QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
WS Binder: a framework to enable dynamic binding of composite web services
Proceedings of the 2006 international workshop on Service-oriented software engineering
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Discovering the best web service
Proceedings of the 16th international conference on World Wide Web
QoS-aware Service Composition Based on Tree-Coded Genetic Algorithm
COMPSAC '07 Proceedings of the 31st Annual International Computer Software and Applications Conference - Volume 01
Journal of Systems and Software
Using genetic algorithm to implement cost-driven web service selection
Multiagent and Grid Systems - Special Issue on Nature inspired systems for parallel, asynchronous and decentralised environments
Genetic algorithm-based optimization of service composition and deployment
Proceedings of the 3rd international workshop on Services integration in pervasive environments
Efficient semantic service discovery in pervasive computing environments
Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware
A semantic end-to-end QoS model for dynamic service oriented environments
PESOS '09 Proceedings of the 2009 ICSE Workshop on Principles of Engineering Service Oriented Systems
An adaptive approach for QoS-aware web service composition using cultural algorithms
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Workflow pattern analysis in web services orchestration: the BPEL4WS example
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Flexible binding for reusable composition of web services
SC'05 Proceedings of the 4th international conference on Software Composition
A novel genetic algorithm for qos-aware web services selection
DEECS'06 Proceedings of the Second international conference on Data Engineering Issues in E-Commerce and Services
QoS-aware service-oriented middleware for pervasive environments
Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware
Self-optimisation of the energy footprint in service-oriented architectures
Proceedings of the 1st Workshop on Green Computing
Challenges of satisfying multiple stakeholders: quality of service in the internet of things
Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications
Software—Practice & Experience
MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Prediction of atomic web services reliability based on k-means clustering
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Semantic-based QoS management in cloud systems: Current status and future challenges
Future Generation Computer Systems
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QoS-aware service composition is a key requirement in Service Oriented Computing (SOC) since it enables fulfilling complex user tasks while meeting Quality of Service (QoS) constraints. A challenging issue towards this purpose is the selection of the best set of services to compose, meeting global QoS constraints imposed by the user, which is known to be a NP-hard problem. This challenge becomes even more relevant when it is considered in the context of dynamic service environments. Indeed, two specific issues arise. First, required tasks are fulfilled on the fly, thus the time available for services' selection and composition is limited. Second, service compositions have to be adaptive so that they can cope with changing conditions of the environment. In this paper, we present an efficient service selection algorithm that provides the appropriate ground for QoS-aware composition in dynamic service environments. Our algorithm is formed as a guided heuristic. The paper also presents a set of experiments conducted to evaluate the efficiency of our algorithm, which shows its timeliness and optimality.