.png){kind=icon}
.png){kind=icon}
Return
Hybrid scheduling algorithm in early warning systems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2017.04.002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The development of an efficient Early Warning System (EWS) is essential for the prediction and prevention of imminent natural hazards. In addition to providing a computationally intensive infrastructure with extensive data transfer, high-execution reliability and hard-deadline satisfaction are important requirements of EWS scenario processing. This is due to the fact that EWS has a limited window of opportunity to discern if a scene shows signs of an impending natural disaster. In this paper, the scheduling component of the EWS scenario is investigated and an efficient hybrid algorithm for the urgent workflows scheduling is proposed. The developed algorithm is based on traditional heuristic and meta heuristic approaches along with state-of-the-art cloud computing principles. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Workflow scheduling
EWS
Hybrid algorithm
Hard deadline
Urgent computing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
6.1
Papers:
6.8K
Citations:
2.3W