Future Generation Computer Systems - Special issue on metacomputing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
SETI@home: an experiment in public-resource computing
Communications of the ACM
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
Cost-Based Scheduling of Scientific Workflow Application on Utility Grids
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Concurrency and Computation: Practice & Experience - Second International Workshop on Emerging Technologies for Next-generation GRID (ETNGRID 2005)
Predictive performance modelling of parallel component compositions
Cluster Computing
QoS Scheduling Algorithm Based on Hybrid Particle Swarm Optimization Strategy for Grid Workflow
GCC '07 Proceedings of the Sixth International Conference on Grid and Cooperative Computing
Bi-criteria Scheduling of Scientific Workflows for the Grid
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Multiobjective differential evolution for workflow execution on grids
Proceedings of the 5th international workshop on Middleware for grid computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
Performance modeling of parallel applications for grid scheduling
Journal of Parallel and Distributed Computing
An ant algorithm for balanced job scheduling in grids
Future Generation Computer Systems
Towards a general model of the multi-criteria workflow scheduling on the grid
Future Generation Computer Systems
Workflow-based resource allocation to optimize overall performance of composite services
Future Generation Computer Systems
Predicting the execution time of grid workflow applications through local learning
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
CloudCmp: shopping for a cloud made easy
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Selecting the most fitting resource for task execution
Future Generation Computer Systems
CloudCmp: comparing public cloud providers
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Editorial: Special issue on trusted computing and communications
Journal of Network and Computer Applications
The Journal of Supercomputing
The QoS-based MCDM system for SaaS ERP applications with Social Network
The Journal of Supercomputing
The Journal of Supercomputing
Qualitative preference-based service selection for multiple agents
Web Intelligence and Agent Systems
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Along with the development of the service-oriented architecture (SOA) and cloud computing, a large number of service providers have created an intense competitive world of business. Consequently, it is becoming increasingly complex to select a service provider for a user as a result of their various economic and social attributes. In this paper, we state the problem of how to find the appropriate services with satisfying the users' multiple QoS requirements. We consider the service's response time, trust degree and monetary cost. And inspired from the mode of Web search engine, such as Yahoo, Google, we propose an innovative service selection algorithm for SOA systems. The algorithm can recommend a number of suitable services based on the user's QoS requirements. Compared with the existing scheduling algorithms, our solution is much more flexible in supporting the multiple objectives and user personalization. We study the scalability of the algorithm with different numbers of jobs, service providers and QoS criteria. And we find that it can capture user's preferences value in less than six times of job submissions.