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

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 ener...

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Vydáno v:Cluster computing Ročník 27; číslo 5; s. 6185 - 6204
Hlavní autoři: Xu, Minzhi, Li, Weidong, Zhang, Xuejie, Su, Qian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2024
Springer Nature B.V
Témata:
Algorithms
Approximation
Cloud computing
Collaboration
Computer centers
Computer Communication Networks
Computer Science
Convex analysis
Deep learning
Edge computing
Energy consumption
Heuristic
Mathematical models
Mathematical programming
Neural networks
Operating Systems
Optimization
Optimization algorithms
Processor Architectures
Scheduling
Sensors
Servers
Smart grid
Smart sensors
Task scheduling
Cloud-edge collaborative
Dwarf mongoose algorithm
Unsplittable
Approximation algorithm
Heavy tasks
ISSN:1386-7857, 1573-7543
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Shrnutí: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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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-04271-3