Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Future Generation Computer Systems - Special issue on metacomputing
Online Prediction of the Running Time of Tasks
Cluster Computing
A Prediction-Based Real-Time Scheduling Advisor
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
PlanetLab: an overlay testbed for broad-coverage services
ACM SIGCOMM Computer Communication Review
A taxonomy for resource discovery
Personal and Ubiquitous Computing
Discovery Systems in Ubiquitous Computing
IEEE Pervasive Computing
Dynamic load balancing experiments in a grid
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
The statistical properties of host load
Scientific Programming
Peer-to-Peer resource discovery in Grids: Models and systems
Future Generation Computer Systems
Failure Prediction in Computational Grids
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
Characterizing and Classifying Desktop Grid
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Future Generation Computer Systems
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An Enterprise Desktop Grid (EDG) is a low cost platform that scavenges idle desktop computers to run Grid applications. Since EDGs use idle computer time, it is important to estimate the expected computer availability. Based on this estimation, a scheduling system is able to select those computers with more expected availability to run applications. As a consequence, an overall performance improvement is achieved. Different techniques have been proposed to predict the computer state for an instant of time, but this information is not enough. A prediction model provides a sequence of computer states for different instants of time. The problem is how to identify computer behavior having as input this sequence of states. We identify the need of providing a architecture to model and evaluate desktop computer behavior. Thus, a scheduling system is able to compare and select resources that run applications faster. Experiments have shown that programs run up to 8 times faster when the scheduler selects a computer suggested by our proposal.