An incremental method for finding multivariate splits for decision trees
Proceedings of the seventh international conference (1990) on Machine learning
Evaluation of design choices for gang scheduling using distributed hierarchical control
Journal of Parallel and Distributed Computing
Active disks: programming model, algorithms and evaluation
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
An evaluation of parallel job scheduling for ASCI Blue-Pacific
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
A Realistic Model and an Efficient Heuristic for Scheduling with Heterogeneous Processors
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Gang Scheduling with a Queue for Large Jobs
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Job Characteristics of a Production Parallel Scientivic Workload on the NASA Ames iPSC/860
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Benchmarks and Standards for the Evaluation of Parallel Job Schedulers
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
A Self-Tuning Job Scheduler Family with Dynamic Policy Switching
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Gang Scheduling with Memory Considerations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Improving Parallel Job Scheduling by Combining Gang Scheduling and Backfilling Techniques
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
IEEE Transactions on Parallel and Distributed Systems
Asynchronous and anticipatory filter-stream based parallel algorithm for frequent itemset mining
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Anthill: A Scalable Run-Time Environment for Data Mining Applications
SBAC-PAD '05 Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing
Automatic parallelization of canonical loops
Science of Computer Programming
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Irregular and iterative I/O-intensive jobs need a different approach from parallel job schedulers. The focus in this case is not only the processing requirements anymore: memory, network and storage capacity must all be considered in making a scheduling decision. Job executions are irregular and data dependent, alternating between CPU-bound and I/O-bound phases. In this paper, we propose and implement a parallel job scheduling strategy for such jobs, called AnthillSched, based on a simple heuristic: we map the behavior of a parallel application with minimal resources as we vary its input parameters. From that mapping we infer the best scheduling for a certain set of input parameters given the available resources. To test and verify AnthillSched we used logs obtained from a real system executing data mining jobs. Our main contributions are the implementation of a parallel job scheduling strategy in a real system and the performance analysis of AnthillSched, which allowed us to discard some other scheduling alternatives considered previously.