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

0447867 - ÚJF 2016 RIV GB eng C - Conference Paper (international conference)
Makatun, Dzmitry - Lauret, J. - Rudová, H. - Šumbera, Michal
Planning for distributed workflows: constraint-based coscheduling of computational jobs and data placement in distributed environments.
Journal of Physics Conference Series. Vol. 608. Bristol: IOP Publishing Ltd, 2015, Č. 012028. ISSN 1742-6588.
[International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT). Prague (CZ), 01.09.2014-05.09.2014]
R&D Projects: GA ČR GA13-20841S
Institutional support: RVO:61389005
Keywords : data * CPU * STAR
Subject RIV: BG - Nuclear, Atomic and Molecular Physics, Colliders

Latencies and bandwidth can become the major limiting factor for the overall computation performance and can reduce the CPU/WallTime ratio to excessive TO 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.
Permanent Link: http://hdl.handle.net/11104/0249649