.png){kind=icon}
.png){kind=icon}
Return
A discrete dwarf mongoose optimization algorithm to solve task assignment problems on smart farms
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-024-04271-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this study, we propose a novel cloud-edge collaborative task assignment model;
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;
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. There;
e, we propose a new method to solve this kind of problem.
Keywords:
Cloud-edge collaborative
Unsplittable
Approximation algorithm
Dwarf mongoose algorithm
Heavy tasks
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K