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Related Experiment Video

Updated: May 12, 2025

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A refined Greylag Goose optimization method for effective IoT service allocation in edge computing systems.

Hossein Najafi Khosrowshahi1, Hadi S Aghdasi2, Pedram Salehpour1

  • 1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

Scientific Reports
|May 5, 2025
PubMed
Summary
This summary is machine-generated.

The Modified Greylag Goose Optimization (MGGO) algorithm enhances edge computing service placement. MGGO improves energy consumption, latency, throughput, and load balancing in dynamic Internet of Things environments.

Keywords:
Edge computing optimizationGreylag Goose optimization (GGO)Multi-Access edge computing (MEC)Service placementSwarm intelligence

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Computing

Background:

  • The proliferation of the Internet of Things (IoT) necessitates efficient service placement in edge computing.
  • Dynamic workloads and heterogeneous resources present significant challenges to existing service placement strategies.
  • Current swarm intelligence algorithms like QPSO-SP and WOA-FSP face difficulties in balancing exploration and exploitation.

Purpose of the Study:

  • To introduce a novel optimization algorithm, Modified Greylag Goose Optimization (MGGO), for edge service placement.
  • To address the limitations of existing algorithms in handling dynamic and heterogeneous edge environments.
  • To improve key performance metrics including energy consumption, latency, throughput, and load balancing.

Main Methods:

  • Development of the Modified Greylag Goose Optimization (MGGO) algorithm.
  • Incorporation of adaptive mechanisms: dynamic population partitioning, stagnation detection, and learning-based control.
  • Experimental evaluation using synthetic service placement workloads.

Main Results:

  • MGGO demonstrated a 12-15% improvement over GGO, QPSO-SP, BOA, and WOA-FSP.
  • The algorithm showed enhanced performance across all evaluated metrics: energy consumption, latency, throughput, and load balancing.
  • MGGO effectively optimized service placement in dynamic edge computing scenarios.

Conclusions:

  • The proposed MGGO algorithm offers a significant advancement for edge service placement.
  • MGGO's adaptive mechanisms contribute to its superior performance in dynamic environments.
  • This research highlights MGGO's potential to enhance the efficiency and effectiveness of edge computing systems.