Job Scheduling Under the Portable Batch System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Value Webs: Using Ontologies to Bundle Real-World Services
IEEE Intelligent Systems
A survey on web services composition
International Journal of Web and Grid Services
Mobile Service Bundles: The Example of Navigation Services
Electronic Markets - 'eValues'
A Markov-based collaborative pricing system for information goods bundling
Expert Systems with Applications: An International Journal
RE '08 Proceedings of the 2008 16th IEEE International Requirements Engineering Conference
Combining global optimization with local selection for efficient QoS-aware service composition
Proceedings of the 18th international conference on World wide web
A Model for Designing Generic Services
SCC '09 Proceedings of the 2009 IEEE International Conference on Services Computing
A Case Study on Bi-lateral Resource Integration Oriented Marine Logistics Service System
SCC '09 Proceedings of the 2009 IEEE International Conference on Services Computing
Service-Level Agreements for Electronic Services
IEEE Transactions on Software Engineering
A Service Composition Approach for the Fulfillment of Temporally Sequential Requirements
SERVICES '10 Proceedings of the 2010 6th World Congress on Services
Conceptualizing a bottom-up approach to service bundling
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Knowledge sharing in dynamic virtual enterprises: A socio-technological perspective
Knowledge-Based Systems
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We present a sharing-oriented service selection and scheduling approach capable of finding a trade-off between requirement satisfaction degree, service utilization rate and service sharing cost for limited quantities and capacities of available services. In traditional service selection approaches, each customer requirement is independently satisfied by optimally selecting a set of candidate service resources. However, in real-life service scenarios, it is usual for multiple customers to raise their requirements simultaneously, and available services need to be allocated between them. Especially, when available services are limited in both quantity and capacity, a traditional "first-come-first-serve" strategy would lead to a low service utilization rate, and some requirements cannot be satisfied at all (i.e., a low requirement satisfaction degree). Our approach makes use of the feature that some services can be shared by several customer requirements. Specifically, a virtualized service resource consisting of multiple candidate services is constructed and scheduled to satisfy multiple customer requirements simultaneously. Our approach searches for the global optimization on requirement satisfaction degree, service utilization rate, and service sharing cost. We build a mathematical model for this multi-objective optimization problem and propose a nested genetic algorithm mixed with a greedy strategy. Experiments in an ocean transportation service setting are conducted and our approach is compared with traditional approaches to validate its effectiveness.