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Deployment of batch mode scientific workflow on a Computation-as-a-Service private cloud

Citation

Xu, Y and Liu, P and Penesis, I and He, G, Deployment of batch mode scientific workflow on a Computation-as-a-Service private cloud, Proceedings - 5th International Conference on Soft Computing and Machine Intelligence (ISCMI 2018), 21-22 November 2018, Nairobi, Kenya, pp. 123-128. ISBN 9781728113005 (2018) [Refereed Conference Paper]

Copyright Statement

Copyright 2018 IEEE

Official URL: http://dx.doi.org/10.1109/ISCMI.2018.8703239

Abstract

Cloud computing is usually to address business problems of costly computing infrastructures but nowadays it is considered as a possible alternative to scientific workflow deployment. Therefore, there are only limited cases for scientific and engineering computing in which there are task parallelism closely coupled with high concurrent I/O requirements. To address this issue, this paper developed a new resource management methodology to maximize overall machine utilization levels while minimizing application run time. The key strategy and algorithm in this methodology consist of: (i) a bottom-up architecture that utilizes resources for both servers and clients. (ii) a maximum utilization resource coloration algorithm based on node ability. A prototype system was implemented by incorporating the policies and algorithms mentioned above in Cloud Computing and Distributed Systems (CLOUDS) Laboratory. Initial results were obtained by two different cases, by Rotorysics (formerly Propella), a special marine hydrodynamics code for propellers and turbines and by DF_OSFBEM, a panel method code for unsteady 3D multiple-foil hydrodynamics. Results showed that new solution has speeded up total run time up to 50% at the 2nd level --- the higher service ability level. By using the developed methodology and exploration of Computation-as-a- Service (CaaS), the objective was achieved to accelerate scientific workflow efficiency in private cloud computing platform.

Item Details

Item Type:Refereed Conference Paper
Keywords:scientific workflows, resource management, private cloud computing, node ability, Computation-as-a-Service
Research Division:Information and Computing Sciences
Research Group:Distributed Computing
Research Field:Distributed Computing not elsewhere classified
Objective Division:Energy
Objective Group:Renewable Energy
Objective Field:Tidal Energy
UTAS Author:Xu, Y (Associate Professor Yiyi Xu)
UTAS Author:Liu, P (Associate Professor Pengfei Liu)
UTAS Author:Penesis, I (Professor Irene Penesis)
ID Code:129712
Year Published:2018
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2018-12-15
Last Modified:2019-09-10
Downloads:0

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