An optimal task-scheduling strategy for large-scale astronomical workloads using in-transit computation model

Wang, Xiaoli, Veeravalli, Bharadwaj and Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 2018. An optimal task-scheduling strategy for large-scale astronomical workloads using in-transit computation model. International Journal of Computational Intelligence Systems 11 (1) , pp. 600-607. 10.2991/ijcis.11.1.45

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Taylor & Francis
ISSN:	1875-6883
Date of First Compliant Deposit:	2 April 2018
Date of Acceptance:	5 January 2018
Last Modified:	05 May 2023 01:59
URI:	https://orca.cardiff.ac.uk/id/eprint/110420

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