利托:对于边缘-雾-云连续系统的灵感任务卸载
1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究介绍了LITO,这是一个新的灵感任务卸载算法,用于边缘,雾和云系统. 通过模仿鼠在任务分配和调度方面的社会行为,LITO提高了能源效率和资源利用率.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 边缘,雾和云连续架构面临着异质,延迟敏感的工作负载,能源消耗和资源利用的挑战.
- 传统的任务卸载方法缺乏适应性或需要高计算开销和集中协调.
研究的目的:
- 为边缘,雾和云连续系统提出LITO,一个灵感来自的任务卸载算法.
- 解决能源消耗,资源利用和动态工作负载延迟方面的挑战.
主要方法:
- LITO将基础设施建模为一个社会系统,其节点承担着不同的角色.
- 它结合了灵感来自的机制:能量意识的任务分配 (日光浴) 和合作安排 (拥挤).
- 一个持续监督的政策学习层与上下文盗反细化了卸载决策.
主要成果:
- 利托共同降低能源消耗和违反最后期限,同时最大限度地利用资源和吞吐量.
- 模拟显示LITO的表现优于现有的多目标卸载基线.
- 该算法在能源消耗,资源利用,延迟,SLA违规和拥挤场景下的吞吐量方面表现出色.
结论:
- 在复杂的连续环境中,LITO提供了一种有效的,生物灵感的方法来卸载任务.
- 灵感来自的机制在动态,高负载条件下提供适应性和效率.
- 与传统的卸载策略相比,LITO显示出了显著的改进.
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