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Related Concept Videos

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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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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Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

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The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments.
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Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

800
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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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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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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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Load-Balancing of Kubernetes-Based Edge Computing Infrastructure Using Resource Adaptive Proxy.

Quang-Minh Nguyen1, Linh-An Phan1, Taehong Kim1

  • 1School of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Korea.

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Summary
This summary is machine-generated.

Kubernetes (K8s) load balancing causes delays in edge computing. A new Resource Adaptive Proxy (RAP) improves throughput and reduces latency by intelligently routing requests based on real-time resource availability.

Keywords:
Kubernetescontainerizationedge computingkube-proxyload-balancingmetric-servermicroservice

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Area of Science:

  • Edge Computing
  • Container Orchestration
  • Network Load Balancing

Background:

  • Kubernetes (K8s) is a key container orchestration tool for edge computing.
  • K8s default load balancing (kube-proxy) can cause significant delays due to geographically dispersed nodes and worker overload.
  • Existing mechanisms lack adaptive resource awareness for optimal request distribution.

Purpose of the Study:

  • To propose an enhanced load balancer, Resource Adaptive Proxy (RAP), for Kubernetes in edge computing environments.
  • To address the limitations of default K8s load balancing, specifically request latency and throughput.
  • To improve the efficiency and performance of containerized applications at the edge.

Main Methods:

  • Developed Resource Adaptive Proxy (RAP) to monitor pod resource status and network conditions.
  • Implemented intelligent load-balancing decisions prioritizing local handling and adaptive forwarding.
  • Compared RAP's performance against K8s default load balancing through experimental evaluation.

Main Results:

  • RAP significantly improved throughput compared to the default K8s load balancing mechanism.
  • RAP demonstrated a substantial reduction in request latency, especially in edge scenarios.
  • Experimental results validated RAP's effectiveness in dynamic edge environments.

Conclusions:

  • Resource Adaptive Proxy (RAP) offers a superior load-balancing solution for Kubernetes in edge computing.
  • RAP's adaptive resource monitoring and intelligent routing effectively mitigate latency and enhance performance.
  • The proposed method provides a scalable and efficient approach for edge infrastructure management.