基于模糊推理规则的任务卸载模型 (FI-RBTOM) 用于边缘计算
Kashif Ibrahim1, Ahthasham Sajid2, Ihsan Ullah1
1Department of Computer Science and Information Technology, University of Balochistan, Quetta, Balochistan, Pakistan.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了一种高效的基于规则的任务卸载模型 (FI-RBTOM),以应对边缘计算的挑战,优化任务配置以减少延迟和改进服务.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 边缘计算旨在最大限度地减少延迟并提高服务质量.
- 挑战包括高用户流动性,动态的物联网环境和有限的设备资源.
- 任务卸载是边缘计算中的一个关键问题.
研究的目的:
- 提出一个有效的基于规则的任务卸载模型 (FI-RBTOM).
- 优化任务配置决策 (边缘,云或本地处理).
- 解决边缘计算中的资源限制和动态环境问题.
主要方法:
- 开发了一个有效的基于规则的任务卸载模型 (FI-RBTOM).
- 输入参数包括带宽,CPU利用率,任务长度和任务大小.
- 使用MATLAB与模糊逻辑进行的模拟.
主要成果:
- FI-RBTOM模型证明了有效的任务卸载决策.
- 使用 MATLAB 模拟实现了 0.39875 的整体错误率.
- 模型使用75%的数据进行训练,并使用25%的数据进行测试.
结论:
- 拟议的FI-RBTOM模型为边缘计算中的任务卸载提供了一个有效的解决方案.
- 该模型有效地管理了考虑各种环境和资源参数的任务配置.
- 结果表明该模型有可能提高边缘计算服务质量并减少延迟.
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