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

Citation

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]


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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, (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

DOI: doi:10.1109/ACCESS.2019.2939903

Abstract

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:0

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