Jove
Visualize
Contact Us

Related Concept Videos

Production Efficiency01:01

Production Efficiency

Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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:
Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
Parallel Processing01:20

Parallel Processing

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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

Modified grey wolf optimization for energy-efficient internet of things task scheduling in fog computing.

Scientific reports·2025
Same author

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects.

PeerJ. Computer science·2024
Same author

Secure Computing for Fog-Enabled Industrial IoT.

Sensors (Basel, Switzerland)·2024
Same author

EOTE-FSC: An efficient offloaded task execution for fog enabled smart cities.

PloS one·2024
Same author

Efficient content caching for 5G assisted vehicular networks.

Scientific reports·2024
Same author

Identifying drivers of COVID-19 vaccine sentiments for effective vaccination policy.

Heliyon·2023
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles
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 Experiment Video

Updated: Jul 1, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.1K

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments.

Deafallah Alsadie1, Musleh Alsulami2

  • 1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, 21961, Saudi Arabia. dbsadie@uqu.edu.sa.

Scientific Reports
|October 21, 2024
PubMed
Summary
This summary is machine-generated.

The Modified Firefly Optimization Algorithm (ModFOA) efficiently schedules scientific workflows in hybrid cloud-edge environments, outperforming other methods in reducing completion times and optimizing resource use.

Keywords:
Cloud computingFirefly Optimization AlgorithmResource utilizationScheduling algorithmsScientific workflows

More Related Videos

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

490
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

985

Related Experiment Videos

Last Updated: Jul 1, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.1K
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

490
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

985

Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Scientific workflows require efficient scheduling in hybrid cloud-edge environments for optimal resource utilization and minimal completion times.
  • Traditional scheduling algorithms struggle with the complexities of scientific workflows and the unique demands of hybrid cloud-edge architectures.
  • Existing methods like Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) show promise but may not fully address these challenges.

Purpose of the Study:

  • To evaluate and compare the performance of various scheduling algorithms for scientific workflows in hybrid cloud-edge environments.
  • To introduce and assess the Modified Firefly Optimization Algorithm (ModFOA) as a solution for optimizing workflow scheduling in these integrated systems.
  • To analyze key performance metrics including makespan, resource utilization, and energy consumption.

Main Methods:

  • Comparative analysis of scheduling algorithms: Modified Firefly Optimization Algorithm (ModFOA), Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO).
  • Evaluation of performance metrics: makespan, resource utilization, and energy consumption.
  • Testing across both cloud and edge computing configurations within a hybrid environment.

Main Results:

  • ModFOA demonstrated superior performance in reducing makespan and overall completion times compared to ACO, GA, and PSO.
  • The algorithm maintained competitive levels of resource utilization and energy efficiency.
  • ModFOA effectively integrates cloud and edge computing resources for enhanced workflow scheduling.

Conclusions:

  • The Modified Firefly Optimization Algorithm (ModFOA) offers an effective solution for scheduling scientific workflows in hybrid cloud-edge environments.
  • ModFOA enhances workflow efficiency and resource management by effectively integrating cloud and edge resources.
  • Future research should focus on parameter refinement and practical validation of ModFOA in real-world hybrid cloud-edge scenarios.