Operations Research
Probabilistic inference and influence diagrams
Operations Research
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Composing Web services on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Current Solutions for Web Service Composition
IEEE Internet Computing
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Using Semantics for Policy-Based Web Service Composition
Distributed and Parallel Databases
Deploying and managing Web services: issues, solutions, and directions
The VLDB Journal — The International Journal on Very Large Data Bases
Workflow-based resource allocation to optimize overall performance of composite services
Future Generation Computer Systems
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
An Economic Model for Self-Tuned Cloud Caching
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Genetic algorithm based QoS-aware service compositions in cloud computing
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Optimal Service Pricing for a Cloud Cache
IEEE Transactions on Knowledge and Data Engineering
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Cloud service composition is usually long term based and economically driven. We consider cloud service composition from a user-based perspective. Specifically, the contributions are shown in three aspects. We propose to use discrete Bayesian Network to represent the economic model of end users. The cloud service composition problem is modeled as an Influence Diagram problem. A novel influence-diagram-based cloud service composition approach is proposed. Analytical and simulational results are presented to show the performance of the proposed composition approach.