Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim
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 r...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/22/11508 |
| _version_ | 1797465953021198336 |
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| author | Zhibao Wang Lu Bai Xiaogang Liu Yuanlin Chen Man Zhao Jinhua Tao |
| author_facet | Zhibao Wang Lu Bai Xiaogang Liu Yuanlin Chen Man Zhao Jinhua Tao |
| author_sort | Zhibao Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:29:54Z |
| format | Article |
| id | doaj.art-02dca2e0b3434a16ae5a093fcf0f0147 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:54Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-02dca2e0b3434a16ae5a093fcf0f01472023-11-24T07:36:26ZengMDPI AGApplied Sciences2076-34172022-11-0112221150810.3390/app122211508Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSimZhibao Wang0Lu Bai1Xiaogang Liu2Yuanlin Chen3Man Zhao4Jinhua Tao5School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, ChinaSchool of Computing, Ulster University, Belfast BT15 1ED, UKSchool of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, ChinaSchool of Communication and Electronic Engineering, Qiqihaer University, Qiqihaer 161003, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, ChinaWith 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.https://www.mdpi.com/2076-3417/12/22/11508remote sensing databig data acquisitiontask schedulingPSOCloudSim |
| spellingShingle | Zhibao Wang Lu Bai Xiaogang Liu Yuanlin Chen Man Zhao Jinhua Tao Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim Applied Sciences remote sensing data big data acquisition task scheduling PSO CloudSim |
| title | Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim |
| title_full | Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim |
| title_fullStr | Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim |
| title_full_unstemmed | Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim |
| title_short | Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim |
| title_sort | dynamic task scheduling in remote sensing data acquisition from open access data using cloudsim |
| topic | remote sensing data big data acquisition task scheduling PSO CloudSim |
| url | https://www.mdpi.com/2076-3417/12/22/11508 |
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