Long-Term Qualitative IaaS Composition

User preferences are one of the key research subjects in developing personalized applications [126]. In many real life service composition scenarios, the target is to achieve the desired functional goal while ensuring user-provided preferences. For example, a travel planner usually composes services from different transportation and accommodation services. The functional goal of the planner is to find a trip from a source to a destination for its users. However, such a composition usually takes into account user preferences such as total costs, journey times and modes of transportation. A user may specify that he/she is flexible on tour dates but wishes to travel on business class or on a flight with a lower price.

[1]  JungKun Park,et al.  M‐loyalty: winning strategies for mobile carriers , 2006 .

[2]  Wei Jiang,et al.  Large-Scale Longitudinal Analysis of SOAP-Based and RESTful Web Services , 2012, 2012 IEEE 19th International Conference on Web Services.

[3]  F. Lewis,et al.  Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers , 2012, IEEE Control Systems.

[4]  Guy Shani,et al.  An MDP-Based Recommender System , 2002, J. Mach. Learn. Res..

[5]  Tram Truong Huu,et al.  A Game-Theoretic Model for Dynamic Pricing and Competition among Cloud Providers , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[6]  Marc Toussaint,et al.  Hierarchical Monte-Carlo Planning , 2015, AAAI.

[7]  Athman Bouguettaya,et al.  QoS-Aware Cloud Service Composition Based on Economic Models , 2012, ICSOC.

[8]  Christof Weinhardt,et al.  Towards an Efficient Decision Policy for Cloud Service Providers , 2010, ICIS.

[9]  Jordi Torres,et al.  Economic model of a Cloud provider operating in a federated Cloud , 2012, Inf. Syst. Frontiers.

[10]  Athman Bouguettaya,et al.  Preference recommendation for personalized search , 2016, Knowl. Based Syst..

[11]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[12]  Luca Maria Gambardella,et al.  Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem , 1995, ICML.

[13]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[14]  Sheryl E. Kimes,et al.  Restaurant Revenue Management at Chevys: Determining the Best Table Mix , 2004, Decis. Sci..

[15]  Xiang Zhou,et al.  WCP-Nets: A Weighted Extension to CP-Nets for Web Service Selection , 2012, ICSOC.

[16]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[17]  Xiang Zhou,et al.  Adaptive Service Composition Based on Reinforcement Learning , 2010, ICSOC.

[18]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[19]  G. Fasano,et al.  A multidimensional version of the Kolmogorov–Smirnov test , 1987 .

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

[21]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[22]  Ronen I. Brafman,et al.  CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements , 2011, J. Artif. Intell. Res..