Journal
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Volume 11, Issue 1, Pages 600-607Publisher
ATLANTIS PRESS
DOI: 10.2991/ijcis.11.1.45
Keywords
Task Scheduling; In-transit Computation; Load Distribution; Fog Computing; Genetic Algorithm
Categories
Funding
- MOE Tier-1 grant [R-263-000-C14-112]
- National Natural Science Foundation of China [61402350, 61472297, 61572391]
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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.
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