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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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通过 MUMS 框架优化移动边缘计算中的多个用户和服务器的低功耗任务调度.

Guangxu Li1,2,3, Junke Li1,2,3,4

  • 1College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

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概括

本研究介绍了物联网 (IoT) 中移动边缘计算 (MEC) 的多用户多服务器 (MUMS) 框架. 它使用L1_PSO算法显著降低了能源消耗.

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优化能源消耗 优化能源消耗低功耗任务调度 任务调度移动边缘计算移动边缘计算粒子群集优化优化 粒子群集优化电力消耗建模的电力消耗建模

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 面临着重大的能源消耗挑战.
  • 移动边缘计算 (MEC) 对于解决物联网能源需求至关重要.
  • 在MEC中有效的任务安排对于节能至关重要.

研究的目的:

  • 提出一个多用户多服务器 (MUMS) 调度框架,以减少MEC系统的能源消耗.
  • 为MUMS开发一个能源消耗优化模型.
  • 在物联网环境中提高任务调度效率.

主要方法:

  • 定义了四个基本模型:多用户多服务器系统的通信,卸载,能源和延迟.
  • 集成模型用于构建MUMS的能源消耗优化模型.
  • 利用L1_PSO算法,一种增强的粒子群集优化,来解决优化问题.

主要成果:

  • 与典型的调度算法相比,MUMS框架证明了合理性和可行性.
  • L1_PSO算法实现了4.6%的能源消耗降低,而不是随机分配.
  • 与传统的粒子群优化相比,L1_PSO算法减少了2.3%的能源消耗.

结论:

  • 拟议的MUMS框架有效地减少了物联网的MEC的能源消耗.
  • L1_PSO是一个高级优化算法,用于MEC任务调度.
  • 这项研究为节能物联网部署提供了可行的解决方案.