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相关概念视频

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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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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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

705
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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PI Controller: Design01:24

PI Controller: Design

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Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
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PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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Controller Configurations01:22

Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
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相关实验视频

Updated: Jan 7, 2026

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MC-LBTO:安全和弹性状态感知多控制器框架,具有适应负载平衡,用于SD-IoT性能优化.

Ashraf Alyanbaawi1, Ameer El-Sayed2, Nihal Salah3

  • 1College of Computer Science and Engineering, Taibah University, Yanbu, 966144, Saudi Arabia.

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

MC-LBTO为软件定义物联网 (SD-IoT) 网络提供了一个模块化多控制器框架,通过智能流量管理和自适应控制提高了可扩展性和效率. 该系统优化负载平衡和网络弹性,适用于苛刻的物联网环境.

关键词:
适应式负载平衡系统这就是为什么物联网是物联网物联网.多控制器弹性 复杂性在SDN中,SDN是SDN.国家意识监测监测值得信赖的故障重置程序

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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
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

  • 传统的软件定义网络 (SDN) 难以应对大型,异构的物联网 (IoT) 系统的动态和数据密集型需求.
  • 传统SDN架构中的集中控制和静态转发限制了物联网网络的可扩展性和实时适应性.

研究的目的:

  • 提出MC-LBTO,一个模块化多控制器框架,旨在提高软件定义物联网 (SD-IoT) 网络的可扩展性,效率和弹性.
  • 整合可编程数据平面智能与分布式控制器之间的安全,自适应协调,以优化负载均衡和网络性能.

主要方法:

  • 开发了MC-LBTO,一个包含三个合作模块的框架:PDSM (支持P4的动态状态监控),PALB (基于P4的自适应负载平衡器) 和STAM (安全可信的自适应多重控制).
  • 利用P4编程用于开关内流量监控,自适应负载平衡和安全的控制器间通信.
  • 进行实验性评估,以评估单个模块和综合框架的性能.

主要成果:

  • PDSM模块将控制器CPU利用率降低了35.7%,并将流量状态检测精度提高到98.3%.
  • PALB模块实现了 36% 的请求延迟减少和 25% 的吞吐量增加,负载分布差异较低 (5.5%).
  • STAM模块增强了控制平面的稳定性,将平均恢复时间 (MTTR) 减少到75毫秒,并将数据包损失降低了70%.

结论:

  • MC-LBTO提供了一个可扩展,安全和自适应的SD-IoT架构.
  • 该框架保持低的开销,平衡的资源利用和快速恢复,确保可靠和高性能物联网网络.
  • 对于物联网的快速扩张所带来的挑战,MC-LBTO提供了一个技术基础的解决方案.