eCite Digital Repository

A task-resource mapping algorithm for large-scale batch-mode computational marine hydrodynamics codes on containerized private cloud


Xu, Y and Liu, P and Penesis, I and He, G, A task-resource mapping algorithm for large-scale batch-mode computational marine hydrodynamics codes on containerized private cloud, IEEE Access, 7 pp. 127943-127955. ISSN 2169-3536 (2019) [Refereed Article]

PDF (Published version)

Copyright Statement

2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

DOI: doi:10.1109/ACCESS.2019.2939903


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,.

Item Details

Item Type:Refereed Article
Keywords:computational marine hydrodynamics (CMH) codes, containerization, large-scale batch mode computing, private cloud, task-resource mapping algorithm
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed computing and systems software not elsewhere classified
Objective Division:Energy
Objective Group:Renewable energy
Objective Field:Tidal energy
UTAS Author:Xu, Y (Associate Professor Yiyi Xu)
UTAS Author:Penesis, I (Professor Irene Penesis)
ID Code:145324
Year Published:2019
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2021-07-15
Last Modified:2021-09-09
Downloads:10 View Download Statistics

Repository Staff Only: item control page