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相关概念视频

Distributed Loads01:19

Distributed Loads

1.0K
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...
1.0K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

557
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...
557
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
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...
1.2K
Multimachine Stability01:25

Multimachine Stability

602
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
602
Parallel Processing01:20

Parallel Processing

851
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
851
Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.4K

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相关实验视频

Updated: Mar 15, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

4.2K

针对多集群物联网环境的分布式事件驱动无服务器平台.

Hyungwoo Ju1, Jangwon Seo1, Younghan Kim1

  • 1School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了智能城市和物联网的新功能即服务 (FaaS) 架构,改进了多个集群的实时事件处理. 该平台提高了负载分配和工作流的成功率,即使在重的工作负载下.

关键词:
物联网系统物联网系统物联网系统.事件驱动系统是一个事件驱动系统.多活动多活动.实时处理实时处理.无服务器架构 无服务器架构

相关实验视频

Last Updated: Mar 15, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

4.2K

科学领域:

  • 计算机科学 计算机科学
  • 分布式系统 分布式系统
  • 云计算 云计算 云计算 云计算

背景情况:

  • 智能城市和物联网环境从多种传感器生成了庞大的实时异质事件流.
  • 现有的Kubernetes托管的无服务器功能即服务 (FaaS) 部署往往缺乏动态的多集群配置和用户控制.
  • 高效处理多源事件流需要可扩展和响应的计算架构.

研究的目的:

  • 提出一个通用的事件驱动的FaaS架构,用于在多集群环境中高效的多事件流处理.
  • 解决单个集群 FaaS 部署在动态配置和资源管理方面的局限性.
  • 为了增强物联网和智能城市应用程序的实时事件处理能力.

主要方法:

  • 在基于Kubernetes的测试平台上实现了通用事件驱动的FaaS架构.
  • 集成了一个多集群编排器,一个事件处理引擎,一个工作流执行层和一个无服务器平台.
  • 评估了平台使用智能城市启发的场景,工作量增加.

主要成果:

  • 拟议的平台表现出更好的负载分配特性.
  • 与单个集群基线相比,在工作负载增加的情况下保持了更高的工作流成功率.
  • 架构有效地处理跨多集群环境的多事件流.

结论:

  • 开发的 FaaS 架构为物联网和智能城市应用中的实时事件处理提供了一个可扩展的解决方案.
  • 多集群方法提高了事件驱动系统的响应能力和资源利用率.
  • 这项研究为复杂,数据密集型环境中的先进无服务器平台提供了基础.