Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

914
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...
914
Parallel Processing01:20

Parallel Processing

441
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...
441
Distributed Loads01:19

Distributed Loads

784
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...
784
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

924
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
924
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

17.0K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
17.0K
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

1.4K
The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
To apply the principle of virtual work,...
1.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Novel PCB Surface Defect Detection Method Based on the GBE-YOLOv8 Model.

Micromachines·2026
Same author

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

Sensors (Basel, Switzerland)·2024
Same author

Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof.

Foods (Basel, Switzerland)·2023
Same author

Domain Adaptation Multitask Optimization.

IEEE transactions on cybernetics·2022
Same author

LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification.

IEEE transactions on cybernetics·2022
Same author

Single Diode Solar Cells-Improved Model and Exact Current-Voltage Analytical Solution Based on Lambert's W Function.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Nov 19, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

942

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm.

Shichao Chen1,2, Qijie Li3, Mengchu Zhou1,4,5

  • 1Faculty of Information Tecnology, Macau University of Science and Technology, Macau 999078, China.

Sensors (Basel, Switzerland)
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

Edge computing enables task offloading but can cause overload. This study reviews collaborative scheduling mechanisms for edge servers, cloud centers, and devices to optimize task execution and resource utilization.

Keywords:
collaborative schedulingedge computinginternet of thingslimited resourcesoptimizationtask offloading

More Related Videos

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.9K
Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
07:40

Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices

Published on: March 10, 2023

2.5K

Related Experiment Videos

Last Updated: Nov 19, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

942
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.9K
Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
07:40

Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices

Published on: March 10, 2023

2.5K

Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Edge computing allows devices to offload tasks to edge servers for efficient processing.
  • Overloading edge servers with all tasks leads to high latency and energy consumption, while underutilizing cloud and idle edge resources.
  • Effective resource management is crucial for optimizing edge computing performance.

Purpose of the Study:

  • To analyze and summarize edge computing scenarios and task classifications.
  • To formulate the computation offloading optimization problem in edge computing systems.
  • To review collaborative scheduling methods for edge, cloud, and device resources.

Main Methods:

  • Analysis and summarization of edge computing paradigms and task types.
  • Mathematical formulation of the computation offloading optimization problem.
  • Review of existing collaborative scheduling strategies for heterogeneous computing environments.

Main Results:

  • Identification of challenges in edge computing, including server overload and resource underutilization.
  • A structured approach to defining and solving the computation offloading problem.
  • Categorization of collaborative scheduling methods based on task characteristics and system status.

Conclusions:

  • Collaborative scheduling across edge servers, cloud centers, and edge devices is essential for efficient task execution.
  • Understanding task characteristics and system status is key to optimizing offloading decisions.
  • Future research should focus on advanced collaborative scheduling for complex edge computing environments.