IEEE Transactions on Parallel and Distributed Systems
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
Theory and Practice in Parallel Job Scheduling
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
The Impact of More Accurate Requested Runtimes on Production Job Scheduling Performance
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
Job-Length Estimation and Performance in Backfilling Schedulers
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Utilization and Predictability in Scheduling the IBM SP2 with Backfilling
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
Locality of sampling and diversity in parallel system workloads
Proceedings of the 21st annual international conference on Supercomputing
Risk aware overbooking for commercial grids
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
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Backfilling is a scheduling optimization that requires information about job runtimes to be known. Such information can come from either of two sources: estimates provided by users when the jobs are submitted, or predictions made by the system based on historical data regarding previous executions of jobs. In both cases, each job is assigned a precise prediction of how long it will run. We suggest that instead the whole distribution of the historical data be used. As a result, the whole backfilling framework shifts from a concrete plan for the future schedule to a probabilistic plan where jobs are backfilled based on the probability that they will terminate in time.