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A discrete dwarf mongoose optimization algorithm to solve task assignment problems on smart farms

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Abstract

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.

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Funding

This work was supported in part by the National Natural Science Foundation of China [Grant nos. 62266051, 12071417, and 62062065] and the Basic Research Project of Yunnan Province [Grant no. 202401AT070471].

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China

    Minzhi Xu, Xuejie Zhang & Qian Su

  2. School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, China

    Weidong Li

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  1. Minzhi Xu
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Contributions

Conceptualization, WL; methodology, MX and WL; software, MX; validation, MX; formal analysis, QS and MX; investigation, MX; resources, QS and MX; data curation, QS and MX; writing-original draft preparation, MX; writing-review and editing, QS and MX; visualization, MX; supervision, XZ and WL; project administration, XZ; funding acquisition, XZ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Qian Su.

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Xu, M., Li, W., Zhang, X. et al. A discrete dwarf mongoose optimization algorithm to solve task assignment problems on smart farms. Cluster Comput 27, 6185–6204 (2024). https://doi.org/10.1007/s10586-024-04271-3

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