Planning for distributed workflows: constraint-based coscheduling of computational jobs and data placement in distributed environments

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Authors

MAKATUN Dzmitry LAURET Jerome RUDOVÁ Hana ŠUMBERA Michal

Year of publication 2015
Type Article in Proceedings
Conference Journal of Physics: Conference Series, vol. 608
MU Faculty or unit

Faculty of Informatics

Citation
Web URL
Doi http://dx.doi.org/10.1088/1742-6596/608/1/012028A
Field Informatics
Keywords planning; constraint programming; distributed computational resources; STAR experiment
Description When running data intensive applications on distributed computational resources long I/O overheads may be observed as access to remotely stored data is performed. Latencies and bandwidth can become the major limiting factor for the overall computation performance and can reduce the CPU/WallTime ratio to excessive IO wait. Reusing the knowledge of our previous research, we propose a constraint programming based planner that schedules computational jobs and data placements (transfers) in a distributed environment in order to optimize resource utilization and reduce the overall processing completion time. The optimization is achieved by ensuring that none of the resources (network links, data storages and CPUs) are oversaturated at any moment of time and either (a) that the data is pre-placed at the site where the job runs or (b) that the jobs are scheduled where the data is already present. Such an approach eliminates the idle CPU cycles occurring when the job is waiting for the I/O from a remote site and would have wide application in the community. Our planner was evaluated and simulated based on data extracted from log files of batch and data management systems of the STAR experiment. The results of evaluation and estimation of performance improvements are discussed in this paper.
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