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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Energy Budgets00:51

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Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
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Maximum Power Flow and Line Loadability01:23

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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When an object is acted upon by a variable force, the amount of work done and the change in energy of the object can be more complex to calculate compared to when a constant force is applied. Work is the product of force and displacement, while energy is the capacity of a system to do work. When a constant force is applied to an object, the work done can be calculated as the product of the force and the distance moved in the direction of the force. However, when a variable force is applied, the...
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相关实验视频

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Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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云计算中的动态多目标任务调度,使用强化学习来优化能源和成本.

Xiaomo Yu1,2, Jie Mi2, Ling Tang3

  • 1Guangxi Colleges and Universities Key Laboratory of Intelligent Logistics Technology, Nanning Normal University, Nanning, 530001, Guangxi, China.

Scientific reports
|November 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种强化学习驱动的多目标任务调度 (RL-MOTS) 框架,使用深度Q网络 (DQN) 进行高效的云任务分配. 在确保服务质量 (QoS) 的同时,RL-MOTS显著降低了能源消耗和成本.

关键词:
云计算是一种云计算.云端计算是云边缘计算.能源效率 能源效率是指能源的使用效率.多目标优化多目标优化服务质量服务质量.强化学习是一种强化学习.任务安排 任务安排

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科学领域:

  • 云计算 云计算 云计算
  • 人工智能的人工智能
  • 优化优化 优化优化

背景情况:

  • 有效的任务调度对于云计算性能,能源效率和成本管理至关重要.
  • 云环境中的动态工作负载需要适应性调度解决方案.
  • 在云资源管理中,平衡性能,能源和成本仍然是关键的挑战.

研究的目的:

  • 引入一个新的强化学习驱动的多目标任务安排 (RL-MOTS) 框架.
  • 通过使用深度Q网络 (DQN) 在云环境中实现动态任务分配.
  • 同时最大限度地降低能源消耗,降低运营成本,并确保服务质量 (QoS).

主要方法:

  • 加强学习驱动的多目标任务安排 (RL-MOTS) 框架的开发.
  • 使用深度Q网络 (DQN) 进行动态的任务对虚拟机的分配.
  • 实施适应性奖励函数,考虑实时资源利用,截止日期和能源指标.

主要成果:

  • RL-MOTS实现了高达27%的能源消耗降低.
  • 与现有方法相比,成本效益提高了18%.
  • 在不同的工作负载条件下,成功满足了严格的任务截止日期限制.

结论:

  • RL-MOTS框架为云计算中的多目标任务调度提供了有效的解决方案.
  • 该框架在异构的云环境中展示了强大的性能和适应性.
  • RL-MOTS为下一代分布式计算提供了一个前性的解决方案,包括混合云端架构.