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对物联网应用程序的按需集中资源配置:支持人工智能的基准.

Ran Zhang1, Lei Liu1, Mianxiong Dong2

  • 1School of Software, Shandong University, Jinan 250101, China.

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概括
此摘要是机器生成的。

本研究介绍了一种基于人工智能的基准,用于通用处理器共享 (GPS) 性能预测. 它在各种交通条件下评估传统和人工智能方法,解决当前对物联网 (IoT) 服务研究的局限性.

关键词:
这就是为什么物联网物联网物联网.人工智能的人工智能是人工智能.一个基准的基准指标.业绩预测 业绩预测资源分配的资源分配.

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 像物联网这样的新兴技术产生了大量数据,增加了对高效网络资源配置的需求.
  • 一般化处理器共享 (GPS) 对于在网络中管理差异化服务和按需资源分配至关重要.
  • 现有的GPS性能预测方法往往缺乏现实世界的适用性和标准化的基准.

研究的目的:

  • 解决现有的通用处理器共享 (GPS) 性能预测方法的局限性.
  • 引入一个支持人工智能的基准来评估GPS性能预测技术.
  • 在各种交通条件下提供传统和基于人工智能的方法的全面比较.

主要方法:

  • 开发一个支持人工智能的GPS性能基准.
  • 实施和评估传统的近似分析方法.
  • 传统机器学习和基于深度学习的方法的应用和评估.
  • 进行全面的以交通为导向的实验,使用各种交通流和设置.

主要成果:

  • 对GPS性能预测的传统分析,机器学习和深度学习方法的实验分析.
  • 在不同的交通条件下评估方法和功能级别的性能.
  • 识别用于改进未来GPS性能预测的见解.

结论:

  • 本文介绍的支持人工智能的基准标准有助于公平地比较GPS性能预测方法.
  • 该研究强调了对现实世界物联网场景的适应性和全面方法的需求.
  • 结果为未来的网络资源分配和性能预测研究提供了宝贵的指导.