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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Góes, L. F., Guerra, P., Coutinho, B., Rocha, L., Meira, W., Ferreira, R., … Cirne, W. (2006). AnthillSched: A scheduling strategy for irregular and iterative I/O-intensive parallel jobs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3834 LNCS, pp. 108–122). https://doi.org/10.1007/11605300_5
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