Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient load balancing for wide-area divide-and-conquer applications
PPoPP '01 Proceedings of the eighth ACM SIGPLAN symposium on Principles and practices of parallel programming
Automatic Generation of Self-Scheduling Programs
IEEE Transactions on Parallel and Distributed Systems
An Enabling Framework for Master-Worker Applications on the Computational Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
A Problem-Specific Fault-Tolerance Mechanism for Asynchronous, Distributed Systems
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Adaptive scheduling of master/worker applications on distributed computational resources
Adaptive scheduling of master/worker applications on distributed computational resources
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Scalable Peer-to-Peer Process Management - The OSIRIS Approach
ICWS '04 Proceedings of the IEEE International Conference on Web Services
Subspace Selection for Clustering High-Dimensional Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Journal of Biomedical Informatics
Web Service Call Parallelization Using OpenMP
IWOMP '07 Proceedings of the 3rd international workshop on OpenMP: A Practical Programming Model for the Multi-Core Era
A novel strategy for multi-resource load balancing in agent-based systems
International Journal of Intelligent Information and Database Systems
DELOS'04 Proceedings of the 6th Thematic conference on Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures
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In a grid environment, it is of primary concern to make efficient use of the resources that are available at run-time. If new computational resources become available, then requests shall also be sent to these newly added resources in order to balance the overall load in the system. However, scheduling of requests in a service grid considers each single service invocation in isolation and determines the most appropriate provider, according to some heuristics. Even when several providers offer the same service, only one of them is chosen. In this paper, we provide a novel approach to the parallelization of individual service requests. This approach makes dynamic use of a set of service providers available at the time the request is being issued. A dynamic service uses meta information on the currently available service providers and their capabilities and splits the original request up into a set of simpler requests of the same service types, submits these requests in parallel to as many service providers as possible, and finally integrates the individual results to the result of the original service request.