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Multi-machine and multi-task emergency allocation algorithm based on precedence rules
Double layer programming model to the scheduling of remote sensing data processing tasks
1. | School of Mathematics and Big Data, Foshan University, Foshan 528000, China |
2. | College of Systems Engineering, National University of Defense Technology, Changsha 410073, China |
3. | Business School of Hunan University, Changsha 410082, China |
4. | School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, China |
Remotely sensed data are widely used in disaster and environment monitoring. To complete the tasks associated with processing these data, it is a practical and pressing problem to match the resources for these data with data processing centers in real or near-real time and complete as many tasks on time as possible. However, scheduling remotely sensed data processing tasks has two phases, namely, task assignment and task scheduling. This paper presents a model using bilevel optimization, which considers task assignment and task scheduling as a single problem. Using this architecture, a mathematical model for both levels of the problem is presented. To solve the mathematical model, this paper presents a cooperative coevolution algorithm that combines the advantages of a very fast simulated annealing algorithm with a learnable ant colony optimization algorithm. Finally, the effectiveness and feasibility of the proposed approach compared with the conventional method is demonstrated through empirical results.
References:
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G. Aloisio, M. Cafaro and R. Williams, The digital Puglia project: An active digital library of remote sensing data, in Proceedings of the 7th International Conference on High Performance Computing and Networking, HPCN-Europe, 1593 (1999), 563-572. |
[2] |
G. Aloisio, M.. Cafaro, R. Williams and P. Messina, A Distributed Web-Based Metacomputing Environment, in International Conference and Exhibition on High-Performance Computing and Networking, HPCN-Europe, 1225 (1997), 480-486. |
[3] |
G. Aloisio, M. Cafaro, S. Fiore and G. Quarta, A Grid-Based Architecture for Earth Observation Data Access, in ACM Symposium on Applied Computing, Santa Fe, New Mexico, USA, 2005. |
[4] |
G. Bai, L. Xing and Y. Chen,
Scheduling multi-platforms collaborative disasters monitoring based on coevolution algorithm, Research Journal of Chemistry and Environment, 16 (2012), 43-50.
|
[5] |
G. Bai, L. Xing and Y. Chen,
The knowledge-based genetic algorithm to the disasters monitoring task allocation problem, Research Journal of Chemistry & Environment, 16 (2012), 27-34.
|
[6] |
P. Caccetta, S. Collings, K. Hingee, D. McFarlane and X. Wu, Fine-Scale Monitoring of Complex Environments Using Remotely Sensed Aerial, Satellite, and Other Spatial Data, 2011 International Symposium on Image and Data Fusion, Tengchong, Yunnan, (2011), 1-5. |
[7] |
Y. Cao, S. Wang, L. Kang and Y. Gao,
A TQCS-based service selection and scheduling strategy in cloud manufacturing, The International Journal of Advanced Manufacturing Technology, 82 (2016), 235-251.
|
[8] |
Z. Chen, Y. Xue, J. Dong, J. Liu and Y. Li, The task scheduling for Remote Sensing Quantitative Retrieval based on hierarchical grid computing platform, 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, (2012), 487-490. |
[9] |
P. Chinnasamy and M. G. Sunde,
Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data--a case study of the Indian Ocean Tsunami, Earth Science Informatics, 9 (2016), 101-111.
|
[10] |
S. D. Dao, K. Abhary and R. Marian,
An integrated production scheduling model for multi-product orders in VCIM systems, International Journal of System Assurance Engineering and Management, 8 (2017), 12-27.
|
[11] |
J. Dong, Y. Xue, Z. Chen, H. Xu and Y. Li, Analysis of remote sensing quantitative inversion in cloud computing, 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, (2011), 4348-4351. |
[12] |
J. Dong, Y. Xue, Z. Chen, H. Xu, Y. Li and C. Wu, A study of grid workflow dynamic customization for remote sensing quantitative retrieval, 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, (2012), 5382-5385. |
[13] |
Y. Guan, W. Xiao, R. Cheung and C. Li,
A multiprocessor task scheduling model for berth allocation: Heuristic and worst-case analysis, Operations Research Letters, 30 (2002), 343-350.
doi: 10.1016/S0167-6377(02)00147-5. |
[14] |
R. He and L. Xing,
A learnable ant colony optimization to the mission planning of multiple satellites, Research Journal of Chemistry and Environment, 16 (2012), 18-26.
|
[15] |
T. Jena and J. R. Mohanty,
GA-based customer-conscious resource allocation and task scheduling in multi-cloud computing, Arabian Journal for Science and Engineering, (2017), 1-16.
|
[16] |
A. Kamalinia and A. Ghaffari, Hybrid task scheduling method for cloud computing by genetic and DE algorithms, Wireless Personal Communications, to be published. |
[17] |
S. S. Karamchand, Operator Scheduling in a Distributed Stream Management System for Remotely Sensed Imagery, Ph.D thesis, University of California in Davis, USA, 2006. |
[18] |
I. Kucukkoc and D. Z. Zhang,
Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines, The International Journal of Advanced Manufacturing Technology, 82 (2016), 265-285.
|
[19] |
W. Li, Y.-W. Chen, J.-F. Li and P. Gao, Remotely sensed data processing task scheduling based on limited concentration model, PIC2010, Shanghai, China, (2001), 15-18. |
[20] |
J. F. Li and L. Xing,
Integrative Forest Fire Monitoring System Framework Design Aim to Early Warning Tasks, Disaster Advances, 5 (2012), 726-729.
|
[21] |
H. Liu, Y. Fan, X. Deng and S. Ji, Parallel Processing Architecture of Remotely Sensed Image Processing System Based on Cluster, 2009 2nd International Congress on Image and Signal Processing, Tianjin, (2009), 1-4. |
[22] |
Y. Ma, L. Wang, A. Y. Zomaya, D. Chen and R. Ranjan,
Task-Tree Based Large-Scale Mosaicking for Massive Remote Sensed Imageries with Dynamic DAG Scheduling, IEEE Transactions on Parallel and Distributed Systems, 25 (2014), 2126-2137.
|
[23] |
P. Myszkowski, M. E.
Skowro$\acute{\rm{v}}$ski, L. P. Olech and K. Oślizłiptono,
Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem, Soft Computing, 19 (2015), 3599-3619.
|
[24] |
S. Nguyen, Y. Mei and M. Zhang,
Genetic programming for production scheduling: A survey with a unified framework, Complex & Intelligent Systems, 3 (2017), 41-66.
|
[25] |
S. Nguyen,
A learning and optimizing system for order acceptance and scheduling, The International Journal of Advanced Manufacturing Technology, 86 (2016), 2021-2036.
|
[26] |
S. K. Panda, I. Gupta and P. K. Jana, A multiprocessor task scheduling model for berth allocation: heuristic and worst-case analysis, Information Systems Frontiers, to be published. |
[27] |
T. J. Pultz and R. A. Scofield,
Applications of remotely sensed data in flood prediction and monitoring: Report of the CEOS Disaster Management Support Group flood team, IEEE International Geoscience and Remote Sensing Symposium, 2 (2002), 768-770.
|
[28] |
M. A. Salido, J. Escamilla, A. Giret and F. Barber,
A genetic algorithm for energy-efficiency in job-shop scheduling, The International Journal of Advanced Manufacturing Technology, 85 (2016), 1303-1314.
|
[29] |
Y Shen, N Zhao, M Xia and X Du,
A deep Q-learning network for ship stowage planning probleM, Polish Maritime Research, 24 (2017), 102-109.
|
[30] |
S. Tehraniana, Y.-S. Zhao, T. Harveya, A. Swaroopa and M. Keith,
A robust framework for real-time distributed processing of satellite data, Journal of Parallel Distributed Computing, 66 (2006), 403-418.
|
[31] |
J. T. Tsai, J. C. Fang and J. H. Chou,
Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm, Computers & Operations Research, 40 (2013), 3045-3055.
|
[32] |
Z. Wang -F, Predictive Control of Networked Control Systems, Ph.D thesis, Zhe Jiang University, Zhe Jiang, China, 2008. |
[33] |
L. Wen, G. Peng, C. Ying-Wu and L. Ju-Fang, Approach of remotely sensed data processing task scheduling problem based on ant colony optimization, Proceedings of 2011 International Conference on Modelling, Identification and Control, Shanghai, (2011), 532-536. |
[34] |
C. S. Wong, F. T. S. Chan and S. H. Chung,
A joint production scheduling approach considering multiple resources and preventive maintenance tasks, International Journal of Production ResearcH, 51 (2013), 883-896.
|
[35] |
S. Wu, P. Zhang, F. Li, F. Gu and Y. Pan,
A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems, Journal of Central South University, 23 (2016), 421-429.
|
[36] |
B. Xiang, G. Q. Li, D. S. Liu and J. S. Li,
Research on task management and scheduling of high performance remote sensing satellite ground pre-processing system, Journal of Astronautics, 29 (2008), 1443-1446.
|
[37] |
Y. Xu, K. Li, J. Hu and K. Li,
A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues, Information Sciences, 270 (2014), 255-287.
doi: 10.1016/j.ins.2014.02.122. |
[38] |
K. Yang and L. Xing,
The Learnable Ant Colony Optimization to Satellite Ground Station System Scheduling Problems, Przeglad Elektrotechniczny, 88 (2012), 62-65.
|
[39] |
F. Yao and L. Xing,
The Model, Algorithm and Application to Scheduling Problem of Agile Earth Observing Satellites, Disaster Advances, 5 (2012), 1112-1116.
|
[40] |
H. Zheng, L. Wang and X. Zheng,
Teaching--learning-based optimization algorithm for multi--skill resource constrained project scheduling problem, Soft Computing, 21 (2017), 1537-1548.
|
[41] |
M. Zhu, G. Wang and T. Oyana,
Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images, The Journal of Supercomputing, 59 (2012), 83-103.
|
[42] |
X. Zuo, G. Zhang and W. Tan,
Self-adaptive learning pso-based deadline constrained task scheduling for hybrid iaas cloud, IEEE Transactions on Automation Science and Engineering, 11 (2014), 564-573.
|
show all references
References:
[1] |
G. Aloisio, M. Cafaro and R. Williams, The digital Puglia project: An active digital library of remote sensing data, in Proceedings of the 7th International Conference on High Performance Computing and Networking, HPCN-Europe, 1593 (1999), 563-572. |
[2] |
G. Aloisio, M.. Cafaro, R. Williams and P. Messina, A Distributed Web-Based Metacomputing Environment, in International Conference and Exhibition on High-Performance Computing and Networking, HPCN-Europe, 1225 (1997), 480-486. |
[3] |
G. Aloisio, M. Cafaro, S. Fiore and G. Quarta, A Grid-Based Architecture for Earth Observation Data Access, in ACM Symposium on Applied Computing, Santa Fe, New Mexico, USA, 2005. |
[4] |
G. Bai, L. Xing and Y. Chen,
Scheduling multi-platforms collaborative disasters monitoring based on coevolution algorithm, Research Journal of Chemistry and Environment, 16 (2012), 43-50.
|
[5] |
G. Bai, L. Xing and Y. Chen,
The knowledge-based genetic algorithm to the disasters monitoring task allocation problem, Research Journal of Chemistry & Environment, 16 (2012), 27-34.
|
[6] |
P. Caccetta, S. Collings, K. Hingee, D. McFarlane and X. Wu, Fine-Scale Monitoring of Complex Environments Using Remotely Sensed Aerial, Satellite, and Other Spatial Data, 2011 International Symposium on Image and Data Fusion, Tengchong, Yunnan, (2011), 1-5. |
[7] |
Y. Cao, S. Wang, L. Kang and Y. Gao,
A TQCS-based service selection and scheduling strategy in cloud manufacturing, The International Journal of Advanced Manufacturing Technology, 82 (2016), 235-251.
|
[8] |
Z. Chen, Y. Xue, J. Dong, J. Liu and Y. Li, The task scheduling for Remote Sensing Quantitative Retrieval based on hierarchical grid computing platform, 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, (2012), 487-490. |
[9] |
P. Chinnasamy and M. G. Sunde,
Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data--a case study of the Indian Ocean Tsunami, Earth Science Informatics, 9 (2016), 101-111.
|
[10] |
S. D. Dao, K. Abhary and R. Marian,
An integrated production scheduling model for multi-product orders in VCIM systems, International Journal of System Assurance Engineering and Management, 8 (2017), 12-27.
|
[11] |
J. Dong, Y. Xue, Z. Chen, H. Xu and Y. Li, Analysis of remote sensing quantitative inversion in cloud computing, 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, (2011), 4348-4351. |
[12] |
J. Dong, Y. Xue, Z. Chen, H. Xu, Y. Li and C. Wu, A study of grid workflow dynamic customization for remote sensing quantitative retrieval, 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, (2012), 5382-5385. |
[13] |
Y. Guan, W. Xiao, R. Cheung and C. Li,
A multiprocessor task scheduling model for berth allocation: Heuristic and worst-case analysis, Operations Research Letters, 30 (2002), 343-350.
doi: 10.1016/S0167-6377(02)00147-5. |
[14] |
R. He and L. Xing,
A learnable ant colony optimization to the mission planning of multiple satellites, Research Journal of Chemistry and Environment, 16 (2012), 18-26.
|
[15] |
T. Jena and J. R. Mohanty,
GA-based customer-conscious resource allocation and task scheduling in multi-cloud computing, Arabian Journal for Science and Engineering, (2017), 1-16.
|
[16] |
A. Kamalinia and A. Ghaffari, Hybrid task scheduling method for cloud computing by genetic and DE algorithms, Wireless Personal Communications, to be published. |
[17] |
S. S. Karamchand, Operator Scheduling in a Distributed Stream Management System for Remotely Sensed Imagery, Ph.D thesis, University of California in Davis, USA, 2006. |
[18] |
I. Kucukkoc and D. Z. Zhang,
Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines, The International Journal of Advanced Manufacturing Technology, 82 (2016), 265-285.
|
[19] |
W. Li, Y.-W. Chen, J.-F. Li and P. Gao, Remotely sensed data processing task scheduling based on limited concentration model, PIC2010, Shanghai, China, (2001), 15-18. |
[20] |
J. F. Li and L. Xing,
Integrative Forest Fire Monitoring System Framework Design Aim to Early Warning Tasks, Disaster Advances, 5 (2012), 726-729.
|
[21] |
H. Liu, Y. Fan, X. Deng and S. Ji, Parallel Processing Architecture of Remotely Sensed Image Processing System Based on Cluster, 2009 2nd International Congress on Image and Signal Processing, Tianjin, (2009), 1-4. |
[22] |
Y. Ma, L. Wang, A. Y. Zomaya, D. Chen and R. Ranjan,
Task-Tree Based Large-Scale Mosaicking for Massive Remote Sensed Imageries with Dynamic DAG Scheduling, IEEE Transactions on Parallel and Distributed Systems, 25 (2014), 2126-2137.
|
[23] |
P. Myszkowski, M. E.
Skowro$\acute{\rm{v}}$ski, L. P. Olech and K. Oślizłiptono,
Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem, Soft Computing, 19 (2015), 3599-3619.
|
[24] |
S. Nguyen, Y. Mei and M. Zhang,
Genetic programming for production scheduling: A survey with a unified framework, Complex & Intelligent Systems, 3 (2017), 41-66.
|
[25] |
S. Nguyen,
A learning and optimizing system for order acceptance and scheduling, The International Journal of Advanced Manufacturing Technology, 86 (2016), 2021-2036.
|
[26] |
S. K. Panda, I. Gupta and P. K. Jana, A multiprocessor task scheduling model for berth allocation: heuristic and worst-case analysis, Information Systems Frontiers, to be published. |
[27] |
T. J. Pultz and R. A. Scofield,
Applications of remotely sensed data in flood prediction and monitoring: Report of the CEOS Disaster Management Support Group flood team, IEEE International Geoscience and Remote Sensing Symposium, 2 (2002), 768-770.
|
[28] |
M. A. Salido, J. Escamilla, A. Giret and F. Barber,
A genetic algorithm for energy-efficiency in job-shop scheduling, The International Journal of Advanced Manufacturing Technology, 85 (2016), 1303-1314.
|
[29] |
Y Shen, N Zhao, M Xia and X Du,
A deep Q-learning network for ship stowage planning probleM, Polish Maritime Research, 24 (2017), 102-109.
|
[30] |
S. Tehraniana, Y.-S. Zhao, T. Harveya, A. Swaroopa and M. Keith,
A robust framework for real-time distributed processing of satellite data, Journal of Parallel Distributed Computing, 66 (2006), 403-418.
|
[31] |
J. T. Tsai, J. C. Fang and J. H. Chou,
Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm, Computers & Operations Research, 40 (2013), 3045-3055.
|
[32] |
Z. Wang -F, Predictive Control of Networked Control Systems, Ph.D thesis, Zhe Jiang University, Zhe Jiang, China, 2008. |
[33] |
L. Wen, G. Peng, C. Ying-Wu and L. Ju-Fang, Approach of remotely sensed data processing task scheduling problem based on ant colony optimization, Proceedings of 2011 International Conference on Modelling, Identification and Control, Shanghai, (2011), 532-536. |
[34] |
C. S. Wong, F. T. S. Chan and S. H. Chung,
A joint production scheduling approach considering multiple resources and preventive maintenance tasks, International Journal of Production ResearcH, 51 (2013), 883-896.
|
[35] |
S. Wu, P. Zhang, F. Li, F. Gu and Y. Pan,
A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems, Journal of Central South University, 23 (2016), 421-429.
|
[36] |
B. Xiang, G. Q. Li, D. S. Liu and J. S. Li,
Research on task management and scheduling of high performance remote sensing satellite ground pre-processing system, Journal of Astronautics, 29 (2008), 1443-1446.
|
[37] |
Y. Xu, K. Li, J. Hu and K. Li,
A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues, Information Sciences, 270 (2014), 255-287.
doi: 10.1016/j.ins.2014.02.122. |
[38] |
K. Yang and L. Xing,
The Learnable Ant Colony Optimization to Satellite Ground Station System Scheduling Problems, Przeglad Elektrotechniczny, 88 (2012), 62-65.
|
[39] |
F. Yao and L. Xing,
The Model, Algorithm and Application to Scheduling Problem of Agile Earth Observing Satellites, Disaster Advances, 5 (2012), 1112-1116.
|
[40] |
H. Zheng, L. Wang and X. Zheng,
Teaching--learning-based optimization algorithm for multi--skill resource constrained project scheduling problem, Soft Computing, 21 (2017), 1537-1548.
|
[41] |
M. Zhu, G. Wang and T. Oyana,
Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images, The Journal of Supercomputing, 59 (2012), 83-103.
|
[42] |
X. Zuo, G. Zhang and W. Tan,
Self-adaptive learning pso-based deadline constrained task scheduling for hybrid iaas cloud, IEEE Transactions on Automation Science and Engineering, 11 (2014), 564-573.
|






station1 | station2 | ||||
PC1 | PC2 | PC3 | PC1 | PC2 | PC3 |
3420 | 3568 | 2954 | 2486 | 1471 | 1770 |
station1 | station2 | ||||
PC1 | PC2 | PC3 | PC1 | PC2 | PC3 |
3420 | 3568 | 2954 | 2486 | 1471 | 1770 |
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