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Related Concept Videos

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...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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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 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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Multimachine Stability01:25

Multimachine Stability

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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:
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Parallel Processing01:20

Parallel Processing

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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...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Related Experiment Video

Updated: Mar 15, 2026

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

Published on: February 3, 2021

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Distributed Event-Driven Serverless Platform for Multicluster IoT Environments.

Hyungwoo Ju1, Jangwon Seo1, Younghan Kim1

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

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Function-as-a-Service (FaaS) architecture for smart cities and IoT, improving real-time event processing across multiple clusters. The platform enhances load distribution and workflow success rates, even under heavy workloads.

Keywords:
IoT systemsevent-driven systemmulti-eventreal-time processingserverless architecture

Related Experiment Videos

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

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Cloud Computing

Background:

  • Smart city and IoT environments generate vast, real-time heterogeneous event streams from diverse sensors.
  • Existing Kubernetes-hosted serverless Function-as-a-Service (FaaS) deployments often lack dynamic multicluster placement and user control.
  • Processing multi-source event streams efficiently requires scalable and responsive computing architectures.

Purpose of the Study:

  • To propose a generalized event-driven FaaS architecture for efficient multi-event stream processing across multicluster environments.
  • To address limitations in single-cluster FaaS deployments regarding dynamic placement and resource management.
  • To enhance real-time event processing capabilities for IoT and smart city applications.

Main Methods:

  • Implemented a generalized event-driven FaaS architecture on a Kubernetes-based testbed.
  • Integrated a multicluster orchestrator, an event-processing engine, a workflow execution layer, and a serverless platform.
  • Evaluated the platform using a smart city-inspired scenario with increasing workloads.

Main Results:

  • The proposed platform demonstrated improved load distribution characteristics.
  • Higher workflow success rates were maintained under increasing workloads compared to a single-cluster baseline.
  • The architecture efficiently processed multi-event streams across multicluster environments.

Conclusions:

  • The developed FaaS architecture offers a scalable solution for real-time event processing in IoT and smart city applications.
  • The multicluster approach enhances responsiveness and resource utilization for event-driven systems.
  • This research provides a foundation for advanced serverless platforms in complex, data-intensive environments.