HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >

Multiobjective Level-Wise Scientific Workflow Optimization in IaaS Public Cloud Environment

This item is licensed under: Creative Commons Attribution 4.0 International

Files in This Item:
5342727.pdf2.78 MBPDFView/Open
Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/68288

Title: Multiobjective Level-Wise Scientific Workflow Optimization in IaaS Public Cloud Environment
Authors: Thant, Phyo Thandar Browse this author
Powell, Courtney Browse this author
Schlueter, Martin Browse this author
Munetomo, Masaharu Browse this author →KAKEN DB
Issue Date: 21-Dec-2017
Publisher: Hindawi Publishing Corporation
Journal Title: Scientific programming
Start Page: 5342727
Publisher DOI: 10.1155/2017/5342727
Abstract: Cloud computing in the field of scientific applications such as scientific big data processing and big data analytics has become popular because of its service oriented model that provides a pool of abstracted, virtualized, dynamically scalable computing resources and services on demand over the Internet. However, resource selection to make the right choice of instances for a certain application of interest is a challenging problem for researchers. In addition, providing services with optimal performance at the lowest financial resource deployment cost based on users' resource selection is quite challenging for cloud service providers. Consequently, it is necessary to develop an optimization system that can provide benefits to both users and service providers. In this paper, we conduct scientific workflow optimization on three perspectives: makespan minimization, virtual machine deployment cost minimization, and virtual machine failure minimization in the cloud infrastructure in a level-wise manner. Further, balanced task assignment to the virtual machine instances at each level of the workflow is also considered. Finally, system efficiency verification is conducted through evaluation of the results with different multiobjective optimization algorithms such as SPEA2 and NSGA-II.
Rights: https://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/68288
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: Phyo Thandar Thant

Export metadata:

OAI-PMH ( junii2 , jpcoar )

MathJax is now OFF:


 

 - Hokkaido University