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为了节能物联网,修改了灰狼优化,在雾中安排任务计算计算.

Deafallah Alsadie1, Musleh Alsulami2

  • 1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, 21961, Makkah, Saudi Arabia. dbsadie@uqu.edu.sa.

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概括

使用修改后的灰狼优化 (TS-GWO) 的新任务调度算法优化了物联网 (IoT) 在雾云系统中的任务调度. TS-GWO显著减少了任务完成时间和能源消耗.

关键词:
雾云计算是一种雾云计算.灰色狼优化器 (GWO) 的使用物联网 (IoT) 的物联网 (IoT) 的物联网.这种算法是Metaheuristic算法.任务安排 任务安排

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科学领域:

  • 计算机科学 计算机科学
  • 分布式计算 (Distributed Computing) 是一种分布式计算.
  • 人工智能的人工智能

背景情况:

  • 雾云计算对于物联网 (IoT) 应用程序至关重要,要求高效的任务调度.
  • 现有的方法在动态雾云环境中平衡产量范围和能源效率方面面临挑战.
  • 雾云系统的资源限制需要先进的调度解决方案.

研究的目的:

  • 为物联网请求引入基于修改的灰狼优化 (TS-GWO) 的新型任务调度算法.
  • 加强在雾云系统中寻找最佳调度解决方案的勘探和开发能力.
  • 在动态和资源有限的环境中解决当前任务安排方法的局限性.

主要方法:

  • 与创新运营商一起开发一个修改的灰狼优化 (TS-GWO) 算法.
  • 专门为雾云系统中的物联网 (IoT) 任务调度量身定制TS-GWO算法.
  • 使用合成数据集和现实工作负载进行评估 (NASA Ames iPSC,HPC2N).

主要成果:

  • 与已建立的元启发方法相比,TS-GWO表现出优越的性能.
  • 在madespan (任务完成时间) 中实现了高达46.15%的改进.
  • 实现了高达28.57%的能源消耗降低.

结论:

  • TS-GWO有效地解决了在雾云环境中的任务安排挑战.
  • 该算法显示了优化物联网应用程序的巨大潜力.
  • 在分布式系统中,TS-GWO可以应用于更广泛的优化任务.