Selecting Dynamic Skyline Services for QoS-based Service Composition

With the growing adoption of web services on the Internet, service selection becomes an important issue of service-oriented computing (SOC). Appropriate services selection algorithm is the fundamental guarantee to compose complex services from single- function components effectively. The quality of selected component services is crucial for the performance of the service composition. Therefore, it has become a hot issue to select the best services from a set of services with similar functionality. Recently, skyline has been introduced to solve the problem by selecting skyline services as the best candidate services. In this paper, we focus on selecting skyline services in dynamic environment, where new services may appear, original services may invalidate and QoS of services may change. We propose a skyline service model as well as a novel skyline algorithm to maintain dynamic skyline services. An extensive performance study is propoesed to verify the effectiveness and effi ciency of our approach.

[1]  H. T. Kung,et al.  On Finding the Maxima of a Set of Vectors , 1975, JACM.

[2]  Michael J. Carey,et al.  On saying “Enough already!” in SQL , 1997, SIGMOD '97.

[3]  Hanan Samet,et al.  Distance browsing in spatial databases , 1999, TODS.

[4]  Christian Böhm,et al.  Determining the Convex Hull in Large Multidimensional Databases , 2001, DaWaK.

[5]  Mihalis Yannakakis,et al.  Multiobjective query optimization , 2001, PODS '01.

[6]  Donald Kossmann,et al.  Shooting Stars in the Sky: An Online Algorithm for Skyline Queries , 2002, VLDB.

[7]  I. Maros.  Computational Techniques of the Simplex Method , 2002 .

[8]  Mike P. Papazoglou,et al.  Service-oriented computing: concepts, characteristics and directions , 2003, Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003..

[9]  Shuping Ran,et al.  A model for web services discovery with QoS , 2003, SECO.

[10]  Bernhard Seeger,et al.  An optimal and progressive algorithm for skyline queries , 2003, SIGMOD '03.

[11]  Quan Z. Sheng,et al.  Quality driven web services composition , 2003, WWW '03.

[12]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[13]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[14]  Anne H. H. Ngu,et al.  QoS computation and policing in dynamic web service selection , 2004, WWW Alt. '04.

[15]  Abdelhakim Hafid,et al.  A QoS broker based architecture for efficient Web services selection , 2005, IEEE International Conference on Web Services (ICWS'05).

[16]  Jignesh M. Patel,et al.  Efficient Continuous Skyline Computation , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[17]  Yehia Taher,et al.  Towards an Approach forWeb services Substitution , 2006, 2006 10th International Database Engineering and Applications Symposium (IDEAS'06).

[18]  Junfeng Zhao,et al.  Personalized QoS Prediction forWeb Services via Collaborative Filtering , 2007, IEEE International Conference on Web Services (ICWS 2007).

[19]  Danilo Ardagna,et al.  Adaptive Service Composition in Flexible Processes , 2007, IEEE Transactions on Software Engineering.

[20]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[21]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[22]  Mike P. Papazoglou,et al.  Service-Oriented Computing , 2008 .

[23]  Thomas Risse,et al.  Combining global optimization with local selection for efficient QoS-aware service composition , 2009, WWW '09.

[24]  Vasant Honavar,et al.  Web Service Substitution Based on Preferences Over Non-functional Attributes , 2009, 2009 IEEE International Conference on Services Computing.

[25]  Arindam Banerjee,et al.  Generalized Probabilistic Matrix Factorizations for Collaborative Filtering , 2010, 2010 IEEE International Conference on Data Mining.

[26]  Liang Chen,et al.  Recommendation on Uncertain Services , 2010, 2010 IEEE International Conference on Web Services.

[27]  Xi Chen,et al.  RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation , 2010, 2010 IEEE International Conference on Web Services.

[28]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[29]  Zibin Zheng,et al.  Distributed QoS Evaluation for Real-World Web Services , 2010, 2010 IEEE International Conference on Web Services.

[30]  Athman Bouguettaya,et al.  Computing Service Skyline from Uncertain QoWS , 2010, IEEE Transactions on Services Computing.

[31]  Thomas Risse,et al.  Selecting skyline services for QoS-based web service composition , 2010, WWW '10.

[32]  Zibin Zheng,et al.  Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing , 2011, 2011 IEEE 30th International Symposium on Reliable Distributed Systems.

[33]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[34]  Stephen Dawson,et al.  Markovian Workload Characterization for QoS Prediction in the Cloud , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[35]  Zibin Zheng,et al.  An Enhanced QoS Prediction Approach for Service Selection , 2011, 2011 IEEE International Conference on Services Computing.

[36]  Zibin Zheng,et al.  QoS-Aware Web Service Recommendation by Collaborative Filtering , 2011, IEEE Transactions on Services Computing.

[37]  Zhaohui Wu,et al.  An Extended Matrix Factorization Approach for QoS Prediction in Service Selection , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[38]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.

[39]  Zibin Zheng,et al.  Predicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.