在边界之外,一个混合细胞和粒子群优化模型用于边缘计算中的能量和延迟优化
Dinesh Sahu1, Nidhi1, Shiv Prakash2
1SCSET, Bennett University, Plot Nos 8, 11, TechZone 2, Greater Noida, Uttar Pradesh, 201310, India.
Scientific reports
|February 20, 2025
概括
本研究介绍了一种混合的细胞盆模型 (CPM) 和粒子优化 (PSO) 方法,用于高效的边缘计算资源调度. 新方法减少了能源消耗和处理时间,在分布式环境中提高了服务质量 (QoS).
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:
- 边缘计算需要实时数据处理和高效的资源管理.
- 为能源消耗和延迟优化资源调度是分布式环境中的一个关键挑战.
研究的目的:
- 为边缘计算资源调度引入一种综合方法,将蜂盆模型 (CPM) 和粒子集群优化 (PSO) 结合起来.
- 为了减轻服务质量 (QoS) 要求,特别是能源消耗和端到端延迟.
主要方法:
- 利用蜂盆模型 (CPM) 来捕获本地资源交互和依赖关系.
- 采用粒子优化 (PSO) 作为调度任务的全局优化器.
- 集成CPM的本地优化与PSO的全球寻找混合方法.
主要成果:
- 拟议的混合CPM-PSO模型显示,与传统方法相比,能耗和处理时间降低了 (循环,随机卸载,基于值).
- 在资源有限的设置中实现了更高的可扩展性,高效地处理许多任务和边缘节点,具有高 QoS.
- 该模型为资源调度提供高性能,实时的解决方案.
结论:
- 混合CPM-PSO模型为边缘计算中的资源调度提供了有效的解决方案.
- 这种方法在未来边缘计算开发中显示出对能源受限和时间敏感应用的巨大潜力.
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