A discrete dwarf mongoose optimization algorithm to solve task assignment problems on smart farms
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| Xuất bản năm: | Cluster Computing vol. 27, no. 5 (Aug 2024), p. 6185 |
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| Tác giả chính: | |
| Tác giả khác: | , , |
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Springer Nature B.V.
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| Những chủ đề: | |
| Truy cập trực tuyến: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1386-7857 | ||
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| 024 | 7 | |a 10.1007/s10586-024-04271-3 |2 doi | |
| 035 | |a 3092151769 | ||
| 045 | 2 | |b d20240801 |b d20240831 | |
| 100 | 1 | |a Xu, Minzhi |u Yunnan University, School of Information Science and Engineering, Kunming, China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456) | |
| 245 | 1 | |a A discrete dwarf mongoose optimization algorithm to solve task assignment problems on smart farms | |
| 260 | |b Springer Nature B.V. |c Aug 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In this study, we propose a novel cloud-edge collaborative task assignment model for smart farms that consists of a cloud server, m edge servers, and n sensors. The edge servers rely solely on solar-generated energy, which is limited, whereas the cloud server has access to a limitless amount of energy supplied by the smart grid. Each entire task from a sensor is processed by either an edge server or the cloud server. We consider the task to be unsplittable. Building on the algorithm for the multimachine job scheduling problem, we develop a corresponding approximation algorithm. In addition, we propose a new discrete heuristic based on the dwarf mongoose optimization algorithmm, named the discrete dwarf mongoose optimization algorithm, and we utilize the proposed approximation algorithm to improve the convergence speed of this heuristic while yielding better solutions. In this study, we consider task sets with heavy tasks independently, where a heavy task is a task that requires many computing resources to process. If such tasks are assigned as ordinary tasks, the assignment results may be poor. Therefore, we propose a new method to solve this kind of problem. | |
| 653 | |a Mathematical programming | ||
| 653 | |a Scheduling | ||
| 653 | |a Heuristic | ||
| 653 | |a Computer centers | ||
| 653 | |a Task scheduling | ||
| 653 | |a Deep learning | ||
| 653 | |a Collaboration | ||
| 653 | |a Servers | ||
| 653 | |a Mathematical models | ||
| 653 | |a Cloud computing | ||
| 653 | |a Neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Optimization | ||
| 653 | |a Edge computing | ||
| 653 | |a Convex analysis | ||
| 653 | |a Approximation | ||
| 653 | |a Algorithms | ||
| 653 | |a Smart grid | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Energy consumption | ||
| 653 | |a Smart sensors | ||
| 700 | 1 | |a Li, Weidong |u Yunnan University, School of Mathematics and Statistics, Kunming, China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456) | |
| 700 | 1 | |a Zhang, Xuejie |u Yunnan University, School of Information Science and Engineering, Kunming, China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456) | |
| 700 | 1 | |a Su, Qian |u Yunnan University, School of Information Science and Engineering, Kunming, China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456) | |
| 773 | 0 | |t Cluster Computing |g vol. 27, no. 5 (Aug 2024), p. 6185 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3092151769/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3092151769/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3092151769/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |