Distributed Monitoring with Collaborative Prediction
Distributed Monitoring with Collaborative Prediction
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 Factor- ization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%.
[INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]
[INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]
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