A CP-Net Based Qualitative Composition Approach for an IaaS Provider

We propose a novel CP-Net based composition approach to qualitatively select an optimal set of consumers for an IaaS provider. The IaaS provider’s and consumers’ qualitative preferences are captured using CP-Nets. We propose a CP-Net composability model using the semantic congruence property of a qualitative composition. A greedy-based and a heuristic-based consumer selection approaches are proposed that effectively reduce the search space of candidate consumers in the composition. Experimental results prove the feasibility of the proposed composition approach.

[1]  Xin Lin,et al.  Incorporating both qualitative and quantitative preferences for service recommendation , 2018, J. Parallel Distributed Comput..

[2]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[3]  Siamak Mohammadi,et al.  Hypervisor and Neighbors’ Noise: Performance Degradation in Virtualized Environments , 2018, IEEE Transactions on Services Computing.

[4]  Athman Bouguettaya,et al.  Ev-LCS: A System for the Evolution of Long-Term Composed Services , 2013, IEEE Transactions on Services Computing.

[5]  Abdelkarim Erradi,et al.  Qualitative Economic Model for Long-Term IaaS Composition , 2016, ICSOC.

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

[7]  Djamel Belaïd,et al.  A quantitative model for user preferences based on qualitative specifications , 2009, ICPS '09.

[8]  Athman Bouguettaya,et al.  Web Service Selection with Incomplete or Inconsistent User Preferences , 2009, ICSOC/ServiceWave.

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

[10]  Jie Zhang,et al.  Measuring similarity of users with qualitative preferences for service selection , 2017, Knowledge and Information Systems.

[11]  Lirong Xia,et al.  Sequential composition of voting rules in multi-issue domains , 2009, Math. Soc. Sci..

[12]  Hai Dong,et al.  Long-Term QoS-Aware Cloud Service Composition Using Multivariate Time Series Analysis , 2016, IEEE Transactions on Services Computing.

[13]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[14]  Toby Walsh,et al.  mCP Nets: Representing and Reasoning with Preferences of Multiple Agents , 2004, AAAI.

[15]  Ryszard Kowalczyk,et al.  Aggregating multi-valued CP-nets: a CSP-based approach , 2014, Journal of Heuristics.

[16]  K. Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[17]  Abdelkarim Erradi,et al.  Probabilistic Qualitative Preference Matching in Long-Term IaaS Composition , 2017, ICSOC.

[18]  Hai Dong,et al.  Metaheuristic Optimization for Long-term IaaS Service Composition , 2018, IEEE Transactions on Services Computing.

[19]  Li Li,et al.  Semantic based aspect-oriented programming for context-aware Web service composition , 2011, Inf. Syst..

[20]  Wolf-Tilo Balke,et al.  Towards Personalized Selection of Web Services , 2003, WWW.

[21]  Hai Dong,et al.  Predicting Dynamic Requests Behavior in Long-Term IaaS Service Composition , 2015, 2015 IEEE International Conference on Web Services.