, Pei Z, Park DS, Phonexay V, Seo HS (2017). Mobile cloud services recommendation: a soft set-based approach. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-017-0572-7 Google Scholar
  25. Herssens C, Jureta I, Faulkner S (2008) Dealing with quality tradeoffs during service selection. IAG–LSM working papers.  https://doi.org/10.1109/ICAC.2008.8
  26. Hyde K, Maier HR, Colby C (2003) Incorporating uncertainty in the PROMETHEE MCDA method. J Multi Criteria Decis Anal 12(4–5):245–259CrossRefGoogle Scholar
  27. Jarvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst TOIS 20(4):422–446.  https://doi.org/10.1145/582415.582418 CrossRefGoogle Scholar
  28. Kangas A, Kangas J, Pykäläinen J (2001) Outranking methods as tools in strategic natural resources planningGoogle Scholar
  29. Karim R, Ding C, Chi CH (2011) An enhanced PROMETHEE model for QoS-based Web service selection. In: Services computing (SCC), IEEE international conference, IEEE, pp 536–543.  https://doi.org/10.1109/SCC.2011.81
  30. Kumar RR, Kumar C (2018) A multicriteria decision-making method for cloud service selection and ranking. Adv Comput Comput Sci. Springer, Singapore, pp 139–147.  https://doi.org/10.1007/978-981-10-3773-3_14
  31. Kumar RR, Mishra S, Kumar C (2017). Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment. J Supercomput  https://doi.org/10.1007/s11227-017-2039-1 Google Scholar
  32. Kwong CK, Bai H (2002) A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. J Intell Manuf 13(5):367–377CrossRefGoogle Scholar
  33. Laarhoven Van PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11(1–3):229–241.  https://doi.org/10.1016/S0165-0114(83)80082-7 MathSciNetCrossRefzbMATHGoogle Scholar
  34. Lin CL, Shih YH, Tzeng GH, Yu HC (2016) A service selection model for digital music service platforms using a hybrid MCDM approach. Appl Soft Comput 48:385–403.  https://doi.org/10.1016/j.asoc.2016.05.035 CrossRefGoogle Scholar
  35. Ma Y, Wang S, Sun Q, Zou H, Yang F (2013) Web services QoS measure based on subjective and objective weight. In: Services computing (SCC), 2013 IEEE international conference, IEEE, pp 543–550.  https://doi.org/10.1109/SCC.2013.10
  36. Macharis C, Springael J, De Brucker K, Verbeke A (2004) PROMETHEE and AHP: the design of operational synergies in multicriteria analysis: strengthening PROMETHEE with ideas of AHP. Eur J Oper Res 153(2):307–317.  https://doi.org/10.1016/S0377-2217(03)00153-X CrossRefzbMATHGoogle Scholar
  37. Marinoni O (2006) A discussion on the computational limitations of outranking methods for land-use suitability assessment. Int J Geogr Inf Sci 20(1):69–87.  https://doi.org/10.1080/13658810500287040 MathSciNetCrossRefGoogle Scholar
  38. Mobedpour D, Ding C (2013) User-centered design of a QoS-based Web service selection system. SOCA 7(2):117–127CrossRefGoogle Scholar
  39. Ouadah A, Benouaret K, Hadjali A, Nader F (2015a) Combining skyline and multi-criteria decision methods to enhance Web services selection. In: Programming and systems (ISPS), 2015 12th international symposium, IEEE, pp 1–8.  https://doi.org/10.1109/ISPS.2015.7244975
  40. Ouadah A, Benouaret K, Hadjali A, Nader F (2015b) Skyap-s3: a hybrid approach for efficient skyline services selection. In: Service-oriented computing and applications (SOCA), 2015 IEEE 8th international conference, IEEE, pp 18–25.  https://doi.org/10.1109/SOCA.2015.22
  41. Papadias D, Tao Y, Fu G, Seeger B (2003) An optimal and progressive algorithm for skyline queries. In: Proceedings of the 2003 ACM SIGMOD international conference on Management of data, ACM, pp 467–478.  https://doi.org/10.1145/872757.872814
  42. Rehman urZ, Hussain OK, Hussain FK (2014) Parallel cloud service selection and ranking based on QoS history. Int J Parallel Prog 42(5):820–852CrossRefGoogle Scholar
  43. Roy B, Figueira JR, Almeida-Dias J (2014) Discriminating thresholds as a tool to cope with imperfect knowledge in multiple criteria decision aiding: theoretical results and practical issues. Omega 43:9–20.  https://doi.org/10.1016/j.omega.2013.05.003 CrossRefGoogle Scholar
  44. Saaty RW (1987) The analytic hierarchy process–what it simple percentage and how it used simple percentage. Math Model 9(3):161–176CrossRefzbMATHGoogle Scholar
  45. Seo YJ, Jeong HY, Song YJ (2004) A study on web services selection method based on the negotiation through quality broker: a maut-based approach. ICESS.  https://doi.org/10.1007/11535409_9 Google Scholar
  46. Seo YJ, Jeong HY, Song YJ (2005) Best web service selection based on the decision making between qos criteria of service. ICESS 5:408–419.  https://doi.org/10.1007/11599555_39 Google Scholar
  47. Serrai W, Abdelli A, Mokdad L, Hammal Y (2016) An efficient approach for web service selection. Comput Commun (ISCC), 2016 IEEE symposium, IEEE, pp 167–172.  https://doi.org/10.1109/ISCC.2016.7543734
  48. Serrai W, Abdelli A, Mokdad L, Hammal Y (2017) Towards an efficient and a more accurate web service selection using MCDM methods. J Comput Sci.  https://doi.org/10.1016/j.jocs.2017.05.024 Google Scholar
  49. Shannon CE (1948) A mathematical theory of communication, part I, part II. Bell Syst Tech J 27:623–656.  https://doi.org/10.1002/j.1538-7305.1948.tb01338.x CrossRefzbMATHGoogle Scholar
  50. Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized qos prediction for web services via collaborative filtering. In: Web services. ICWS 2007. IEEE international conference, IEEE, pp 439–446.  https://doi.org/10.1109/ICWS.2007.140
  51. Siala F, Ghedira K (2014) How to select dynamically a QoS-driven composite web service by a multi-agent system using CBR method. Int J Wirel Mob Comput 7(4):327–347.  https://doi.org/10.1504/IJWMC.2014.063054 CrossRefGoogle Scholar
  52. Sun L, Dong H, Hussain FK, Hussain OK, Ma J, Zhang Y (2014) A hybrid fuzzy framework for cloud service selection. In: Web services (ICWS), 2014 IEEE international conference, IEEE, pp 313–320.  https://doi.org/10.1109/ICWS.2014.53
  53. Sun R, Zhang B, Liu T (2016) Ranking web service for high quality by applying improved entropy—TOPSIS method. In: Software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), 2016 17th IEEE/ACIS international conference, IEEE, pp 249–254.  https://doi.org/10.1109/SNPD.2016.7515909
  54. Tan KL, Eng PK, Ooi BC (2001) Efficient progressive skyline computation. VLDB 1:301–310Google Scholar
  55. Tang YC (2009) An approach to budget allocation for an aerospace company—fuzzy analytic hierarchy process and artificial neural network. Neurocomputing 72:3477–3489.  https://doi.org/10.1016/j.neucom.2009.03.020 CrossRefGoogle Scholar
  56. Vesyropoulos N, Georgiadis CK (2015) QoS-based filters in web service compositions: utilizing multi-criteria decision analysis methods. J Multi Criteria Decis Anal 22(5–6):279–292.  https://doi.org/10.1002/mcda.1538 CrossRefGoogle Scholar
  57. Vincke Ph (1992) Multi-criteria decision aid. Wiley, Hoboken, p 154Google Scholar
  58. Vlachou A, Vazirgiannis M (2010) Ranking the sky: discovering the importance of skyline points through subspace dominance relationships. Data Knowl Eng 69(9):943–964.  https://doi.org/10.1016/j.datak.2010.03.008 CrossRefGoogle Scholar
  59. Wang JJ, Yang DL (2007) Using a hybrid multi-criteria decision aid method for information systems outsourcing. Comput Oper Res 34(12):3691–3700.  https://doi.org/10.1016/j.cor.2006.01.017 CrossRefzbMATHGoogle Scholar
  60. Wang P, Chao KM, Lo CC, Huang CL, Li Y (2006) A fuzzy model for selection of QoS-aware web services. e-Bus Eng. ICEBE’06. IEEE international conference, IEEE, pp 585–593.  https://doi.org/10.1109/ICEBE.2006.3
  61. Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT (2014) Cloud service selection using multicriteria decision analysis. Sci World J.  https://doi.org/10.1155/2014/459375 Google Scholar
  62. Xiong P, Fan Y (2007) Qos-aware web service selection by a synthetic weight. Fuzzy Syst Knowl Discov. FSKD 2007. Fourth international conference, IEEE, vol 3, pp 632–637.  https://doi.org/10.1109/FSKD.2007.462 Google Scholar
  63. Yu Q, Bouguettaya A (2009) Foundations for efficient web service selection. Springer, BerlinGoogle Scholar
  64. Yu Q, Bouguettaya A (2010) Computing service skyline from uncertain qows. IEEE Trans Serv Comput 3(1):16–29.  https://doi.org/10.1109/TSC.2010.7 CrossRefGoogle Scholar
  65. Zadeh LA (1999) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 100:9–34.  https://doi.org/10.1016/S0165-0114(99)80004-9 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratoire des Méthodes de Conception des Systèmes, Ecole Nationale Supérieure d’InformatiqueOued-Smar, AlgerAlgérie
  2. 2.LIAS, ISAE-ENSMAPoitierFrance
  3. 3.LIRIS, Université Claude Bernard Lyon 1LyonFrance

Personalised recommendations