C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The ANL/IBM SP Scheduling System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
Adaptive job scheduling via predictive job resource allocation
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Are user runtime estimates inherently inaccurate?
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
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Jobs in a computer cluster have several exit statuses caused by application properties, user and scheduler behavior. In this paper we analyze importance of job statuses and potential use of their prediction prior to job execution. Method for prediction of failed jobs based on Bayesian classifier is proposed and accuracy of the method is analyzed on several workloads. This method is integrated to the EASY algorithm adapted to prioritize jobs that are likely to fail. System performance for both failed jobs and the entire workload is analyzed.