International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 600 - 607

An Optimal Task-Scheduling Strategy for Large-Scale Astronomical Workloads using In-transit Computation Model

Authors
Xiaoli Wang1, wangxiaoli@mail.xidian.edu.cn, Bharadwaj Veeravalli2, elebv@nus.edu.sg, Omer F. Rana3, ranaof@cardiff.ac.uk
1School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, China, 710071.
2Department of Electrical and ComputerEngineering, The National University of Singapore, 4 Engineering Drive 3, Singapore 117576.
3School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, Newport Road, Cardiff CF24 3AA, UK.
Received 30 April 2017, Accepted 5 January 2018, Available Online 22 January 2018.
DOI
10.2991/ijcis.11.1.45How to use a DOI?
Keywords
Task Scheduling; In-transit Computation; Load Distribution; Fog Computing; Genetic Algorithm
Abstract

The Sloan Digital Sky Survey (SDSS) has been one of the most successful sky surveys in the history of astronomy. To map the universe, SDSS uses their telescopes to take pictures of the sky over the whole survey area. Now the total SDSS data volume is larger than 125 TB since every night telescopes produce about 200 GB of data. To improve the processing efficiency of such large-scale astronomical data, we develop an optimal task-scheduling strategy by using in-transit computation model under fog computing. Within the proposed strategy, we design a global optimization technique to derive an optimal load distribution among heterogeneously computational resources. Finally, we conduct various experiments to illustrate the correctness and effectiveness of the proposed strategy. Experimental results show that it can significantly decrease the processing time of large-scale workloads.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
600 - 607
Publication Date
2018/01/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.45How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Xiaoli Wang
AU  - Bharadwaj Veeravalli
AU  - Omer F. Rana
PY  - 2018
DA  - 2018/01/22
TI  - An Optimal Task-Scheduling Strategy for Large-Scale Astronomical Workloads using In-transit Computation Model
JO  - International Journal of Computational Intelligence Systems
SP  - 600
EP  - 607
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.45
DO  - 10.2991/ijcis.11.1.45
ID  - Wang2018
ER  -