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相关概念视频

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Distributed Loads: Problem Solving01:21

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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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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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深度强化学习用于联合云环境中的工作负载预测.

Zaakki Ahamed1, Maher Khemakhem1, Fathy Eassa1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

联合云工作负载预测与深度Q学习 (FEDQWP) 优化了云服务提供商的资源配置. 这种新的方法提高了CPU利用率,降低了能源消耗,并在联合云环境中最大限度地减少了服务级别协议违规行为.

关键词:
深度Q学习 (Deep Q Learning) 是一种深度Q学习.深度强化学习 (deep reinforcement learning) 是一种深度强化学习的方法.联合云计算 联合云计算机器学习 机器学习虚拟机的放置位置能源效率是指能效的能源效率.工作负载预测工作负载预测

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 云计算 云计算 云计算 云计算

背景情况:

  • 联合云计算 (FCC) 提供了可扩展性,但在能源效率和服务水平协议 (SLA) 遵守方面面临挑战.
  • 现有的研究往往优先考虑虚拟机 (VM) 放置,而不是整体性能优化.

研究的目的:

  • 引入一种新的解决方案,即使用深度Q学习 (FEDQWP) 进行联合云工作负载预测,以优化FCC环境.
  • 同时解决VM安置,能源效率和SLA保护问题.

主要方法:

  • 使用深度Q学习 (DQL) 开发FEDQWP模型,用于工作负载预测和资源分配.
  • 使用现实工作负载进行广泛的评估,将FEDQWP与现有解决方案进行比较.

主要成果:

  • 在CPU利用率 (中位数为29.02%),迁移时间 (平均时间为29.02%),移动时间 (平均时间为29.02%),移动时间 (平均时间为29.02%),移动时间 (平均时间为29.02%),移动时间 (平均时间为29.02%),移动时间 (平均时间为29.02%),移动时间 (平均时间为29.02%),移动时间 (平均时间为29.05%),移动时间 (平均时间为29.05%),移动时间 (平均时间为29.05%). 0.31个单位) 和完成任务 (平均 699个任务). 这些任务.
  • DQL模型实现了最低的能源消耗 (平均值). 1.85千瓦时) 和最小的SLA违规 (平均值) 0.03个违规行为).

结论:

  • 在FCC设置中的关键性能指标中,FEDQWP模型显著优于现有的算法.
  • 在联邦云中,FEDQWP提供了一种全面的方法来优化资源配置,能源效率和SLA遵守.