ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Globus Toolkit for Grid Computing
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Advanced resource connector middleware for lightweight computational Grids
Future Generation Computer Systems - Special section: Information engineering and enterprise architecture in distributed computing environments
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-HDP: a non parametric Bayesian model for tensor factorization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Collaborative reliability prediction of service-oriented systems
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
QoE model driven for network services
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
Efficient distributed monitoring with active Collaborative Prediction
Future Generation Computer Systems
| Hi-index | 0.00 |
Isolating users from the inevitable faults in large distributed systems is critical to Quality of Experience. We formulate the problem of probe selection for fault prediction based on end-to-end probing as a Collaborative Prediction (CP) problem. On an extensive experimental dataset from the EGI grid, the combination of the Maximum Margin Matrix Factorization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%.