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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Related Experiment Videos

A hybrid evolutionary framework for efficient IoT task scheduling in fog computing.

Lianhe Cui1

  • 1School of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, Heilongjiang, China. 18003622999@189.cn.

Scientific Reports
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid Gorilla Troops Optimizer (GTO) and resource-aware strategy for optimizing task scheduling and virtual machine (VM) placement in fog computing for Internet of Things (IoT) applications, enhancing energy efficiency and task completion.

Keywords:
Energy efficiencyFog computingGorilla troops optimizerInternet of ThingsTask schedulingVirtual machine placement

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Computing

Background:

  • Fog computing is crucial for Internet of Things (IoT) applications due to its proximity to data sources, enabling lower latency and reduced bandwidth usage.
  • Efficient task scheduling and virtual machine (VM) placement are critical challenges in fog computing, impacting performance, energy consumption, and reliability.
  • Existing algorithms often struggle to simultaneously optimize multiple objectives like latency, power consumption, and load balancing for dynamic IoT workloads.

Purpose of the Study:

  • To propose a novel hybrid approach for optimizing task scheduling and VM placement in fog computing environments.
  • To enhance energy efficiency and task completion rates for Internet of Things (IoT) applications.
  • To address the multi-objective optimization challenges in fog-based IoT systems.

Main Methods:

  • A hybrid approach combining the Gorilla Troops Optimizer (GTO) metaheuristic with a resource-aware VM placement strategy.
  • Implementation of a dynamic exploration-exploitation strategy and an innovative fitness function for efficient task mapping.
  • Simulation using iFogSim2 to evaluate the performance against existing algorithms.

Main Results:

  • The proposed hybrid method achieved significant improvements in task scheduling and VM placement compared to existing algorithms.
  • Demonstrated an 18% improvement over Ant Colony Optimization (ACO), 15% over Improved Multi-Objective Differential Evolution (IMODE), and 13% over Genetic and Simulated Annealing (GASA).
  • Effectively minimized task failure and suspension periods while optimizing latency, power consumption, and load balancing.

Conclusions:

  • The hybrid GTO-based approach offers a scalable and effective solution for optimizing fog computing environments for IoT.
  • The method significantly enhances service quality by optimizing resource and energy consumption in dynamic, latency-sensitive applications.
  • This work presents a promising strategy for real-world fog computing deployments supporting diverse IoT applications.