LAUSR

CPU time has long been a remaining problem for large-scale batch mode based scientific computing applications. To address this time-consuming problem, a container-based private cloud was employed, and a novel task-resource mapping algorithm was developed. Firstly, the execution features of typical batch mode codes were extracted and then computing jobs were formulated as a coarseness acyclic DAG. Secondly, to guarantee both job makespan and resource utilization, a novel task-resource mapping algorithm, along with container pre-planning and worst-case-first task placement phases, were developed. Finally, a typical Computational Marine Hydrodynamics software, Rotorysics, with a different scale of input data matrix was used as benchmark software. To manifest the effectiveness of the proposed method, a number of numerical examples were given via CloudSim and a small-medium containerized private cloud platform was adopted with three practical study cases. The computational results show that 1) compared with the traditional HPC workstation computing solution, container-based cloud solution shows significant savings in makespan by more than 6 times. 2) the new method is scalable to address bigger size batch computing problem up to a run matrix 108.