Abstract
With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed.
Original language | English |
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Article number | 11508 |
Number of pages | 17 |
Journal | Applied Sciences (Switzerland) |
Volume | 12 |
Issue number | 22 |
DOIs | |
Publication status | Published - 12 Nov 2022 |
Externally published | Yes |
Bibliographical note
Funding Information:This work was supported in part by TUOHAI special project 2020 of the Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002 and the Project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903.
Publisher Copyright:
© 2022 by the authors.
Keywords
- big data acquisition
- CloudSim
- PSO
- remote sensing data
- task scheduling
ASJC Scopus subject areas
- General Materials Science
- Instrumentation
- General Engineering
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes