智能任务调度和实时资源优化,用于下一代网络中的边缘云连续性
Awad Bin Naeem1, Biswaranjan Senapati2, Jawad Rasheed3,4,5,6
1Department of Computer Science, National College of Business Administration and Economics, Multan-Sub Campus, Multan, 60000, Pakistan.
Scientific reports
|November 22, 2025
概括
本研究介绍了一个人工智能任务调度器,它结合了不公平的半贪 (USG),最早的截止日期 (EDF) 和增强的截止日期零宽松 (EDZL) 算法. 混合方法显著减少了云边缘环境中的实时任务的响应时间和最后期限错误.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 云端基础设施需要对6G网络进行先进的资源管理,这些网络要求低延迟和高可靠性.
- 在这些环境中实时任务调度面临诸如资源效率低下,虚拟机问题和截止日期违规等挑战.
研究的目的:
- 开发一个人工智能驱动的任务调度系统,以便在云端环境中高效地管理资源.
- 通过改善响应时间和减少错过截止日期来应对实时任务调度的挑战.
主要方法:
- 开发了一个人工智能任务调度器,集成了不公平的半贪 (USG),最早的最后期限 (EDF) 和增强的最后期限零宽松 (EDZL) 算法.
- 采用强化学习自适应逻辑和动态资源表来根据负载和任务关键性选择最佳调度器.
- 在各种云边缘场景中使用超过10,000个软实时任务集来评估框架.
主要成果:
- 与独立的EDF和EDZL相比,混合调度方法的平均响应时间减少了高达26.3%,截止日期的例外情况减少了41.7%.
- 不公平的半贪 (USG) 组件在和边缘条件下显示了98.6%的任务刺激性.
- 该系统有效地管理了工作负载,并在苛刻的云端设置中提高了任务完成率.
结论:
- 拟议的AI驱动混合任务调度系统为实时应用程序的性能提供了显著的改进.
- 该架构非常适合于对延迟敏感和可靠的应用程序,如自主系统,远程医疗保健和沉浸式媒体.
- 这种方法可以扩展到未来的人工智能原生6G网络,增强其功能.
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